National Library of Energy BETA

Sample records for random number times

  1. High-speed quantum-random number generation by continuous measurement of arrival time of photons

    SciTech Connect

    Yan, Qiurong; Zhao, Baosheng; Hua, Zhang; Liao, Qinghong; Yang, Hao

    2015-07-15

    We demonstrate a novel high speed and multi-bit optical quantum random number generator by continuously measuring arrival time of photons with a common starting point. To obtain the unbiased and post-processing free random bits, the measured photon arrival time is converted into the sum of integral multiple of a fixed period and a phase time. Theoretical and experimental results show that the phase time is an independent and uniform random variable. A random bit extraction method by encoding the phase time is proposed. An experimental setup has been built and the unbiased random bit generation rate could reach 128 Mb/s, with random bit generation efficiency of 8 bits per detected photon. The random numbers passed all tests in the statistical test suite.

  2. Practical and fast quantum random number generation based on photon arrival time relative to external reference

    SciTech Connect

    Nie, You-Qi; Zhang, Jun Pan, Jian-Wei; Zhang, Hong-Fei; Wang, Jian; Zhang, Zhen; Ma, Xiongfeng

    2014-02-03

    We present a practical high-speed quantum random number generator, where the timing of single-photon detection relative to an external time reference is measured as the raw data. The bias of the raw data can be substantially reduced compared with the previous realizations. The raw random bit rate of our generator can reach 109 Mbps. We develop a model for the generator and evaluate the min-entropy of the raw data. Toeplitz matrix hashing is applied for randomness extraction, after which the final random bits are able to pass the standard randomness tests.

  3. Quantum random number generation

    DOE PAGES [OSTI]

    Ma, Xiongfeng; Yuan, Xiao; Cao, Zhu; Zhang, Zhen; Qi, Bing

    2016-06-28

    Here, quantum physics can be exploited to generate true random numbers, which play important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum system reveals the inherent nature of quantumness -- coherence, an important feature that differentiates quantum mechanics from classical physics. The generation of genuine randomness is generally considered impossible with only classical means. Based on the degree of trustworthiness on devices, quantum random number generators (QRNGs) can be grouped into three categories. The first category, practical QRNG, is built on fully trusted and calibrated devices and typically can generate randomness at amore » high speed by properly modeling the devices. The second category is self-testing QRNG, where verifiable randomness can be generated without trusting the actual implementation. The third category, semi-self-testing QRNG, is an intermediate category which provides a tradeoff between the trustworthiness on the device and the random number generation speed.« less

  4. Quantum random number generator

    DOEpatents

    Pooser, Raphael C.

    2016-05-10

    A quantum random number generator (QRNG) and a photon generator for a QRNG are provided. The photon generator may be operated in a spontaneous mode below a lasing threshold to emit photons. Photons emitted from the photon generator may have at least one random characteristic, which may be monitored by the QRNG to generate a random number. In one embodiment, the photon generator may include a photon emitter and an amplifier coupled to the photon emitter. The amplifier may enable the photon generator to be used in the QRNG without introducing significant bias in the random number and may enable multiplexing of multiple random numbers. The amplifier may also desensitize the photon generator to fluctuations in power supplied thereto while operating in the spontaneous mode. In one embodiment, the photon emitter and amplifier may be a tapered diode amplifier.

  5. Self-correcting random number generator

    DOEpatents

    Humble, Travis S.; Pooser, Raphael C.

    2016-09-06

    A system and method for generating random numbers. The system may include a random number generator (RNG), such as a quantum random number generator (QRNG) configured to self-correct or adapt in order to substantially achieve randomness from the output of the RNG. By adapting, the RNG may generate a random number that may be considered random regardless of whether the random number itself is tested as such. As an example, the RNG may include components to monitor one or more characteristics of the RNG during operation, and may use the monitored characteristics as a basis for adapting, or self-correcting, to provide a random number according to one or more performance criteria.

  6. Truly Random Number Generator Promises Stronger Encryption Across...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    quantum mechanics. They generate truly random numbers by harnessing the entropy (randomness or disorder) of nature, which is much more random than any of the sources computing ...

  7. Truly Random Number Generator Promises Stronger Encryption Across All

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Devices, Cloud Truly Random Number Generator Promises Stronger Encryption Across All Devices, Cloud Truly Random Number Generator Promises Stronger Encryption Across All Devices, Cloud Whitewood Encryption Systems, launched in summer 2015, introduces NetRandom, providing truly random quantum encryption. March 4, 2016 Random Number Generator Whitewood Encryption Systems, launched in summer 2015, introduces NetRandom, providing truly random quantum encryption. They were awarded a third patent

  8. Semi-device-independent random-number expansion without entanglement

    SciTech Connect

    Li Hongwei; Yin Zhenqiang; Wu Yuchun; Zou Xubo; Wang Shuang; Chen Wei; Guo Guangcan; Han Zhengfu

    2011-09-15

    By testing the classical correlation violation between two systems, true random numbers can be generated and certified without applying classical statistical method. In this work, we propose a true random-number expansion protocol without entanglement, where the randomness can be guaranteed only by the two-dimensional quantum witness violation. Furthermore, we only assume that the dimensionality of the system used in the protocol has a tight bound, and the whole protocol can be regarded as a semi-device-independent black-box scenario. Compared with the device-independent random-number expansion protocol based on entanglement, our protocol is much easier to implement and test.

  9. Random Number Generation for Petascale Quantum Monte Carlo

    SciTech Connect

    Ashok Srinivasan

    2010-03-16

    The quality of random number generators can affect the results of Monte Carlo computations, especially when a large number of random numbers are consumed. Furthermore, correlations present between different random number streams in a parallel computation can further affect the results. The SPRNG software, which the author had developed earlier, has pseudo-random number generators (PRNGs) capable of producing large numbers of streams with large periods. However, they had been empirically tested on only thousand streams earlier. In the work summarized here, we tested the SPRNG generators with over a hundred thousand streams, involving over 10^14 random numbers per test, on some tests. We also tested the popular Mersenne Twister. We believe that these are the largest tests of PRNGs, both in terms of the numbers of streams tested and the number of random numbers tested. We observed defects in some of these generators, including the Mersenne Twister, while a few generators appeared to perform well. We also corrected an error in the implementation of one of the SPRNG generators.

  10. Quantum Statistical Testing of a Quantum Random Number Generator

    SciTech Connect

    Humble, Travis S

    2014-01-01

    The unobservable elements in a quantum technology, e.g., the quantum state, complicate system verification against promised behavior. Using model-based system engineering, we present methods for verifying the opera- tion of a prototypical quantum random number generator. We begin with the algorithmic design of the QRNG followed by the synthesis of its physical design requirements. We next discuss how quantum statistical testing can be used to verify device behavior as well as detect device bias. We conclude by highlighting how system design and verification methods must influence effort to certify future quantum technologies.

  11. Statistical evaluation of PACSTAT random number generation capabilities

    SciTech Connect

    Piepel, G.F.; Toland, M.R.; Harty, H.; Budden, M.J.; Bartley, C.L.

    1988-05-01

    This report summarizes the work performed in verifying the general purpose Monte Carlo driver-program PACSTAT. The main objective of the work was to verify the performance of PACSTAT's random number generation capabilities. Secondary objectives were to document (using controlled configuration management procedures) changes made in PACSTAT at Pacific Northwest Laboratory, and to assure that PACSTAT input and output files satisfy quality assurance traceability constraints. Upon receipt of the PRIME version of the PACSTAT code from the Basalt Waste Isolation Project, Pacific Northwest Laboratory staff converted the code to run on Digital Equipment Corporation (DEC) VAXs. The modifications to PACSTAT were implemented using the WITNESS configuration management system, with the modifications themselves intended to make the code as portable as possible. Certain modifications were made to make the PACSTAT input and output files conform to quality assurance traceability constraints. 10 refs., 17 figs., 6 tabs.

  12. Statistical analysis of random duration times

    SciTech Connect

    Engelhardt, M.E.

    1996-04-01

    This report presents basic statistical methods for analyzing data obtained by observing random time durations. It gives nonparametric estimates of the cumulative distribution function, reliability function and cumulative hazard function. These results can be applied with either complete or censored data. Several models which are commonly used with time data are discussed, and methods for model checking and goodness-of-fit tests are discussed. Maximum likelihood estimates and confidence limits are given for the various models considered. Some results for situations where repeated durations such as repairable systems are also discussed.

  13. Truly Random Number Generator Promises Stronger Encryption Across...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    So, when an application -- even a cryptographic application -- calls for a random ... Whitewood also announced an integration with Cryptsoft, an OEM provider of a key management ...

  14. Multi-bit quantum random number generation by measuring positions of arrival photons

    SciTech Connect

    Yan, Qiurong; Zhao, Baosheng; Liao, Qinghong; Zhou, Nanrun

    2014-10-15

    We report upon the realization of a novel multi-bit optical quantum random number generator by continuously measuring the arrival positions of photon emitted from a LED using MCP-based WSA photon counting imaging detector. A spatial encoding method is proposed to extract multi-bits random number from the position coordinates of each detected photon. The randomness of bits sequence relies on the intrinsic randomness of the quantum physical processes of photonic emission and subsequent photoelectric conversion. A prototype has been built and the random bit generation rate could reach 8 Mbit/s, with random bit generation efficiency of 16 bits per detected photon. FPGA implementation of Huffman coding is proposed to reduce the bias of raw extracted random bits. The random numbers passed all tests for physical random number generator.

  15. Bad Estimates as a Function of Exceeding the MCNP Random Number Stride

    SciTech Connect

    Booth, Thomas E.

    2014-05-05

    Examples of bad MCNP estimates resulting from exceeding the MCNP random number stride are given for a simple infinite medium problem.

  16. Time series, correlation matrices and random matrix models

    SciTech Connect

    Vinayak; Seligman, Thomas H.

    2014-01-08

    In this set of five lectures the authors have presented techniques to analyze open classical and quantum systems using correlation matrices. For diverse reasons we shall see that random matrices play an important role to describe a null hypothesis or a minimum information hypothesis for the description of a quantum system or subsystem. In the former case various forms of correlation matrices of time series associated with the classical observables of some system. The fact that such series are necessarily finite, inevitably introduces noise and this finite time influence lead to a random or stochastic component in these time series. By consequence random correlation matrices have a random component, and corresponding ensembles are used. In the latter we use random matrices to describe high temperature environment or uncontrolled perturbations, ensembles of differing chaotic systems etc. The common theme of the lectures is thus the importance of random matrix theory in a wide range of fields in and around physics.

  17. Robust random number generation using steady-state emission of gain-switched laser diodes

    SciTech Connect

    Yuan, Z. L. Lucamarini, M.; Dynes, J. F.; Frhlich, B.; Plews, A.; Shields, A. J.

    2014-06-30

    We demonstrate robust, high-speed random number generation using interference of the steady-state emission of guaranteed random phases, obtained through gain-switching a semiconductor laser diode. Steady-state emission tolerates large temporal pulse misalignments and therefore significantly improves the interference quality. Using an 8-bit digitizer followed by a finite-impulse-response unbiasing algorithm, we achieve random number generation rates of 8 and 20?Gb/s, for laser repetition rates of 1 and 2.5?GHz, respectively, with a 20% tolerance in the interferometer differential delay. We also report a generation rate of 80?Gb/s using partially phase-correlated short pulses. In relation to the field of quantum key distribution, our results confirm the gain-switched laser diode as a suitable light source, capable of providing phase-randomized coherent pulses at a clock rate of up to 2.5?GHz.

  18. Oracle inequalities for SVMs that are based on random entropy numbers

    SciTech Connect

    Steinwart, Ingo

    2009-01-01

    In this paper we present a new technique for bounding local Rademacher averages of function classes induced by a loss function and a reproducing kernel Hilbert space (RKHS). At the heart of this technique lies the observation that certain expectations of random entropy numbers can be bounded by the eigenvalues of the integral operator associated to the RKHS. We then work out the details of the new technique by establishing two new oracle inequalities for SVMs, which complement and generalize orevious results.

  19. Statistical Stability and Time-Reversal Imgaing in Random Media

    SciTech Connect

    Berryman, J; Borcea, L; Papanicolaou, G; Tsogka, C

    2002-02-05

    Localization of targets imbedded in a heterogeneous background medium is a common problem in seismic, ultrasonic, and electromagnetic imaging problems. The best imaging techniques make direct use of the eigenfunctions and eigenvalues of the array response matrix, as recent work on time-reversal acoustics has shown. Of the various imaging functionals studied, one that is representative of a preferred class is a time-domain generalization of MUSIC (MUltiple Signal Classification), which is a well-known linear subspace method normally applied only in the frequency domain. Since statistical stability is not characteristic of the frequency domain, a transform back to the time domain after first diagonalizing the array data in the frequency domain takes optimum advantage of both the time-domain stability and the frequency-domain orthogonality of the relevant eigenfunctions.

  20. Scaling Behavior of the First Arrival Time of a Random-Walking Magnetic Domain

    SciTech Connect

    Im, M.-Y.; Lee, S.-H.; Kim, D.-H.; Fischer, P.; Shin, S.-C.

    2008-02-04

    We report a universal scaling behavior of the first arrival time of a traveling magnetic domain wall into a finite space-time observation window of a magneto-optical microscope enabling direct visualization of a Barkhausen avalanche in real time. The first arrival time of the traveling magnetic domain wall exhibits a nontrivial fluctuation and its statistical distribution is described by universal power-law scaling with scaling exponents of 1.34 {+-} 0.07 for CoCr and CoCrPt films, despite their quite different domain evolution patterns. Numerical simulation of the first arrival time with an assumption that the magnetic domain wall traveled as a random walker well matches our experimentally observed scaling behavior, providing an experimental support for the random-walking model of traveling magnetic domain walls.

  1. Flow Intermittency, Dispersion, and Correlated Continuous Time Random Walks in Porous Media

    SciTech Connect

    de Anna, Pietro; Le Borgne, Tanguy; Dentz, Marco; Tartakovsky, Alexandre M.; Bolster, Diogo; Davy, Philippe

    2013-05-01

    We study the intermittency of fluid velocities in porous media and its relation to anomalous dispersion. Lagrangian velocities measured at equidistant points along streamlines are shown to form a spatial Markov process. As a consequence of this remarkable property, the dispersion of fluid particles can be described by a continuous time random walk with correlated temporal increments. This new dynamical picture of intermittency provides a direct link between the microscale flow, its intermittent properties, and non-Fickian dispersion.

  2. Dynamic Response of an Optomechanical System to a Stationary Random Excitation in the Time Domain

    DOE PAGES [OSTI]

    Palmer, Jeremy A.; Paez, Thomas L.

    2011-01-01

    Modern electro-optical instruments are typically designed with assemblies of optomechanical members that support optics such that alignment is maintained in service environments that include random vibration loads. This paper presents a nonlinear numerical analysis that calculates statistics for the peak lateral response of optics in an optomechanical sub-assembly subject to random excitation of the housing. The work is unique in that the prior art does not address peak response probability distribution for stationary random vibration in the time domain for a common lens-retainer-housing system with Coulomb damping. Analytical results are validated by using displacement response data from random vibration testingmore » of representative prototype sub-assemblies. A comparison of predictions to experimental results yields reasonable agreement. The Type I Asymptotic form provides the cumulative distribution function for peak response probabilities. Probabilities are calculated for actual lens centration tolerances. The probability that peak response will not exceed the centration tolerance is greater than 80% for prototype configurations where the tolerance is high (on the order of 30 micrometers). Conversely, the probability is low for those where the tolerance is less than 20 micrometers. The analysis suggests a design paradigm based on the influence of lateral stiffness on the magnitude of the response.« less

  3. Engine autoignition: The relationship between octane numbers and autoignition delay times

    SciTech Connect

    Bradley, Derek; Head, R.A.

    2006-11-15

    The research octane (RON) and motor octane (MON) numbers, carefully measured in standardized tests, are the principal parameters for characterizing autoignition of gasoline in engines. Increasingly, engines operate under different conditions of temperature, pressure, and mixture strength from those in these tests. As a result, RON and MON values become an incomplete guide to the onset of knock, and the octane index (OI), an octane number under operational conditions, is often measured. Values of the OI were measured with different fuels in a controlled autoignition single-cylinder engine, at different initial temperatures and pressures, at the instant of 10% heat release. Fundamental understanding of engine autoignition was sought by finding the OIs of different non-primary reference fuels (non-PRFs) by identifying the corresponding PRFs that give 10% heat release under identical conditions. The autoignition delay times of the PRFs were obtained from the shock tube data, for different temperatures and pressures, of the Aachen group. It was sufficiently accurate to equate the delay time of a non-PRF to that of the corresponding PRF under the same conditions for 10% heat release. The PRFs exhibited a higher value of the inverse pressure exponent for the delay time than the non-PRFs. Together with different temperature dependencies, these gave autoignition delay times of non-PRFs that could be higher than those of their associated RONs. This tendency increased with pressure and decreased with temperature and was most marked with olefenic and toluenic fuels. This could result in values of the OI that were higher than the RON of the fuel. This is important because, for a number of evolutionary reasons, engine pressures are tending to increase and temperatures to decrease. (author)

  4. Beyond the random phase approximation: Stimulated Brillouin backscatter for finite laser coherence times

    SciTech Connect

    Korotkevich, Alexander O.; Lushnikov, Pavel M.; Rose, Harvey A.

    2015-01-15

    We developed a linear theory of backward stimulated Brillouin scatter (BSBS) of a spatially and temporally random laser beam relevant for laser fusion. Our analysis reveals a new collective regime of BSBS (CBSBS). Its intensity threshold is controlled by diffraction, once cT{sub c} exceeds a laser speckle length, with T{sub c} the laser coherence time. The BSBS spatial gain rate is approximately the sum of that due to CBSBS, and a part which is independent of diffraction and varies linearly with T{sub c}. The CBSBS spatial gain rate may be reduced significantly by the temporal bandwidth of KrF-based laser systems compared to the bandwidth currently available to temporally smoothed glass-based laser systems.

  5. Characterization of compounds by time-of-flight measurement utilizing random fast ions

    DOEpatents

    Conzemius, R.J.

    1989-04-04

    An apparatus is described for characterizing the mass of sample and daughter particles, comprising a source for providing sample ions; a fragmentation region wherein a fraction of the sample ions may fragment to produce daughter ion particles; an electrostatic field region held at a voltage level sufficient to effect ion-neutral separation and ion-ion separation of fragments from the same sample ion and to separate ions of different kinetic energy; a detector system for measuring the relative arrival times of particles; and processing means operatively connected to the detector system to receive and store the relative arrival times and operable to compare the arrival times with times detected at the detector when the electrostatic field region is held at a different voltage level and to thereafter characterize the particles. Sample and daughter particles are characterized with respect to mass and other characteristics by detecting at a particle detector the relative time of arrival for fragments of a sample ion at two different electrostatic voltage levels. The two sets of particle arrival times are used in conjunction with the known altered voltage levels to mathematically characterize the sample and daughter fragments. In an alternative embodiment the present invention may be used as a detector for a conventional mass spectrometer. In this embodiment, conventional mass spectrometry analysis is enhanced due to further mass resolving of the detected ions. 8 figs.

  6. Characterization of compounds by time-of-flight measurement utilizing random fast ions

    DOEpatents

    Conzemius, Robert J.

    1989-01-01

    An apparatus for characterizing the mass of sample and daughter particles, comprising a source for providing sample ions; a fragmentation region wherein a fraction of the sample ions may fragment to produce daughter ion particles; an electrostatic field region held at a voltage level sufficient to effect ion-neutral separation and ion-ion separation of fragments from the same sample ion and to separate ions of different kinetic energy; a detector system for measuring the relative arrival times of particles; and processing means operatively connected to the detector system to receive and store the relative arrival times and operable to compare the arrival times with times detected at the detector when the electrostatic field region is held at a different voltage level and to thereafter characterize the particles. Sample and daughter particles are characterized with respect to mass and other characteristics by detecting at a particle detector the relative time of arrival for fragments of a sample ion at two different electrostatic voltage levels. The two sets of particle arrival times are used in conjunction with the known altered voltage levels to mathematically characterize the sample and daughter fragments. In an alternative embodiment the present invention may be used as a detector for a conventional mass spectrometer. In this embodiment, conventional mass spectrometry analysis is enhanced due to further mass resolving of the detected ions.

  7. A Possible Approach to Inclusion of Space and Time in Frame Fields of Quantum Representations of Real and Complex Numbers

    DOE PAGES [OSTI]

    Benioff, Paul

    2009-01-01

    Tmore » his work is based on the field of reference frames based on quantum representations of real and complex numbers described in other work. Here frame domains are expanded to include space and time lattices. Strings of qukits are described as hybrid systems as they are both mathematical and physical systems. As mathematical systems they represent numbers. As physical systems in each frame the strings have a discrete Schrodinger dynamics on the lattices.he frame field has an iterative structure such that the contents of a stage j frame have images in a stage j - 1 (parent) frame. A discussion of parent frame images includes the proposal that points of stage j frame lattices have images as hybrid systems in parent frames.he resulting association of energy with images of lattice point locations, as hybrid systems states, is discussed. Representations and images of other physical systems in the different frames are also described.« less

  8. Method and allocation device for allocating pending requests for data packet transmission at a number of inputs to a number of outputs of a packet switching device in successive time slots

    SciTech Connect

    Abel, Francois; Iliadis, Ilias; Minkenberg, Cyriel J. A.

    2009-02-03

    A method for allocating pending requests for data packet transmission at a number of inputs to a number of outputs of a switching system in successive time slots, including a matching method including the steps of providing a first request information in a first time slot indicating data packets at the inputs requesting transmission to the outputs of the switching system, performing a first step in the first time slot depending on the first request information to obtain a first matching information, providing a last request information in a last time slot successive to the first time slot, performing a last step in the last time slot depending on the last request information and depending on the first matching information to obtain a final matching information, and assigning the pending data packets at the number of inputs to the number of outputs based on the final matching information.

  9. Discrete-Time Pricing and Optimal Exercise of American Perpetual Warrants in the Geometric Random Walk Model

    SciTech Connect

    Vanderbei, Robert J.; P Latin-Small-Letter-Dotless-I nar, Mustafa C.; Bozkaya, Efe B.

    2013-02-15

    An American option (or, warrant) is the right, but not the obligation, to purchase or sell an underlying equity at any time up to a predetermined expiration date for a predetermined amount. A perpetual American option differs from a plain American option in that it does not expire. In this study, we solve the optimal stopping problem of a perpetual American option (both call and put) in discrete time using linear programming duality. Under the assumption that the underlying stock price follows a discrete time and discrete state Markov process, namely a geometric random walk, we formulate the pricing problem as an infinite dimensional linear programming (LP) problem using the excessive-majorant property of the value function. This formulation allows us to solve complementary slackness conditions in closed-form, revealing an optimal stopping strategy which highlights the set of stock-prices where the option should be exercised. The analysis for the call option reveals that such a critical value exists only in some cases, depending on a combination of state-transition probabilities and the economic discount factor (i.e., the prevailing interest rate) whereas it ceases to be an issue for the put.

  10. (Document Number)

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    A TA-53 TOUR FORM/RADIOLOGICAL LOG (Send completed form to MS H831) _____________ _____________________________ _________________________________ Tour Date Purpose of Tour or Tour Title Start Time and Approximate Duration ___________________________ ______________ _______________________ _________________ Tour Point of Contact/Requestor Z# (if applicable) Organization/Phone Number Signature Locations Visited: (Check all that apply, and list any others not shown. Prior approval must be obtained

  11. SPRNG Scalable Parallel Random Number Generator LIbrary

    Energy Science and Technology Software Center

    2010-03-16

    This revision corrects some errors in SPRNG 1. Users of newer SPRNG versions can obtain the corrected files and build their version with it. This version also improves the scalability of some of the application-based tests in the SPRNG test suite. It also includes an interface to a parallel Mersenne Twister, so that if users install the Mersenne Twister, then they can test this generator with the SPRNG test suite and also use some SPRNGmore » features with that generator.« less

  12. Lattice Boltzmann simulation of solute transport in heterogeneous porous media with conduits to estimate macroscopic continuous time random walk model parameters

    SciTech Connect

    Anwar, S.; Cortis, A.; Sukop, M.

    2008-10-20

    Lattice Boltzmann models simulate solute transport in porous media traversed by conduits. Resulting solute breakthrough curves are fitted with Continuous Time Random Walk models. Porous media are simulated by damping flow inertia and, when the damping is large enough, a Darcy's Law solution instead of the Navier-Stokes solution normally provided by the lattice Boltzmann model is obtained. Anisotropic dispersion is incorporated using a direction-dependent relaxation time. Our particular interest is to simulate transport processes outside the applicability of the standard Advection-Dispersion Equation (ADE) including eddy mixing in conduits. The ADE fails to adequately fit any of these breakthrough curves.

  13. Second moment of multiple-quantum NMR and a time-dependent growth of the number of multispin correlations in solids

    SciTech Connect

    Zobov, V. E. Lundin, A. A.

    2006-12-15

    The time evolution of multispin correlations (the growth of the number of correlated spins as a function of time) can be observed directly using the multiple-quantum nuclear magnetic resonance spectroscopy of solids. A quantity related to this number, namely, the second moment of the intensity distribution of coherences of different orders in the multiple-quantum spectrum can be calculated using the theory proposed in this work. An approach to the calculation of the four-spin time correlation function through which this moment is expressed is developed. The main sequences of contributions in the expansion of this function into a time power series are summed using the approximation of a large number of neighbors both for systems with a secular dipole-dipole interaction and for systems with a nonsecular effective interaction. An exponential dependence of is obtained. The value of is additionally calculated using an expansion in terms of orthogonal operators for three model examples corresponding to different limiting realizations of spin systems. It is shown that the results of the microscopic theory at least qualitatively agree with both the results obtained for model examples and experimental results obtained recently for adamantane.

  14. Fast generation of sparse random kernel graphs

    SciTech Connect

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  15. Fast generation of sparse random kernel graphs

    DOE PAGES [OSTI]

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  16. Request Number:

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    1074438 Name: Gayle Cooper Organization: nla Address: _ Country: United States Phone Number: Fax Number: nla E-mail: . ~===--------- Reasonably Describe Records Description: Information pertaining to the Department of Energy's cost estimate for reinstating pension benefit service years to the Enterprise Company (ENCO) employees who are active plan participants in the Hanford Site Pension Plan. This cost estimate was an outcome of the DOE's Worker Town Hall Meetings held on September 17-18, 2009.

  17. Time

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    3 4 5 6 7 8 9 10 Time with respect to the BNB Trigger Time [µs] 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Fractional Flash Count per 0.15 µs with respect to Cosmic Background Measured Cosmic Rate (Beam-Off) BNB Trigger Data (Beam-On) [4.51E18 POT]

  18. Time

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    10 15 20 Time with respect to the NuMI Trigger Time [µs] 0.9 1.0 1.1 1.2 1.3 1.4 1.5 Fractional Flash Count per 0.5 µs with respect to Cosmic Background Measured Cosmic Rate (Beam-Off) NuMI Trigger Data (Beam-On) [4.83E18 POT]

  19. USER S GUIDE FOR THE RANDOM DRUG SCREENING SYSTEM

    SciTech Connect

    McNeany, Karen I

    2013-12-01

    The Random Drug Screening System (RDSS) is a desktop computing application designed to assign nongameable drug testing dates to each member in a population of employees, within a specific time line. The program includes reporting capabilities, test form generation, unique test ID number assignment, and the ability to flag high-risk employees for a higher frequency of drug testing than the general population.

  20. Random one-of-N selector

    DOEpatents

    Kronberg, J.W.

    1993-04-20

    An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

  1. Random one-of-N selector

    DOEpatents

    Kronberg, James W.

    1993-01-01

    An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

  2. Report number codes

    SciTech Connect

    Nelson, R.N.

    1985-05-01

    This publication lists all report number codes processed by the Office of Scientific and Technical Information. The report codes are substantially based on the American National Standards Institute, Standard Technical Report Number (STRN)-Format and Creation Z39.23-1983. The Standard Technical Report Number (STRN) provides one of the primary methods of identifying a specific technical report. The STRN consists of two parts: The report code and the sequential number. The report code identifies the issuing organization, a specific program, or a type of document. The sequential number, which is assigned in sequence by each report issuing entity, is not included in this publication. Part I of this compilation is alphabetized by report codes followed by issuing installations. Part II lists the issuing organization followed by the assigned report code(s). In both Parts I and II, the names of issuing organizations appear for the most part in the form used at the time the reports were issued. However, for some of the more prolific installations which have had name changes, all entries have been merged under the current name.

  3. Number | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Property:NumOfPlants Property:NumProdWells Property:NumRepWells Property:Number of Color Cameras Property:Number of Devices Deployed Property:Number of Plants included in...

  4. NSR Key Number Retrieval

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    NSR Key Number Retrieval Pease enter key in the box Submit

  5. Compendium of Experimental Cetane Numbers

    SciTech Connect

    Yanowitz, J.; Ratcliff, M. A.; McCormick, R. L.; Taylor, J. D.; Murphy, M. J.

    2014-08-01

    This report is an updated version of the 2004 Compendium of Experimental Cetane Number Data and presents a compilation of measured cetane numbers for pure chemical compounds. It includes all available single compound cetane number data found in the scientific literature up until March 2014 as well as a number of unpublished values, most measured over the past decade at the National Renewable Energy Laboratory. This Compendium contains cetane values for 389 pure compounds, including 189 hydrocarbons and 201 oxygenates. More than 250 individual measurements are new to this version of the Compendium. For many compounds, numerous measurements are included, often collected by different researchers using different methods. Cetane number is a relative ranking of a fuel's autoignition characteristics for use in compression ignition engines; it is based on the amount of time between fuel injection and ignition, also known as ignition delay. The cetane number is typically measured either in a single-cylinder engine or a constant volume combustion chamber. Values in the previous Compendium derived from octane numbers have been removed, and replaced with a brief analysis of the correlation between cetane numbers and octane numbers. The discussion on the accuracy and precision of the most commonly used methods for measuring cetane has been expanded and the data has been annotated extensively to provide additional information that will help the reader judge the relative reliability of individual results.

  6. Big Numbers | Jefferson Lab

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Big Numbers Big Numbers May 16, 2011 This article has some numbers in it. In principle, numbers are just language, like English or Japanese. Nevertheless, it is true that not everyone is comfortable or facile with numbers and may be turned off by too many of them. To those people, I apologize that this article pays less attention to maximizing the readership than some I do. But sometimes it's just appropriate to indulge one's self, so here goes. When we discuss the performance of some piece of

  7. Florida Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Florida Natural Gas Number of Oil Wells (Number of ... Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Florida ...

  8. Random array grid collimator

    DOEpatents

    Fenimore, E.E.

    1980-08-22

    A hexagonally shaped quasi-random no-two-holes touching grid collimator. The quasi-random array grid collimator eliminates contamination from small angle off-axis rays by using a no-two-holes-touching pattern which simultaneously provides for a self-supporting array increasng throughput by elimination of a substrate. The presentation invention also provides maximum throughput using hexagonally shaped holes in a hexagonal lattice pattern for diffraction limited applications. Mosaicking is also disclosed for reducing fabrication effort.

  9. Florida Natural Gas Number of Commercial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Florida Natural Gas Number of Commercial ... Referring Pages: Number of Natural Gas Commercial Consumers Florida Number of Natural Gas ...

  10. Florida Natural Gas Number of Industrial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Florida Natural Gas Number of Industrial ... Referring Pages: Number of Natural Gas Industrial Consumers Florida Number of Natural Gas ...

  11. Florida Natural Gas Number of Residential Consumers (Number of...

    Gasoline and Diesel Fuel Update

    Residential Consumers (Number of Elements) Florida Natural Gas Number of Residential ... Referring Pages: Number of Natural Gas Residential Consumers Florida Number of Natural Gas ...

  12. New York Natural Gas Number of Commercial Consumers (Number of...

    Annual Energy Outlook

    Commercial Consumers (Number of Elements) New York Natural Gas Number of Commercial ... Referring Pages: Number of Natural Gas Commercial Consumers New York Number of Natural Gas ...

  13. New Mexico Natural Gas Number of Commercial Consumers (Number...

    Gasoline and Diesel Fuel Update

    Commercial Consumers (Number of Elements) New Mexico Natural Gas Number of Commercial ... Referring Pages: Number of Natural Gas Commercial Consumers New Mexico Number of Natural ...

  14. North Dakota Natural Gas Number of Commercial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) North Dakota Natural Gas Number of Commercial ... Referring Pages: Number of Natural Gas Commercial Consumers North Dakota Number of Natural ...

  15. Randomized Linear Algebra for BioImaging

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Randomized Linear Algebra for BioImaging Randomized Linear Algebra for BioImaging Mass-Spectrometry Imaging Mass spectrometry measures ions derived from the molecules present in a biological sample. Spectra of the ions are acquired at each location (pixel) of a sample, allowing for the collection of spatially resolved mass spectra. This mode of analysis is known as mass spectrometry imaging (MSI). The addition of ion-mobility separation (IMS) to MSI adds another dimension, drift time. The

  16. ALARA notes, Number 8

    SciTech Connect

    Khan, T.A.; Baum, J.W.; Beckman, M.C.

    1993-10-01

    This document contains information dealing with the lessons learned from the experience of nuclear plants. In this issue the authors tried to avoid the `tyranny` of numbers and concentrated on the main lessons learned. Topics include: filtration devices for air pollution abatement, crack repair and inspection, and remote handling equipment.

  17. Electrokinetic transport in microchannels with random roughness

    SciTech Connect

    Wang, Moran [Los Alamos National Laboratory; Kang, Qinjun [Los Alamos National Laboratory

    2008-01-01

    We present a numerical framework to model the electrokinetic transport in microchannels with random roughness. The three-dimensional microstructure of the rough channel is generated by a random generation-growth method with three statistical parameters to control the number density, the total volume fraction, and the anisotropy characteristics of roughness elements. The governing equations for the electrokinetic transport are solved by a high-efficiency lattice Poisson?Boltzmann method in complex geometries. The effects from the geometric characteristics of roughness on the electrokinetic transport in microchannels are therefore modeled and analyzed. For a given total roughness volume fraction, a higher number density leads to a lower fluctuation because of the random factors. The electroosmotic flow rate increases with the roughness number density nearly logarithmically for a given volume fraction of roughness but decreases with the volume fraction for a given roughness number density. When both the volume fraction and the number density of roughness are given, the electroosmotic flow rate is enhanced by the increase of the characteristic length along the external electric field direction but is reduced by that in the direction across the channel. For a given microstructure of the rough microchannel, the electroosmotic flow rate decreases with the Debye length. It is found that the shape resistance of roughness is responsible for the flow rate reduction in the rough channel compared to the smooth channel even for very thin double layers, and hence plays an important role in microchannel electroosmotic flows.

  18. Sublinear scaling for time-dependent stochastic density functional theory

    SciTech Connect

    Gao, Yi; Neuhauser, Daniel; Baer, Roi; Rabani, Eran

    2015-01-21

    A stochastic approach to time-dependent density functional theory is developed for computing the absorption cross section and the random phase approximation (RPA) correlation energy. The core idea of the approach involves time-propagation of a small set of stochastic orbitals which are first projected on the occupied space and then propagated in time according to the time-dependent Kohn-Sham equations. The evolving electron density is exactly represented when the number of random orbitals is infinite, but even a small number (≈16) of such orbitals is enough to obtain meaningful results for absorption spectrum and the RPA correlation energy per electron. We implement the approach for silicon nanocrystals using real-space grids and find that the overall scaling of the algorithm is sublinear with computational time and memory.

  19. Modular redundant number systems

    SciTech Connect

    1998-05-31

    With the increased use of public key cryptography, faster modular multiplication has become an important cryptographic issue. Almost all public key cryptography, including most elliptic curve systems, use modular multiplication. Modular multiplication, particularly for the large public key modulii, is very slow. Increasing the speed of modular multiplication is almost synonymous with increasing the speed of public key cryptography. There are two parts to modular multiplication: multiplication and modular reduction. Though there are fast methods for multiplying and fast methods for doing modular reduction, they do not mix well. Most fast techniques require integers to be in a special form. These special forms are not related and converting from one form to another is more costly than using the standard techniques. To this date it has been better to use the fast modular reduction technique coupled with standard multiplication. Standard modular reduction is much more costly than standard multiplication. Fast modular reduction (Montgomery`s method) reduces the reduction cost to approximately that of a standard multiply. Of the fast multiplication techniques, the redundant number system technique (RNS) is one of the most popular. It is simple, converting a large convolution (multiply) into many smaller independent ones. Not only do redundant number systems increase speed, but the independent parts allow for parallelization. RNS form implies working modulo another constant. Depending on the relationship between these two constants; reduction OR division may be possible, but not both. This paper describes a new technique using ideas from both Montgomery`s method and RNS. It avoids the formula problem and allows fast reduction and multiplication. Since RNS form is used throughout, it also allows the entire process to be parallelized.

  20. Random Selection for Drug Screening

    SciTech Connect

    Center for Human Reliability Studies

    2007-05-01

    Simple random sampling is generally the starting point for a random sampling process. This sampling technique ensures that each individual within a group (population) has an equal chance of being selected. There are a variety of ways to implement random sampling in a practical situation.

  1. Wyoming Natural Gas Number of Residential Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Wyoming Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  2. Virginia Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  3. Utah Natural Gas Number of Industrial Consumers (Number of Elements...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Utah Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 ...

  4. Wisconsin Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Wisconsin Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  5. Virginia Natural Gas Number of Commercial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  6. Wyoming Natural Gas Number of Industrial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Wyoming Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  7. Utah Natural Gas Number of Residential Consumers (Number of Elements...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Utah Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  8. Vermont Natural Gas Number of Residential Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Vermont Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  9. Utah Natural Gas Number of Commercial Consumers (Number of Elements...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Utah Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 ...

  10. Virginia Natural Gas Number of Industrial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  11. West Virginia Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) West Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  12. Wisconsin Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Wisconsin Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  13. Vermont Natural Gas Number of Commercial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Vermont Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  14. Wyoming Natural Gas Number of Commercial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Wyoming Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  15. West Virginia Natural Gas Number of Commercial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) West Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  16. Washington Natural Gas Number of Commercial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Washington Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  17. Washington Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Washington Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  18. Washington Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Washington Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  19. Wisconsin Natural Gas Number of Commercial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Wisconsin Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  20. Vermont Natural Gas Number of Industrial Consumers (Number of...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Vermont Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  1. West Virginia Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) West Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  2. New York Natural Gas Number of Residential Consumers (Number...

    Annual Energy Outlook

    Residential Consumers (Number of Elements) New York Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  3. New Mexico Natural Gas Number of Residential Consumers (Number...

    Gasoline and Diesel Fuel Update

    Residential Consumers (Number of Elements) New Mexico Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  4. New Jersey Natural Gas Number of Residential Consumers (Number...

    Gasoline and Diesel Fuel Update

    Residential Consumers (Number of Elements) New Jersey Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  5. North Carolina Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) North Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  6. North Carolina Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) North Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  7. North Dakota Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) North Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  8. North Dakota Natural Gas Number of Residential Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) North Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  9. North Carolina Natural Gas Number of Commercial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) North Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  10. New Hampshire Natural Gas Number of Commercial Consumers (Number...

    Gasoline and Diesel Fuel Update

    Commercial Consumers (Number of Elements) New Hampshire Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  11. New Hampshire Natural Gas Number of Industrial Consumers (Number...

    Gasoline and Diesel Fuel Update

    Industrial Consumers (Number of Elements) New Hampshire Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  12. New Hampshire Natural Gas Number of Residential Consumers (Number...

    Annual Energy Outlook

    Residential Consumers (Number of Elements) New Hampshire Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 ...

  13. New Mexico Natural Gas Number of Industrial Consumers (Number...

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) New Mexico Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 ...

  14. Recombination of polynucleotide sequences using random or defined primers

    DOEpatents

    Arnold, Frances H.; Shao, Zhixin; Affholter, Joseph A.; Zhao, Huimin; Giver, Lorraine J.

    2001-01-01

    A method for in vitro mutagenesis and recombination of polynucleotide sequences based on polymerase-catalyzed extension of primer oligonucleotides is disclosed. The method involves priming template polynucleotide(s) with random-sequences or defined-sequence primers to generate a pool of short DNA fragments with a low level of point mutations. The DNA fragments are subjected to denaturization followed by annealing and further enzyme-catalyzed DNA polymerization. This procedure is repeated a sufficient number of times to produce full-length genes which comprise mutants of the original template polynucleotides. These genes can be further amplified by the polymerase chain reaction and cloned into a vector for expression of the encoded proteins.

  15. Recombination of polynucleotide sequences using random or defined primers

    DOEpatents

    Arnold, Frances H.; Shao, Zhixin; Affholter, Joseph A.; Zhao, Huimin H; Giver, Lorraine J.

    2000-01-01

    A method for in vitro mutagenesis and recombination of polynucleotide sequences based on polymerase-catalyzed extension of primer oligonucleotides is disclosed. The method involves priming template polynucleotide(s) with random-sequences or defined-sequence primers to generate a pool of short DNA fragments with a low level of point mutations. The DNA fragments are subjected to denaturization followed by annealing and further enzyme-catalyzed DNA polymerization. This procedure is repeated a sufficient number of times to produce full-length genes which comprise mutants of the original template polynucleotides. These genes can be further amplified by the polymerase chain reaction and cloned into a vector for expression of the encoded proteins.

  16. Organization of growing random networks

    SciTech Connect

    Krapivsky, P. L.; Redner, S.

    2001-06-01

    The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.

  17. The peculiar phase structure of random graph bisection

    SciTech Connect

    Percus, Allon G; Istrate, Gabriel; Goncalves, Bruno T; Sumi, Robert Z

    2008-01-01

    The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of 'cut' edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.

  18. Number

    Office of Legacy Management (LM)

    It is seen that all operations are performed vet, thus eliminating almost entirely a dust exposure hazard. A* Monazite sand is at present derived from India which supplies an ore ...

  19. Tennessee Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Tennessee Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 52 75 NA NA NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Tennessee Natural Gas Summ

  20. Texas Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Texas Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 85,030 94,203 96,949 104,205 105,159 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Texas Natural

  1. Pennsylvania Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Pennsylvania Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 7,046 7,627 7,164 8,481 7,557 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Pennsylvania

  2. Louisiana Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Louisiana Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 5,201 5,057 5,078 5,285 4,968 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Louisiana Natural

  3. Michigan Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Michigan Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 510 514 537 584 532 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Michigan Natural Gas Summary

  4. Mississippi Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Mississippi Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 561 618 581 540 501 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Mississippi Natural Gas

  5. Missouri Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Missouri Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 1 1 1 1 NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Missouri Natural Gas Summary

  6. Montana Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Montana Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 1,956 2,147 2,268 2,377 2,277 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Montana Natural Gas

  7. Nebraska Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Nebraska Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 84 73 54 51 51 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Nebraska Natural Gas Summar

  8. Nevada Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Nevada Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 4 4 4 4 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Nevada Natural Gas Summary

  9. Ohio Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Ohio Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 6,775 6,745 7,038 7,257 5,941 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Ohio Natural Gas

  10. Oklahoma Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Oklahoma Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 6,723 7,360 8,744 7,105 8,368 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Oklahoma Natural

  11. Alabama Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Alabama Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 346 367 402 436 414 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Alabama Natural Gas Sum

  12. Alaska Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Alaska Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 2,040 1,981 2,006 2,042 2,096 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Alaska Natural Gas

  13. Arizona Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Arizona Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 1 1 1 0 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Arizona Natural Gas Summary

  14. Arkansas Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Arkansas Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 165 174 218 233 240 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Arkansas Natural Gas

  15. California Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) California Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 25,958 26,061 26,542 26,835 27,075 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) California

  16. Colorado Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Colorado Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 5,963 6,456 6,799 7,771 7,733 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Colorado Natural

  17. Utah Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Utah Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 3,119 3,520 3,946 4,249 3,966 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Utah Natural Gas

  18. Virginia Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Virginia Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 2 1 1 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Virginia Natural Gas Summary

  19. Wyoming Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Wyoming Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 4,430 4,563 4,391 4,538 4,603 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Wyoming Natural Gas

  20. Kentucky Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) Kentucky Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 317 358 340 NA NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Kentucky Natural Gas Su

  1. Compendium of Experimental Cetane Number Data

    SciTech Connect

    Murphy, M. J.; Taylor, J. D.; McCormick, R. L.

    2004-09-01

    In this report, we present a compilation of reported cetane numbers for pure chemical compounds. The compiled database contains cetane values for 299 pure compounds, including 156 hydrocarbons and 143 oxygenates. Cetane number is a relative ranking of fuels based on the amount of time between fuel injection and ignition. The cetane number is typically measured either in a combustion bomb or in a single-cylinder research engine. This report includes cetane values from several different measurement techniques - each of which has associated uncertainties. Additionally, many of the reported values are determined by measuring blending cetane numbers, which introduces significant error. In many cases, the measurement technique is not reported nor is there any discussion about the purity of the compounds. Nonetheless, the data in this report represent the best pure compound cetane number values available from the literature as of August 2004.

  2. Combinatorial theory of the semiclassical evaluation of transport moments. I. Equivalence with the random matrix approach

    SciTech Connect

    Berkolaiko, G.; Kuipers, J.

    2013-11-15

    To study electronic transport through chaotic quantum dots, there are two main theoretical approaches. One involves substituting the quantum system with a random scattering matrix and performing appropriate ensemble averaging. The other treats the transport in the semiclassical approximation and studies correlations among sets of classical trajectories. There are established evaluation procedures within the semiclassical evaluation that, for several linear and nonlinear transport moments to which they were applied, have always resulted in the agreement with random matrix predictions. We prove that this agreement is universal: any semiclassical evaluation within the accepted procedures is equivalent to the evaluation within random matrix theory. The equivalence is shown by developing a combinatorial interpretation of the trajectory sets as ribbon graphs (maps) with certain properties and exhibiting systematic cancellations among their contributions. Remaining trajectory sets can be identified with primitive (palindromic) factorisations whose number gives the coefficients in the corresponding expansion of the moments of random matrices. The equivalence is proved for systems with and without time reversal symmetry.

  3. A stochastic simulation method for the assessment of resistive random access memory retention reliability

    SciTech Connect

    Berco, Dan Tseng, Tseung-Yuen

    2015-12-21

    This study presents an evaluation method for resistive random access memory retention reliability based on the Metropolis Monte Carlo algorithm and Gibbs free energy. The method, which does not rely on a time evolution, provides an extremely efficient way to compare the relative retention properties of metal-insulator-metal structures. It requires a small number of iterations and may be used for statistical analysis. The presented approach is used to compare the relative robustness of a single layer ZrO{sub 2} device with a double layer ZnO/ZrO{sub 2} one, and obtain results which are in good agreement with experimental data.

  4. Maryland Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Maryland Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Maryland Natural Gas Summary

  5. Oregon Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oregon Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Oregon Natural Gas Summary

  6. Alaska Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Alaska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10 11 8 1990's 8 8 10 11 11 9 202 7 7 9 2000's 9 8 9 9 10 12 11 11 6 3 2010's 3 5 3 3 1 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Natural

  7. Hawaii Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Hawaii Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 27 26 29 2000's 28 28 29 29 29 28 26 27 27 25 2010's 24 24 22 22 23 25 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Natural Gas Indu

  8. Indiana Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's NA NA NA NA NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Indiana Natural Gas Summary

  9. Kansas Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) Kansas Natural Gas Summary

  10. ARM - Measurement - Particle number concentration

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    number concentration ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Particle number concentration The number of particles present in any given volume of air. Categories Aerosols Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available measurements, including those

  11. Total Number of Operable Refineries

    Energy Information Administration (EIA) (indexed site)

    Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge

  12. A user`s manual for the program TRES4: Random vibration analysis of vertical-axis wind turbines in turbulent winds

    SciTech Connect

    1994-03-01

    TRES4 is a software package that works with the MSC/NASTRAN finite element analysis code to conduct random vibration analysis of vertical-axis wind turbines. The loads on the turbine are calculated in the time domain to retain the nonlinearities of stalled aerodynamic loadings. The loads are transformed into modal coordinates to reduce the number of degrees of freedom. Power spectra and cross spectra of the loads are calculated in the modal coordinate system. These loads are written in NASTRAN Bulk Data format to be read and applied in a random vibration analysis by NASTRAN. The resulting response is then transformed back to physical coordinates to facilitate user interpretation.

  13. Random Selection for Drug Screening

    SciTech Connect

    Center for Human Reliability Studies

    2007-05-01

    Sampling is the process of choosing some members out of a group or population. Probablity sampling, or random sampling, is the process of selecting members by chance with a known probability of each individual being chosen.

  14. Quasi-Random Sequence Generators.

    Energy Science and Technology Software Center

    1994-03-01

    Version 00 LPTAU generates quasi-random sequences. The sequences are uniformly distributed sets of L=2**30 points in the N-dimensional unit cube: I**N=[0,1]. The sequences are used as nodes for multidimensional integration, as searching points in global optimization, as trial points in multicriteria decision making, as quasi-random points for quasi Monte Carlo algorithms.

  15. Rhode Island Natural Gas Number of Industrial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Industrial Consumers (Number of Elements) Rhode Island Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,158 1,152 1,122 1990's 1,135 1,107 1,096 1,066 1,064 359 363 336 325 302 2000's 317 283 54 236 223 223 245 256 243 260 2010's 249 245 248 271 266 260 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  16. South Dakota Natural Gas Number of Industrial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Industrial Consumers (Number of Elements) South Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 261 267 270 1990's 275 283 319 355 381 396 444 481 464 445 2000's 416 402 533 526 475 542 528 548 598 598 2010's 580 556 574 566 575 578 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  17. Maine Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Maine Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 73 73 74 1990's 80 81 80 66 89 74 87 81 110 108 2000's 178 233 66 65 69 69 73 76 82 85 2010's 94 102 108 120 126 136 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016

  18. Montana Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Montana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 435 435 428 1990's 457 452 459 462 453 463 466 462 454 397 2000's 71 73 439 412 593 716 711 693 693 396 2010's 384 381 372 372 369 366 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date:

  19. Nevada Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Nevada Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 93 98 100 1990's 100 113 114 117 119 120 121 93 93 109 2000's 90 90 96 97 179 192 207 220 189 192 2010's 184 177 177 195 219 215 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date:

  20. Arizona Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Arizona Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 358 344 354 1990's 526 532 532 526 519 530 534 480 514 555 2000's 526 504 488 450 414 425 439 395 383 390 2010's 368 371 379 383 386 400 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release

  1. Delaware Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Delaware Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241 233 235 1990's 240 243 248 249 252 253 250 265 257 264 2000's 297 316 182 184 186 179 170 185 165 112 2010's 114 129 134 138 141 144 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release

  2. Idaho Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Idaho Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 219 132 64 1990's 62 65 66 75 144 167 183 189 203 200 2000's 217 198 194 191 196 195 192 188 199 187 2010's 184 178 179 183 189 187 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date:

  3. Maxima of two random walks: Universal statistics of lead changes

    DOE PAGES [OSTI]

    Ben-Naim, E.; Krapivsky, P. L.; Randon-Furling, J.

    2016-04-18

    In this study, we investigate statistics of lead changes of the maxima of two discrete-time random walks in one dimension. We show that the average number of lead changes grows asmore » $${\\pi }^{-1}\\mathrm{ln}t$$ in the long-time limit. We present theoretical and numerical evidence that this asymptotic behavior is universal. Specifically, this behavior is independent of the jump distribution: the same asymptotic underlies standard Brownian motion and symmetric Lévy flights. We also show that the probability to have at most n lead changes behaves as $${t}^{-1/4}{(\\mathrm{ln}t)}^{n}$$ for Brownian motion and as $${t}^{-\\beta (\\mu )}{(\\mathrm{ln}t)}^{n}$$ for symmetric Lévy flights with index μ. The decay exponent $$\\beta \\equiv \\beta (\\mu )$$ varies continuously with the Lévy index when $$0\\lt \\mu \\lt 2$$, and remains constant $$\\beta =1/4$$ for $$\\mu \\gt 2$$.« less

  4. Departmental Business Instrument Numbering System

    Directives, Delegations, and Other Requirements [Office of Management (MA)]

    2005-01-27

    The Order prescribes the procedures for assigning identifying numbers to all Department of Energy (DOE) and National Nuclear Security Administration (NNSA) business instruments. Cancels DOE O 540.1. Canceled by DOE O 540.1B.

  5. Departmental Business Instrument Numbering System

    Directives, Delegations, and Other Requirements [Office of Management (MA)]

    2000-12-05

    To prescribe procedures for assigning identifying numbers to all Department of Energy (DOE), including the National Nuclear Security Administration, business instruments. Cancels DOE 1331.2B. Canceled by DOE O 540.1A.

  6. Tennessee Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Tennessee Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 77,104 81,159 84,040 1990's 88,753 89,863 91,999 94,860 97,943 101,561 103,867 105,925 109,772 112,978 2000's 115,691 118,561 120,130 131,916 125,042 124,755 126,970 126,324 128,007 127,704 2010's 127,914 128,969 130,139 131,091 131,027 132,392 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Tennessee Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Tennessee Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,206 2,151 2,555 1990's 2,361 2,369 2,425 2,512 2,440 2,393 2,306 2,382 5,149 2,159 2000's 2,386 2,704 2,657 2,755 2,738 2,498 2,545 2,656 2,650 2,717 2010's 2,702 2,729 2,679 2,581 2,595 2,651 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  8. Tennessee Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Tennessee Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 534,882 565,856 599,042 1990's 627,031 661,105 696,140 733,363 768,421 804,724 841,232 867,793 905,757 937,896 2000's 969,537 993,363 1,009,225 1,022,628 1,037,429 1,049,307 1,063,328 1,071,756 1,084,102 1,083,573 2010's 1,085,387 1,089,009 1,084,726 1,094,122 1,106,917 1,124,572 - = No Data

  9. Texas Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Texas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 294,879 284,013 270,227 1990's 268,181 269,411 292,990 297,516 306,376 325,785 329,287 332,077 320,922 314,598 2000's 315,906 314,858 317,446 320,786 322,242 322,999 329,918 326,812 324,671 313,384 2010's 312,277 314,041 314,811 314,036 316,756 319,512 - = No Data Reported; -- = Not Applicable; NA = Not

  10. Texas Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Texas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,852 4,427 13,383 1990's 13,659 13,770 5,481 5,823 5,222 9,043 8,796 5,339 5,318 5,655 2000's 11,613 10,047 9,143 9,015 9,359 9,136 8,664 11,063 5,568 8,581 2010's 8,779 8,713 8,953 8,525 8,398 6,655 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  11. Texas Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Texas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,155,948 3,166,168 3,201,316 1990's 3,232,849 3,274,482 3,285,025 3,346,809 3,350,314 3,446,120 3,501,853 3,543,027 3,600,505 3,613,864 2000's 3,704,501 3,738,260 3,809,370 3,859,647 3,939,101 3,984,481 4,067,508 4,156,991 4,205,412 4,248,613 2010's 4,288,495 4,326,156 4,370,057 4,424,103 4,469,282

  12. Pennsylvania Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 166,901 172,615 178,545 1990's 186,772 191,103 193,863 198,299 206,812 209,245 214,340 215,057 216,519 223,732 2000's 228,037 225,911 226,957 227,708 231,051 233,132 231,540 234,597 233,462 233,334 2010's 233,751 233,588 235,049 237,922 239,681 241,682 - = No Data Reported; -- = Not

  13. Pennsylvania Natural Gas Number of Industrial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Industrial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,089 6,070 6,023 1990's 6,238 6,344 6,496 6,407 6,388 6,328 6,441 6,492 6,736 7,080 2000's 6,330 6,159 5,880 5,577 5,726 5,577 5,241 4,868 4,772 4,745 2010's 4,624 5,007 5,066 5,024 5,084 4,932 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  14. Pennsylvania Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) Pennsylvania Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,237,877 2,271,801 2,291,242 1990's 2,311,795 2,333,377 2,363,575 2,386,249 2,393,053 2,413,715 2,431,909 2,452,524 2,493,639 2,486,704 2000's 2,519,794 2,542,724 2,559,024 2,572,584 2,591,458 2,600,574 2,605,782 2,620,755 2,631,340 2,635,886 2010's 2,646,211 2,667,392 2,678,547

  15. Rhode Island Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) Rhode Island Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,128 16,096 16,924 1990's 17,765 18,430 18,607 21,178 21,208 21,472 21,664 21,862 22,136 22,254 2000's 22,592 22,815 23,364 23,270 22,994 23,082 23,150 23,007 23,010 22,988 2010's 23,049 23,177 23,359 23,742 23,934 24,088 - = No Data Reported; -- = Not Applicable; NA = Not

  16. Rhode Island Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) Rhode Island Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 180,656 185,861 190,796 1990's 195,100 196,438 197,926 198,563 200,959 202,947 204,259 212,777 208,208 211,097 2000's 214,474 216,781 219,769 221,141 223,669 224,320 225,027 223,589 224,103 224,846 2010's 225,204 225,828 228,487 231,763 233,786 236,323 - = No Data Reported; -- =

  17. South Carolina Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) South Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 35,414 37,075 38,856 1990's 39,904 39,999 40,968 42,191 45,487 47,293 48,650 50,817 52,237 53,436 2000's 54,794 55,257 55,608 55,909 56,049 56,974 57,452 57,544 56,317 55,850 2010's 55,853 55,846 55,908 55,997 56,323 56,871 - = No Data Reported; -- = Not Applicable; NA = Not

  18. South Carolina Natural Gas Number of Industrial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Industrial Consumers (Number of Elements) South Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,256 1,273 1,307 1990's 1,384 1,400 1,568 1,625 1,928 1,802 1,759 1,764 1,728 1,768 2000's 1,715 1,702 1,563 1,574 1,528 1,535 1,528 1,472 1,426 1,358 2010's 1,325 1,329 1,435 1,452 1,442 1,438 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. South Carolina Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) South Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 302,321 313,831 327,527 1990's 339,486 344,763 357,818 370,411 416,773 412,259 426,088 443,093 460,141 473,799 2000's 489,340 501,161 508,686 516,362 527,008 541,523 554,953 570,213 561,196 565,774 2010's 570,797 576,594 583,633 593,286 605,644 620,555 - = No Data Reported; -- =

  20. South Dakota Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) South Dakota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,480 12,438 12,771 1990's 13,443 13,692 14,133 16,523 15,539 16,285 16,880 17,432 17,972 18,453 2000's 19,100 19,378 19,794 20,070 20,457 20,771 21,149 21,502 21,819 22,071 2010's 22,267 22,570 22,955 23,214 23,591 24,040 - = No Data Reported; -- = Not Applicable; NA = Not

  1. South Dakota Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) South Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 101,468 102,084 103,538 1990's 105,436 107,846 110,291 128,029 119,544 124,152 127,269 130,307 133,095 136,789 2000's 142,075 144,310 147,356 150,725 148,105 157,457 160,481 163,458 165,694 168,096 2010's 169,838 170,877 173,856 176,204 179,042 182,568 - = No Data Reported; -- =

  2. Louisiana Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Louisiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 67,382 66,472 64,114 1990's 62,770 61,574 61,030 62,055 62,184 62,930 62,101 62,270 63,029 62,911 2000's 62,710 62,241 62,247 63,512 60,580 58,409 57,097 57,127 57,066 58,396 2010's 58,562 58,749 63,381 59,147 58,996 57,873 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  3. Louisiana Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Louisiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,617 1,503 1,531 1990's 1,504 1,469 1,452 1,592 1,737 1,383 1,444 1,406 1,380 1,397 2000's 1,318 1,440 1,357 1,291 1,460 1,086 962 945 988 954 2010's 942 920 963 916 883 845 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  4. Louisiana Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Louisiana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 952,079 946,970 934,472 1990's 934,007 936,423 940,403 941,294 945,387 957,558 945,967 962,786 962,436 961,925 2000's 964,133 952,753 957,048 958,795 940,400 905,857 868,353 879,612 886,084 889,570 2010's 893,400 897,513 963,688 901,635 903,686 888,023 - = No Data Reported; -- = Not Applicable; NA

  5. Maine Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Maine Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,435 3,731 3,986 1990's 4,250 4,455 4,838 4,979 5,297 5,819 6,414 6,606 6,662 6,582 2000's 6,954 6,936 7,375 7,517 7,687 8,178 8,168 8,334 8,491 8,815 2010's 9,084 9,681 10,179 11,415 11,810 11,888 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  6. Maine Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Maine Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,134 11,933 11,902 1990's 12,000 12,424 13,766 13,880 14,104 14,917 14,982 15,221 15,646 15,247 2000's 17,111 17,302 17,921 18,385 18,707 18,633 18,824 18,921 19,571 20,806 2010's 21,142 22,461 23,555 24,765 27,047 31,011 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  7. Maryland Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Maryland Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 51,252 53,045 54,740 1990's 55,576 61,878 62,858 63,767 64,698 66,094 69,991 69,056 67,850 69,301 2000's 70,671 70,691 71,824 72,076 72,809 73,780 74,584 74,856 75,053 75,771 2010's 75,192 75,788 75,799 77,117 77,846 78,138 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  8. Maryland Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Maryland Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,222 5,397 5,570 1990's 5,646 520 514 496 516 481 430 479 1,472 536 2000's 329 795 1,434 1,361 1,354 1,325 1,340 1,333 1,225 1,234 2010's 1,255 1,226 1,163 1,173 1,179 1,169 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  9. Maryland Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Maryland Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 755,294 760,754 767,219 1990's 774,707 782,373 894,677 807,204 824,137 841,772 871,012 890,195 901,455 939,029 2000's 941,384 959,772 978,319 987,863 1,009,455 1,024,955 1,040,941 1,053,948 1,057,521 1,067,807 2010's 1,071,566 1,077,168 1,078,978 1,099,272 1,101,292 1,113,342 - = No Data Reported;

  10. Massachusetts Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) Massachusetts Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 84,636 93,005 92,252 1990's 85,775 88,746 85,873 102,187 92,744 104,453 105,889 107,926 108,832 113,177 2000's 117,993 120,984 122,447 123,006 125,107 120,167 126,713 128,965 242,693 153,826 2010's 144,487 138,225 142,825 144,246 139,556 140,533 - = No Data Reported; -- = Not

  11. Massachusetts Natural Gas Number of Industrial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Industrial Consumers (Number of Elements) Massachusetts Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,626 7,199 13,057 1990's 6,539 5,006 8,723 7,283 8,019 10,447 10,952 11,058 11,245 8,027 2000's 8,794 9,750 9,090 11,272 10,949 12,019 12,456 12,678 36,928 19,208 2010's 12,751 10,721 10,840 11,063 10,946 11,266 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  12. Massachusetts Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) Massachusetts Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,082,777 1,100,635 1,114,920 1990's 1,118,429 1,127,536 1,137,911 1,155,443 1,179,869 1,180,860 1,188,317 1,204,494 1,212,486 1,232,887 2000's 1,278,781 1,283,008 1,295,952 1,324,715 1,306,142 1,297,508 1,348,848 1,361,470 1,236,480 1,370,353 2010's 1,389,592 1,408,314 1,447,947

  13. Michigan Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Michigan Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 178,469 185,961 191,474 1990's 195,766 198,890 201,561 204,453 207,629 211,817 214,843 222,726 224,506 227,159 2000's 230,558 225,109 247,818 246,123 246,991 253,415 254,923 253,139 252,382 252,017 2010's 249,309 249,456 249,994 250,994 253,127 254,484 - = No Data Reported; -- = Not Applicable; NA =

  14. Michigan Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Michigan Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10,885 11,117 11,452 1990's 11,500 11,446 11,460 11,425 11,308 11,454 11,848 12,233 11,888 14,527 2000's 11,384 11,210 10,468 10,378 10,088 10,049 9,885 9,728 10,563 18,186 2010's 9,332 9,088 8,833 8,497 8,156 7,931 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  15. Michigan Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Michigan Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,452,554 2,491,149 2,531,304 1990's 2,573,570 2,609,561 2,640,579 2,677,085 2,717,683 2,767,190 2,812,876 2,859,483 2,903,698 2,949,628 2000's 2,999,737 3,011,205 3,110,743 3,140,021 3,161,370 3,187,583 3,193,920 3,188,152 3,172,623 3,169,026 2010's 3,152,468 3,153,895 3,161,033 3,180,349

  16. Minnesota Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Minnesota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 88,789 90,256 92,916 1990's 95,474 97,388 99,707 93,062 102,857 103,874 105,531 108,686 110,986 114,127 2000's 116,529 119,007 121,751 123,123 125,133 126,310 129,149 128,367 130,847 131,801 2010's 132,163 132,938 134,394 135,557 136,380 138,871 - = No Data Reported; -- = Not Applicable; NA = Not

  17. Minnesota Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Minnesota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,585 2,670 2,638 1990's 2,574 2,486 2,515 2,477 2,592 2,531 2,564 2,233 2,188 2,267 2000's 2,025 1,996 2,029 2,074 2,040 1,432 1,257 1,146 1,131 2,039 2010's 2,106 1,770 1,793 1,870 1,880 1,868 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  18. Minnesota Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Minnesota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 872,148 894,380 911,001 1990's 946,107 970,941 998,201 1,074,631 1,049,263 1,080,009 1,103,709 1,134,019 1,161,423 1,190,190 2000's 1,222,397 1,249,748 1,282,751 1,308,143 1,338,061 1,364,237 1,401,362 1,401,623 1,413,162 1,423,703 2010's 1,429,681 1,436,063 1,445,824 1,459,134 1,472,663 1,496,790

  19. Mississippi Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Mississippi Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 43,362 44,170 44,253 1990's 43,184 43,693 44,313 45,310 43,803 45,444 46,029 47,311 45,345 47,620 2000's 50,913 51,109 50,468 50,928 54,027 54,936 55,741 56,155 55,291 50,713 2010's 50,537 50,636 50,689 50,153 49,911 49,821 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  20. Mississippi Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Mississippi Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,312 1,263 1,282 1990's 1,317 1,314 1,327 1,324 1,313 1,298 1,241 1,199 1,165 1,246 2000's 1,199 1,214 1,083 1,161 996 1,205 1,181 1,346 1,132 1,141 2010's 980 982 936 933 943 930 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  1. Mississippi Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) Mississippi Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 370,094 372,238 376,353 1990's 382,251 386,264 392,155 398,472 405,312 415,123 418,442 423,397 415,673 426,352 2000's 434,501 438,069 435,146 438,861 445,212 445,856 437,669 445,043 443,025 437,715 2010's 436,840 442,479 442,840 445,589 440,252 439,359 - = No Data Reported; -- =

  2. Missouri Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Missouri Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 96,711 97,939 99,721 1990's 105,164 117,675 125,174 125,571 132,378 130,318 133,445 135,553 135,417 133,464 2000's 133,969 135,968 137,924 140,057 141,258 142,148 143,632 142,965 141,529 140,633 2010's 138,670 138,214 144,906 142,495 143,134 141,216 - = No Data Reported; -- = Not Applicable; NA = Not

  3. Missouri Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Missouri Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,832 2,880 3,063 1990's 3,140 3,096 2,989 3,040 3,115 3,033 3,408 3,097 3,151 3,152 2000's 3,094 3,085 2,935 3,115 3,600 3,545 3,548 3,511 3,514 3,573 2010's 3,541 3,307 3,692 3,538 3,497 3,232 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  4. Missouri Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Missouri Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,180,546 1,194,985 1,208,523 1990's 1,213,305 1,211,342 1,220,203 1,225,921 1,281,007 1,259,102 1,275,465 1,293,032 1,307,563 1,311,865 2000's 1,324,282 1,326,160 1,340,726 1,343,614 1,346,773 1,348,743 1,353,892 1,354,173 1,352,015 1,348,781 2010's 1,348,549 1,342,920 1,389,910 1,357,740

  5. Montana Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Montana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 21,382 22,246 22,219 1990's 23,331 23,185 23,610 24,373 25,349 26,329 26,374 27,457 28,065 28,424 2000's 29,215 29,429 30,250 30,814 31,357 31,304 31,817 32,472 33,008 33,731 2010's 34,002 34,305 34,504 34,909 35,205 35,777 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  6. Montana Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Montana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 167,883 171,785 171,156 1990's 174,384 177,726 182,641 188,879 194,357 203,435 205,199 209,806 218,851 222,114 2000's 224,784 226,171 229,015 232,839 236,511 240,554 245,883 247,035 253,122 255,472 2010's 257,322 259,046 259,957 262,122 265,849 269,766 - = No Data Reported; -- = Not Applicable; NA =

  7. Nebraska Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Nebraska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60,707 61,365 60,377 1990's 60,405 60,947 61,319 60,599 62,045 61,275 61,117 51,661 63,819 53,943 2000's 55,194 55,692 56,560 55,999 57,087 57,389 56,548 55,761 58,160 56,454 2010's 56,246 56,553 56,608 58,005 57,191 57,521 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  8. Nebraska Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Nebraska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 675 684 702 1990's 712 718 696 718 766 2,432 2,234 11,553 10,673 10,342 2000's 10,161 10,504 9,156 9,022 8,463 7,973 7,697 7,668 11,627 7,863 2010's 7,912 7,955 8,160 8,495 8,791 8,868 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  9. Nebraska Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Nebraska Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 400,218 403,657 406,723 1990's 407,094 413,354 418,611 413,358 428,201 427,720 439,931 444,970 523,790 460,173 2000's 475,673 476,275 487,332 492,451 497,391 501,279 499,504 494,005 512,013 512,551 2010's 510,776 514,481 515,338 527,397 522,408 525,165 - = No Data Reported; -- = Not Applicable; NA

  10. Nevada Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Nevada Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 18,294 18,921 19,924 1990's 20,694 22,124 22,799 23,207 24,521 25,593 26,613 27,629 29,030 30,521 2000's 31,789 32,782 33,877 34,590 35,792 37,093 38,546 40,128 41,098 41,303 2010's 40,801 40,944 41,192 41,710 42,338 42,860 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  11. Nevada Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Nevada Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,422 219,981 236,237 1990's 256,119 283,307 295,714 305,099 336,353 364,112 393,783 426,221 458,737 490,029 2000's 520,233 550,850 580,319 610,756 648,551 688,058 726,772 750,570 758,315 760,391 2010's 764,435 772,880 782,759 794,150 808,970 824,039 - = No Data Reported; -- = Not Applicable; NA =

  12. Ohio Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Ohio Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,601 219,257 225,347 1990's 233,075 236,519 237,861 240,684 245,190 250,223 259,663 254,991 258,076 266,102 2000's 269,561 269,327 271,160 271,203 272,445 277,767 270,552 272,555 272,899 270,596 2010's 268,346 268,647 267,793 269,081 269,758 269,981 - = No Data Reported; -- = Not Applicable; NA = Not

  13. Ohio Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Ohio Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,929 8,163 8,356 1990's 8,301 8,479 8,573 8,678 8,655 8,650 8,672 7,779 8,112 8,136 2000's 8,267 8,515 8,111 8,098 7,899 8,328 6,929 6,858 6,806 6,712 2010's 6,571 6,482 6,381 6,554 6,526 6,502 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  14. Ohio Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Ohio Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,648,972 2,678,838 2,714,839 1990's 2,766,912 2,801,716 2,826,713 2,867,959 2,921,536 2,967,375 2,994,891 3,041,948 3,050,960 3,111,108 2000's 3,178,840 3,195,584 3,208,466 3,225,908 3,250,068 3,272,307 3,263,062 3,273,791 3,262,716 3,253,184 2010's 3,240,619 3,236,160 3,244,274 3,271,074 3,283,968

  15. Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 87,824 86,666 86,172 1990's 85,790 86,744 87,120 88,181 87,494 88,358 89,852 90,284 89,711 80,986 2000's 80,558 79,045 80,029 79,733 79,512 78,726 78,745 93,991 94,247 94,314 2010's 92,430 93,903 94,537 95,385 96,005 96,471 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  16. Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,772 2,689 2,877 1990's 2,889 2,840 2,859 2,912 2,853 2,845 2,843 2,531 3,295 3,040 2000's 2,821 3,403 3,438 3,367 3,283 2,855 2,811 2,822 2,920 2,618 2010's 2,731 2,733 2,872 2,958 3,062 3,059 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  17. Oklahoma Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Oklahoma Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 809,171 805,107 806,875 1990's 814,296 824,172 832,677 842,130 845,448 856,604 866,531 872,454 877,236 867,922 2000's 859,951 868,314 875,338 876,420 875,271 880,403 879,589 920,616 923,650 924,745 2010's 914,869 922,240 927,346 931,981 937,237 941,137 - = No Data Reported; -- = Not Applicable; NA

  18. Oregon Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Oregon Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 40,967 41,998 43,997 1990's 47,175 55,374 50,251 51,910 53,700 55,409 57,613 60,419 63,085 65,034 2000's 66,893 68,098 69,150 74,515 71,762 73,520 74,683 80,998 76,868 76,893 2010's 77,370 77,822 78,237 79,276 80,480 80,877 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  19. Oregon Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Oregon Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 676 1,034 738 1990's 699 787 740 696 765 791 799 704 695 718 2000's 717 821 842 926 907 1,118 1,060 1,136 1,075 1,051 2010's 1,053 1,066 1,076 1,085 1,099 1,117 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  20. Oregon Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Oregon Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 280,670 288,066 302,156 1990's 326,177 376,166 354,256 371,151 391,845 411,465 433,638 456,960 477,796 502,000 2000's 523,952 542,799 563,744 625,398 595,495 626,685 647,635 664,455 674,421 675,582 2010's 682,737 688,681 693,507 700,211 707,010 717,999 - = No Data Reported; -- = Not Applicable; NA =

  1. Alabama Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Alabama Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 53 54,306 55,400 56,822 1990's 56,903 57,265 58,068 57,827 60,320 60,902 62,064 65,919 76,467 64,185 2000's 66,193 65,794 65,788 65,297 65,223 65,294 66,337 65,879 65,313 67,674 2010's 68,163 67,696 67,252 67,136 67,847 67,746 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  2. Alabama Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Alabama Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,313 2,293 2,380 1990's 2,431 2,523 2,509 2,458 2,477 2,491 2,512 2,496 2,464 2,620 2000's 2,792 2,781 2,730 2,743 2,799 2,787 2,735 2,704 2,757 3,057 2010's 3,039 2,988 3,045 3,143 3,244 3,300 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  3. Alabama Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Alabama Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 656 662,217 668,432 683,528 1990's 686,149 700,195 711,043 730,114 744,394 751,890 766,322 781,711 788,464 775,311 2000's 805,689 807,770 806,389 809,754 806,660 809,454 808,801 796,476 792,236 785,005 2010's 778,985 772,892 767,396 765,957 769,900 768,568 - = No Data Reported; -- = Not Applicable;

  4. Alaska Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Alaska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 11,484 11,649 11,806 1990's 11,921 12,071 12,204 12,359 12,475 12,584 12,732 12,945 13,176 13,409 2000's 13,711 14,002 14,342 14,502 13,999 14,120 14,384 13,408 12,764 13,215 2010's 12,998 13,027 13,133 13,246 13,399 13,549 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  5. Alaska Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Alaska Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 66 67,648 68,612 69,540 1990's 70,808 72,565 74,268 75,842 77,670 79,474 81,348 83,596 86,243 88,924 2000's 91,297 93,896 97,077 100,404 104,360 108,401 112,269 115,500 119,039 120,124 2010's 121,166 121,736 122,983 124,411 126,416 128,605 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Arizona Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Arizona Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 46 46,702 46,636 46,776 1990's 47,292 53,982 47,781 47,678 48,568 49,145 49,693 50,115 51,712 53,022 2000's 54,056 54,724 56,260 56,082 56,186 56,572 57,091 57,169 57,586 57,191 2010's 56,676 56,547 56,532 56,585 56,649 56,793 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  7. Arizona Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Arizona Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 545 567,962 564,195 572,461 1990's 586,866 642,659 604,899 610,337 635,335 661,192 689,597 724,911 764,167 802,469 2000's 846,016 884,789 925,927 957,442 993,885 1,042,662 1,088,574 1,119,266 1,128,264 1,130,047 2010's 1,138,448 1,146,286 1,157,688 1,172,003 1,186,794 1,200,783 - = No Data Reported;

  8. Arkansas Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Arkansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60 60,355 61,630 61,848 1990's 61,530 61,731 62,221 62,952 63,821 65,490 67,293 68,413 69,974 71,389 2000's 72,933 71,875 71,530 71,016 70,655 69,990 69,475 69,495 69,144 69,043 2010's 67,987 67,815 68,765 68,791 69,011 69,265 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  9. Arkansas Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Arkansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 1,410 1,151 1,412 1990's 1,396 1,367 1,319 1,364 1,417 1,366 1,488 1,336 1,300 1,393 2000's 1,414 1,122 1,407 1,269 1,223 1,120 1,120 1,055 1,104 1,025 2010's 1,079 1,133 990 1,020 1,009 1,023 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  10. Arkansas Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Arkansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 475 480,839 485,112 491,110 1990's 488,850 495,148 504,722 513,466 521,176 531,182 539,952 544,460 550,017 554,121 2000's 560,055 552,716 553,192 553,211 554,844 555,861 555,905 557,966 556,746 557,355 2010's 549,970 551,795 549,959 549,764 549,034 550,108 - = No Data Reported; -- = Not Applicable;

  11. California Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) California Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 413 404,507 407,435 410,231 1990's 415,073 421,278 412,467 411,648 411,140 411,535 408,294 406,803 588,224 416,791 2000's 413,003 416,036 420,690 431,795 432,367 434,899 442,052 446,267 447,160 441,806 2010's 439,572 440,990 442,708 444,342 443,115 446,510 - = No Data Reported; -- = Not Applicable;

  12. California Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) California Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31 44,764 44,680 46,243 1990's 46,048 44,865 40,528 42,748 38,750 38,457 36,613 35,830 36,235 36,435 2000's 35,391 34,893 33,725 34,617 41,487 40,226 38,637 39,134 39,591 38,746 2010's 38,006 37,575 37,686 37,996 37,548 36,854 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  13. California Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) California Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,626 7,904,858 8,113,034 8,313,776 1990's 8,497,848 8,634,774 8,680,613 8,726,187 8,790,733 8,865,541 8,969,308 9,060,473 9,181,928 9,331,206 2000's 9,370,797 9,603,122 9,726,642 9,803,311 9,957,412 10,124,433 10,329,224 10,439,220 10,515,162 10,510,950 2010's 10,542,584 10,625,190 10,681,916

  14. Colorado Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Colorado Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 108 109,770 110,769 112,004 1990's 112,661 113,945 114,898 115,924 115,994 118,502 121,221 123,580 125,178 129,041 2000's 131,613 134,393 136,489 138,621 138,543 137,513 139,746 141,420 144,719 145,624 2010's 145,460 145,837 145,960 150,145 150,235 150,545 - = No Data Reported; -- = Not Applicable;

  15. Colorado Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Colorado Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 896 923 976 1990's 1,018 1,074 1,108 1,032 1,176 1,528 2,099 2,923 3,349 4,727 2000's 4,994 4,729 4,337 4,054 4,175 4,318 4,472 4,592 4,816 5,084 2010's 6,232 6,529 6,906 7,293 7,823 8,098 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  16. Colorado Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Colorado Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 925 942,571 955,810 970,512 1990's 983,592 1,002,154 1,022,542 1,044,699 1,073,308 1,108,899 1,147,743 1,183,978 1,223,433 1,265,032 2000's 1,315,619 1,365,413 1,412,923 1,453,974 1,496,876 1,524,813 1,558,911 1,583,945 1,606,602 1,622,434 2010's 1,634,587 1,645,716 1,659,808 1,672,312 1,690,581

  17. Connecticut Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Connecticut Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 38 40,886 41,594 43,703 1990's 45,364 45,925 46,859 45,529 45,042 45,935 47,055 48,195 47,110 49,930 2000's 52,384 49,815 49,383 50,691 50,839 52,572 52,982 52,389 53,903 54,510 2010's 54,842 55,028 55,407 55,500 56,591 57,403 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  18. Connecticut Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Connecticut Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,709 2,818 2,908 1990's 3,061 2,921 2,923 2,952 3,754 3,705 3,435 3,459 3,441 3,465 2000's 3,683 3,881 3,716 3,625 3,470 3,437 3,393 3,317 3,196 3,138 2010's 3,063 3,062 3,148 4,454 4,217 3,945 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  19. Connecticut Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) Connecticut Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 400 411,349 417,831 424,036 1990's 428,912 430,078 432,244 427,761 428,157 431,909 433,778 436,119 438,716 442,457 2000's 458,388 458,404 462,574 466,913 469,332 475,221 478,849 482,902 487,320 489,349 2010's 490,185 494,970 504,138 513,492 522,658 531,380 - = No Data Reported; --

  20. Delaware Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Delaware Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6 6,180 6,566 7,074 1990's 7,485 7,895 8,173 8,409 8,721 9,133 9,518 9,807 10,081 10,441 2000's 9,639 11,075 11,463 11,682 11,921 12,070 12,345 12,576 12,703 12,839 2010's 12,861 12,931 12,997 13,163 13,352 13,430 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  1. Delaware Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Delaware Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 81 82,829 84,328 86,428 1990's 88,894 91,467 94,027 96,914 100,431 103,531 106,548 109,400 112,507 115,961 2000's 117,845 122,829 126,418 129,870 133,197 137,115 141,276 145,010 147,541 149,006 2010's 150,458 152,005 153,307 155,627 158,502 161,607 - = No Data Reported; -- = Not Applicable; NA =

  2. Georgia Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Georgia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 94 98,809 102,277 106,690 1990's 108,295 109,659 111,423 114,889 117,980 120,122 123,200 123,367 126,050 225,020 2000's 128,275 130,373 128,233 129,867 128,923 128,389 127,843 127,832 126,804 127,347 2010's 124,759 123,454 121,243 126,060 122,578 123,307 - = No Data Reported; -- = Not Applicable; NA =

  3. Georgia Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Georgia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 3,034 3,144 3,079 1990's 3,153 3,124 3,186 3,302 3,277 3,261 3,310 3,310 3,262 5,580 2000's 3,294 3,330 3,219 3,326 3,161 3,543 3,053 2,913 2,890 2,254 2010's 2,174 2,184 2,112 2,242 2,481 2,548 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  4. Georgia Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Georgia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,190 1,237,201 1,275,128 1,308,972 1990's 1,334,935 1,363,723 1,396,860 1,430,626 1,460,141 1,495,992 1,538,458 1,553,948 1,659,730 1,732,865 2000's 1,680,749 1,737,850 1,735,063 1,747,017 1,752,346 1,773,121 1,726,239 1,793,650 1,791,256 1,744,934 2010's 1,740,587 1,740,006 1,739,543 1,805,425

  5. Hawaii Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Hawaii Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,896 2,852 2,842 1990's 2,837 2,786 2,793 3,222 2,805 2,825 2,823 2,783 2,761 2,763 2000's 2,768 2,777 2,781 2,804 2,578 2,572 2,548 2,547 2,540 2,535 2010's 2,551 2,560 2,545 2,627 2,789 2,815 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  6. Hawaii Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Hawaii Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 28,502 28,761 28,970 1990's 29,137 29,701 29,805 29,984 30,614 30,492 31,017 30,990 30,918 30,708 2000's 30,751 30,794 30,731 30,473 26,255 26,219 25,982 25,899 25,632 25,466 2010's 25,389 25,305 25,184 26,374 28,919 28,952 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  7. Idaho Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Idaho Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 17,482 18,454 18,813 1990's 19,452 20,328 21,145 21,989 22,999 24,150 25,271 26,436 27,697 28,923 2000's 30,018 30,789 31,547 32,274 33,104 33,362 33,625 33,767 37,320 38,245 2010's 38,506 38,912 39,202 39,722 40,229 40,744 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  8. Idaho Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Idaho Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 104,824 111,532 113,898 1990's 113,954 126,282 136,121 148,582 162,971 175,320 187,756 200,165 213,786 227,807 2000's 240,399 251,004 261,219 274,481 288,380 301,357 316,915 323,114 336,191 342,277 2010's 346,602 350,871 353,963 359,889 367,394 374,557 - = No Data Reported; -- = Not Applicable; NA =

  9. Illinois Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Illinois Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241,367 278,473 252,791 1990's 257,851 261,107 263,988 268,104 262,308 264,756 265,007 268,841 271,585 274,919 2000's 279,179 278,506 279,838 281,877 273,967 276,763 300,606 296,465 298,418 294,226 2010's 291,395 293,213 297,523 282,743 294,391 295,869 - = No Data Reported; -- = Not Applicable; NA =

  10. Illinois Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Illinois Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 19,460 20,015 25,161 1990's 25,991 26,489 27,178 27,807 25,788 25,929 29,493 28,472 28,063 27,605 2000's 27,348 27,421 27,477 26,698 29,187 29,887 26,109 24,000 23,737 23,857 2010's 25,043 23,722 23,390 23,804 23,829 23,049 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  11. Illinois Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Illinois Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,170,364 3,180,199 3,248,117 1990's 3,287,091 3,320,285 3,354,679 3,388,983 3,418,052 3,452,975 3,494,545 3,521,707 3,556,736 3,594,071 2000's 3,631,762 3,670,693 3,688,281 3,702,308 3,754,132 3,975,961 3,812,121 3,845,441 3,869,308 3,839,438 2010's 3,842,206 3,855,942 3,878,806 3,838,120

  12. Indiana Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Indiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 116,571 119,458 122,803 1990's 124,919 128,223 129,973 131,925 134,336 137,162 139,097 140,515 141,307 145,631 2000's 148,411 148,830 150,092 151,586 151,943 159,649 154,322 155,885 157,223 155,615 2010's 156,557 161,293 158,213 158,965 159,596 160,051 - = No Data Reported; -- = Not Applicable; NA =

  13. Indiana Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Indiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,497 5,696 6,196 1990's 6,439 6,393 6,358 6,508 6,314 6,250 6,586 6,920 6,635 19,069 2000's 10,866 9,778 10,139 8,913 5,368 5,823 5,350 5,427 5,294 5,190 2010's 5,145 5,338 5,204 5,178 5,098 5,095 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  14. Indiana Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Indiana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,250,476 1,275,401 1,306,747 1990's 1,327,772 1,358,640 1,377,023 1,402,770 1,438,483 1,463,640 1,489,647 1,509,142 1,531,914 1,570,253 2000's 1,604,456 1,613,373 1,657,640 1,644,715 1,588,738 1,707,195 1,661,186 1,677,857 1,678,158 1,662,663 2010's 1,669,026 1,707,148 1,673,132 1,681,841 1,693,267

  15. Iowa Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Iowa Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 80,797 81,294 82,549 1990's 83,047 84,387 85,325 86,452 86,918 88,585 89,663 90,643 91,300 92,306 2000's 93,836 95,485 96,496 96,712 97,274 97,767 97,823 97,979 98,144 98,416 2010's 98,396 98,541 99,113 99,017 99,186 99,662 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  16. Iowa Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Iowa Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,033 1,937 1,895 1990's 1,883 1,866 1,835 1,903 1,957 1,957 2,066 1,839 1,862 1,797 2000's 1,831 1,830 1,855 1,791 1,746 1,744 1,670 1,651 1,652 1,626 2010's 1,528 1,465 1,469 1,491 1,572 1,572 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Iowa Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Iowa Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 690,532 689,655 701,687 1990's 706,842 716,088 729,081 740,722 750,678 760,848 771,109 780,746 790,162 799,015 2000's 812,323 818,313 824,218 832,230 839,415 850,095 858,915 865,553 872,980 875,781 2010's 879,713 883,733 892,123 895,414 900,420 908,058 - = No Data Reported; -- = Not Applicable; NA =

  18. Kansas Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Kansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 82,934 83,810 85,143 1990's 85,539 86,874 86,840 87,735 86,457 88,163 89,168 85,018 89,654 86,003 2000's 87,007 86,592 87,397 88,030 86,640 85,634 85,686 85,376 84,703 84,715 2010's 84,446 84,874 84,673 84,969 85,654 86,034 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  19. Kansas Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Kansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,440 4,314 4,366 1990's 4,357 3,445 3,296 4,369 3,560 3,079 2,988 7,014 10,706 5,861 2000's 8,833 9,341 9,891 9,295 8,955 8,300 8,152 8,327 8,098 7,793 2010's 7,664 7,954 7,970 7,877 7,328 7,218 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  20. Kansas Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Kansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 725,676 733,101 731,792 1990's 747,081 753,839 762,545 777,658 773,357 797,524 804,213 811,975 841,843 824,803 2000's 833,662 836,486 843,353 850,464 855,272 856,761 862,203 858,304 853,125 855,454 2010's 853,842 854,730 854,800 858,572 860,441 861,419 - = No Data Reported; -- = Not Applicable; NA =

  1. Kentucky Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) Kentucky Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 63,024 63,971 65,041 1990's 67,086 68,461 69,466 71,998 73,562 74,521 76,079 77,693 80,147 80,283 2000's 81,588 81,795 82,757 84,110 84,493 85,243 85,236 85,210 84,985 83,862 2010's 84,707 84,977 85,129 85,999 85,630 85,961 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  2. Kentucky Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) Kentucky Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,391 1,436 1,443 1990's 1,544 1,587 1,608 1,585 1,621 1,630 1,633 1,698 1,864 1,813 2000's 1,801 1,701 1,785 1,695 1,672 1,698 1,658 1,599 1,585 1,715 2010's 1,742 1,705 1,720 1,767 2,008 2,041 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  3. Kentucky Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Residential Consumers (Number of Elements) Kentucky Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 596,320 606,106 614,058 1990's 624,477 633,942 644,281 654,664 668,774 685,481 696,989 713,509 726,960 735,371 2000's 744,816 749,106 756,234 763,290 767,022 770,080 770,171 771,047 753,531 754,761 2010's 758,129 759,584 757,790 761,575 761,935 764,946 - = No Data Reported; -- = Not Applicable; NA

  4. Illinois Natural Gas Number of Oil Wells (Number of Elements)

    Gasoline and Diesel Fuel Update

    Commercial Consumers (Number of Elements) Illinois Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241,367 278,473 252,791 1990's 257,851 261,107 263,988 268,104 262,308 264,756 265,007 268,841 271,585 274,919 2000's 279,179 278,506 279,838 281,877 273,967 276,763 300,606 296,465 298,418 294,226 2010's 291,395 293,213 297,523 282,743 294,391 295,869 - = No Data Reported; -- = Not Applicable; NA =

  5. UGE Scheduler Cycle Time

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    UGE Scheduler Cycle Time UGE Scheduler Cycle Time Genepool Cycle Time Genepool Scheduler Cycle Time Genepool Jobs Dispatched / Hour What is the Scheduler Cycle? The Univa Grid Engine Scheduler cycle performs a number of important tasks, including: Prioritizing Jobs Reserving Resources for jobs requesting more resources (slots / memory) Dispatching jobs or tasks to the compute nodes Evaluating job dependencies The "cycle time" is the length of time it takes the scheduler to complete all

  6. Code System to Generate Latin Hypercube and Random Samples.

    Energy Science and Technology Software Center

    1999-02-25

    Version: 00 LHS was written for the generation of multi variate samples either completely at random or by a constrained randomization termed Latin hypercube sampling (LHS). The generation of these samples is based on user-specified parameters which dictate the characteristics of the generated samples, such as type of sample (LHS or random), sample size, number of samples desired, correlation structure on input variables, and type of distribution specified on each variable. The following distributions aremore » built into the program: normal, lognormal, uniform, loguniform, triangular, and beta. In addition, the samples from the uniform and loguniform distributions may be modified by changing the frequency of the sampling within subintervals, and a subroutine which can be modified by the user to generate samples from other distributions (including empirical data) is provided.« less

  7. The deterministic chaos and random noise in turbulent jet

    SciTech Connect

    Yao, Tian-Liang; Liu, Hai-Feng Xu, Jian-Liang; Li, Wei-Feng

    2014-06-01

    A turbulent flow is usually treated as a superposition of coherent structure and incoherent turbulence. In this paper, the largest Lyapunov exponent and the random noise in the near field of round jet and plane jet are estimated with our previously proposed method of chaotic time series analysis [T. L. Yao, et al., Chaos 22, 033102 (2012)]. The results show that the largest Lyapunov exponents of the round jet and plane jet are in direct proportion to the reciprocal of the integral time scale of turbulence, which is in accordance with the results of the dimensional analysis, and the proportionality coefficients are equal. In addition, the random noise of the round jet and plane jet has the same linear relation with the Kolmogorov velocity scale of turbulence. As a result, the random noise may well be from the incoherent disturbance in turbulence, and the coherent structure in turbulence may well follow the rule of chaotic motion.

  8. Global temperature deviations as a random walk

    SciTech Connect

    Karner, O.

    1996-12-31

    Surface air temperature is the main parameter to represent the earth`s contemporary climate. Several historical temperature records on a global/monthly basis are available. Time-series analysis shows that they can be modelled via autoregressive moving average models closely connected to the classical random walk model. Fitted models emphasize a nonstationary character of the global/monthly temperature deviation from a certain level. The nonstationarity explains all trends and periods, found in the last century`s variability of global mean temperature. This means that the short-term temperature trends are inevitable and may have little in common with a currently increasing carbon dioxide amount. The calculations show that a reasonable understanding of the contemporary global mean climate is attainable, assuming random forcing to the climate system and treating temperature deviation as a response to it. The forcings occur due to volcanic eruptions, redistribution of cloudiness, variations in snow and ice covered areas, changes in solar output, etc. Their impact can not be directly estimated from changes of the earth`s radiation budget at the top of the atmosphere, because actual measurements represent mixture of the forcings and responses. Thus, it is impossible empirically to separate the impact of one particular forcing (e.g., that due to increase of CO{sub 2} amount) from the sequence of all existing forcings in the earth climate system. More accurate modelling involving main feedback loops is necessary to ease such a separation.

  9. Verification Challenges at Low Numbers

    SciTech Connect

    Benz, Jacob M.; Booker, Paul M.; McDonald, Benjamin S.

    2013-06-01

    Many papers have dealt with the political difficulties and ramifications of deep nuclear arms reductions, and the issues of “Going to Zero”. Political issues include extended deterrence, conventional weapons, ballistic missile defense, and regional and geo-political security issues. At each step on the road to low numbers, the verification required to ensure compliance of all parties will increase significantly. Looking post New START, the next step will likely include warhead limits in the neighborhood of 1000 . Further reductions will include stepping stones at1000 warheads, 100’s of warheads, and then 10’s of warheads before final elimination could be considered of the last few remaining warheads and weapons. This paper will focus on these three threshold reduction levels, 1000, 100’s, 10’s. For each, the issues and challenges will be discussed, potential solutions will be identified, and the verification technologies and chain of custody measures that address these solutions will be surveyed. It is important to note that many of the issues that need to be addressed have no current solution. In these cases, the paper will explore new or novel technologies that could be applied. These technologies will draw from the research and development that is ongoing throughout the national laboratory complex, and will look at technologies utilized in other areas of industry for their application to arms control verification.

  10. Detection probabilities for random inspection in variable flow situations

    SciTech Connect

    Lu, Ming-Shih

    1994-03-01

    Improvements in the efficiency and effectiveness of inventory-change verification are necessary at certain nuclear facilities, of which one example is low-enriched uranium fuel fabrication facilities. The Safeguards Criteria suggested carrying out interim inventory-change verifications with randomized inspections. This paper describes randomized inspection schemes for inventory change verifications and evaluates the achievable detection probabilities for realistic plant receipt and shipment schedules and stratum residence times as a. function of the inspection frequency and effort and compares these with the existing inspection strategies.

  11. Developing and Enhancing Workforce Training Programs: Number...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Developing and Enhancing Workforce Training Programs: Number of Projects by State Developing and Enhancing Workforce Training Programs: Number of Projects by State Map of the ...

  12. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

    SciTech Connect

    Quan, Wei; Lv, Lin Liu, Baiqi

    2014-11-15

    In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

  13. Task Time Tracker

    Energy Science and Technology Software Center

    2013-07-24

    This client-side web app tracks the amount of time spent on arbitrary tasks. It allosw the creation of an unlimited number of arbitrarily named tasks ans via simple interactions, tracks the amount of time spent working on the drfined tasks.

  14. Florida Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Gasoline and Diesel Fuel Update

    Gas and Gas Condensate Wells (Number of Elements) Florida Natural Gas Number of Gas and ...2016 Referring Pages: Number of Producing Gas Wells (Summary) Florida Natural Gas Summary

  15. Low Mach Number Models in Computational Astrophysics

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Ann Almgren Low Mach Number Models in Computational Astrophysics February 4, 2014 Ann Almgren. Berkeley Lab Downloads Almgren-nug2014.pdf | Adobe Acrobat PDF file Low Mach Number...

  16. Climate Zone Number 5 | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Climate Zone Number 5 Jump to: navigation, search A type of climate defined in the ASHRAE 169-2006 standard. Climate Zone Number 5 is defined as Cool- Humid(5A) with IP Units 5400...

  17. Amplitude- and rise-time-compensated filters

    DOEpatents

    Nowlin, Charles H.

    1984-01-01

    An amplitude-compensated rise-time-compensated filter for a pulse time-of-occurrence (TOOC) measurement system is disclosed. The filter converts an input pulse, having the characteristics of random amplitudes and random, non-zero rise times, to a bipolar output pulse wherein the output pulse has a zero-crossing time that is independent of the rise time and amplitude of the input pulse. The filter differentiates the input pulse, along the linear leading edge of the input pulse, and subtracts therefrom a pulse fractionally proportional to the input pulse. The filter of the present invention can use discrete circuit components and avoids the use of delay lines.

  18. Alternating current response of carbon nanotubes with randomly distributed impurities

    SciTech Connect

    Hirai, Daisuke; Watanabe, Satoshi [Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); Yamamoto, Takahiro [Department of Electrical Engineering and Department of Liberal Arts (Physics), Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo 125-8585 (Japan)

    2014-10-27

    The increasing need for nanodevices has necessitated a better understanding of the electronic transport behavior of nanomaterials. We therefore theoretically examine the AC transport properties of metallic carbon nanotubes with randomly distributed impurities. We find that the long-range impurity scattering increases the emittance, but does not affect the DC conductance. The estimated dwell time of electrons increases with the potential amplitudes. That is, multiple scattering by the impurities increases the kinetic inductance in proportion to the dwell time, which eventually increases the emittance. We believe that our findings can contribute significantly to nanodevice development.

  19. Real-Time Benchmark Suite

    Energy Science and Technology Software Center

    1992-01-17

    This software provides a portable benchmark suite for real time kernels. It tests the performance of many of the system calls, as well as the interrupt response time and task response time to interrupts. These numbers provide a baseline for comparing various real-time kernels and hardware platforms.

  20. Bayati Kim Saberi random graph sampler

    Energy Science and Technology Software Center

    2012-06-05

    This software package implements the algorithm from a paper by Bayati, Kim, and Saberi (first reference below) to generate a uniformly random sample of a graph with a prescribed degree distribution.

  1. On the binary expansions of algebraic numbers

    SciTech Connect

    Bailey, David H.; Borwein, Jonathan M.; Crandall, Richard E.; Pomerance, Carl

    2003-07-01

    Employing concepts from additive number theory, together with results on binary evaluations and partial series, we establish bounds on the density of 1's in the binary expansions of real algebraic numbers. A central result is that if a real y has algebraic degree D > 1, then the number {number_sign}(|y|, N) of 1-bits in the expansion of |y| through bit position N satisfies {number_sign}(|y|, N) > CN{sup 1/D} for a positive number C (depending on y) and sufficiently large N. This in itself establishes the transcendency of a class of reals {summation}{sub n{ge}0} 1/2{sup f(n)} where the integer-valued function f grows sufficiently fast; say, faster than any fixed power of n. By these methods we re-establish the transcendency of the Kempner--Mahler number {summation}{sub n{ge}0}1/2{sup 2{sup n}}, yet we can also handle numbers with a substantially denser occurrence of 1's. Though the number z = {summation}{sub n{ge}0}1/2{sup n{sup 2}} has too high a 1's density for application of our central result, we are able to invoke some rather intricate number-theoretical analysis and extended computations to reveal aspects of the binary structure of z{sup 2}.

  2. Quasi-random array imaging collimator

    DOEpatents

    Fenimore, E.E.

    1980-08-20

    A hexagonally shaped quasi-random no-two-holes-touching imaging collimator. The quasi-random array imaging collimator eliminates contamination from small angle off-axis rays by using a no-two-holes-touching pattern which simultaneously provides for a self-supporting array increasing throughput by elimination of a substrate. The present invention also provides maximum throughput using hexagonally shaped holes in a hexagonal lattice pattern for diffraction limited applications. Mosaicking is also disclosed for reducing fabrication effort.

  3. COLLOQUIUM: Random Organization, Hyperuniformity and Photonic Bandgaps |

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Princeton Plasma Physics Lab September 28, 2016, 4:15pm to 5:30pm Colloquia MBG Auditorium COLLOQUIUM: Random Organization, Hyperuniformity and Photonic Bandgaps Dr. Paul Chaikin New York University A periodically sheared suspension undergoes collisions which allow the particles to explore new configurations. Below a critical strain the system evolves and organizes itself until collisions no longer occur - an absorbing state by "Random Organization." Recent work shows that at the

  4. Utah Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Energy Information Administration (EIA) (indexed site)

    Gas and Gas Condensate Wells (Number of Elements) Utah Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  5. Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Energy Information Administration (EIA) (indexed site)

    Gas and Gas Condensate Wells (Number of Elements) Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  6. ARM - Measurement - Cloud particle number concentration

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    number concentration ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Cloud particle number concentration The total number of cloud particles present in any given volume of air. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available

  7. Particle Number & Particulate Mass Emissions Measurements on...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Heavy-duty Engine using the PMP Methodologies Particle Number & Particulate Mass Emissions Measurements on a 'Euro VI' Heavy-duty Engine using the PMP Methodologies Poster ...

  8. Calculating Atomic Number Densities for Uranium

    Energy Science and Technology Software Center

    1993-01-01

    Provides method to calculate atomic number densities of selected uranium compounds and hydrogenous moderators for use in nuclear criticality safety analyses at gaseous diffusion uranium enrichment facilities.

  9. Identification of Export Control Classification Number - ITER

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" ... be shipped from the United States to the ITER International Organization in Cadarache, ...

  10. TIMING APPARATUS

    DOEpatents

    Bennett, A.E.; Geisow, J.C.H.

    1956-04-17

    The timing device comprises an escapement wheel and pallet, a spring drive to rotate the escapement wheel to a zero position, means to wind the pretensioned spring proportional to the desired signal time, and a cam mechanism to control an electrical signal switch by energizing the switch when the spring has been wound to the desired position, and deenergizing it when it reaches the zero position. This device produces an accurately timed signal variably witain the control of the operator.

  11. CHANGING TIMES

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    District Number of Sub-agreements District MWs Fort Worth 8 89 14,776,000 Kansas City 14 205 11,440,570 Little Rock 46 1,069 51,810,300 St. Louis 11 58 3,552,000 Tulsa...

  12. Geometrical effects on the electron residence time in semiconductor nano-particles

    SciTech Connect

    Koochi, Hakimeh; Ebrahimi, Fatemeh

    2014-09-07

    We have used random walk (RW) numerical simulations to investigate the influence of the geometry on the statistics of the electron residence time τ{sub r} in a trap-limited diffusion process through semiconductor nano-particles. This is an important parameter in coarse-grained modeling of charge carrier transport in nano-structured semiconductor films. The traps have been distributed randomly on the surface (r{sup 2} model) or through the whole particle (r{sup 3} model) with a specified density. The trap energies have been taken from an exponential distribution and the traps release time is assumed to be a stochastic variable. We have carried out (RW) simulations to study the effect of coordination number, the spatial arrangement of the neighbors and the size of nano-particles on the statistics of τ{sub r}. It has been observed that by increasing the coordination number n, the average value of electron residence time, τ{sup ¯}{sub r} rapidly decreases to an asymptotic value. For a fixed coordination number n, the electron's mean residence time does not depend on the neighbors' spatial arrangement. In other words, τ{sup ¯}{sub r} is a porosity-dependence, local parameter which generally varies remarkably from site to site, unless we are dealing with highly ordered structures. We have also examined the effect of nano-particle size d on the statistical behavior of τ{sup ¯}{sub r}. Our simulations indicate that for volume distribution of traps, τ{sup ¯}{sub r} scales as d{sup 2}. For a surface distribution of traps τ{sup ¯}{sub r} increases almost linearly with d. This leads to the prediction of a linear dependence of the diffusion coefficient D on the particle size d in ordered structures or random structures above the critical concentration which is in accordance with experimental observations.

  13. Battling bird flu by the numbers

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    and crisis managers determine in real time whether an emerging infectious disease ... and crisis managers determine in real time whether an emerging infectious disease ...

  14. Stabilizing Topological Phases in Graphene via Random Adsorption...

    Office of Scientific and Technical Information (OSTI)

    Stabilizing Topological Phases in Graphene via Random Adsorption Title: Stabilizing Topological Phases in Graphene via Random Adsorption Authors: Jiang, Hua ; Qiao, Zhenhua ; Liu, ...

  15. Listening to the noise: random fluctuations reveal gene network...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Listening to the noise: random fluctuations reveal gene network parameters Citation Details In-Document Search Title: Listening to the noise: random fluctuations ...

  16. Application of Random Vibration Theory Methodology for Seismic...

    Energy Saver

    Application of Random Vibration Theory Methodology for Seismic Soil-Structure Interaction Analysis Application of Random Vibration Theory Methodology for Seismic Soil-Structure...

  17. Chiral random matrix model at finite chemical potential: Characteristi...

    Office of Scientific and Technical Information (OSTI)

    Chiral random matrix model at finite chemical potential: Characteristic determinant and edge universality Prev Next Title: Chiral random matrix model at finite chemical ...

  18. Quasiparticle random-phase approximation with interactions from...

    Office of Scientific and Technical Information (OSTI)

    Quasiparticle random-phase approximation with interactions from the Similarity Renormalization Group Citation Details In-Document Search Title: Quasiparticle random-phase ...

  19. Adaptive low Mach number simulations of nuclear flame microphysics

    SciTech Connect

    Bell, J.B.; Day, M.S.; Rendleman, C.A.; Woosley, S.E.; Zingale, M.A.

    2003-03-20

    We introduce a numerical model for the simulation of nuclear flames in Type Ia supernovae. This model is based on a low Mach number formulation that analytically removes acoustic wave propagation while retaining the compressibility effects resulting from nuclear burning. The formulation presented here generalizes low Mach number models used in combustion that are based on an ideal gas approximation to the arbitrary equations of state such as those describing the degenerate matter found in stellar material. The low Mach number formulation permits time steps that are controlled by the advective time scales resulting in a substantial improvement in computational efficiency compared to a compressible formulation. We briefly discuss the basic discretization methodology for the low Mach number equations and their implementation in an adaptive projection framework. We present validation computations in which the computational results from the low Mach number model are compared to a compressible code and present an application of the methodology to the Landau-Darrieus instability of a carbon flame.

  20. Time Off

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Time Off Time Off A comprehensive benefits package with plan options for health care and retirement to take care of our employees today and tomorrow. thumbnail of Benefits Office CONTACT US Benefits Office (505) 667-1806 Email Create time away from work Work schedules A variety of work schedules are available that allow flexibility for workers and Laboratory programs. The most popular work schedule is the "9/80"-employees work 80 hours over a nine-workday (two-week) period, with a

  1. Low Mach Number Modeling of Type Ia Supernovae

    SciTech Connect

    Almgren, Ann S.; Bell, John B.; Rendleman, Charles A.; Zingale,Michael

    2005-08-05

    We introduce a low Mach number equation set for the large-scale numerical simulation of carbon-oxygen white dwarfs experiencing a thermonuclear deflagration. Since most of the interesting physics in a Type Ia supernova transpires at Mach numbers from 0.01 to 0.1, such an approach enables both a considerable increase in accuracy and savings in computer time compared with frequently used compressible codes. Our equation set is derived from the fully compressible equations using low Mach number asymptotics, but without any restriction on the size of perturbations in density or temperature. Comparisons with simulations that use the fully compressible equations validate the low Mach number model in regimes where both are applicable. Comparisons to simulations based on the more traditional an elastic approximation also demonstrate the agreement of these models in the regime for which the anelastic approximation is valid. For low Mach number flows with potentially finite amplitude variations in density and temperature, the low Mach number model overcomes the limitations of each of the more traditional models and can serve as the basis for an accurate and efficient simulation tool.

  2. Performance of wireless sensor networks under random node failures

    SciTech Connect

    Bradonjic, Milan; Hagberg, Aric; Feng, Pan

    2011-01-28

    Networks are essential to the function of a modern society and the consequence of damages to a network can be large. Assessing network performance of a damaged network is an important step in network recovery and network design. Connectivity, distance between nodes, and alternative routes are some of the key indicators to network performance. In this paper, random geometric graph (RGG) is used with two types of node failure, uniform failure and localized failure. Since the network performance are multi-facet and assessment can be time constrained, we introduce four measures, which can be computed in polynomial time, to estimate performance of damaged RGG. Simulation experiments are conducted to investigate the deterioration of networks through a period of time. With the empirical results, the performance measures are analyzed and compared to provide understanding of different failure scenarios in a RGG.

  3. Mo Year Report Period: EIA ID NUMBER:

    Energy Information Administration (EIA) (indexed site)

    Mo Year Report Period: EIA ID NUMBER: http:www.eia.govsurveyformeia14instructions.pdf Mailing Address: Secure File Transfer option available at: (e.g., PO Box, RR) https:...

  4. Random paths and current fluctuations in nonequilibrium statistical mechanics

    SciTech Connect

    Gaspard, Pierre

    2014-07-15

    An overview is given of recent advances in nonequilibrium statistical mechanics about the statistics of random paths and current fluctuations. Although statistics is carried out in space for equilibrium statistical mechanics, statistics is considered in time or spacetime for nonequilibrium systems. In this approach, relationships have been established between nonequilibrium properties such as the transport coefficients, the thermodynamic entropy production, or the affinities, and quantities characterizing the microscopic Hamiltonian dynamics and the chaos or fluctuations it may generate. This overview presents results for classical systems in the escape-rate formalism, stochastic processes, and open quantum systems.

  5. Identification of Export Control Classification Number - ITER

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for the products (equipment, components and/or materials) and if applicable the nonproprietary associated installation/maintenance documentation that will be shipped from the United States to the ITER International Organization in Cadarache, France or to ITER Members worldwide on behalf of the Company. In rare instances an

  6. Time Card Entry System

    SciTech Connect

    Montierth, B. S.

    1996-05-07

    The Time Card Entry System was developed for the Department of Enegy, Idaho Operations Office (DOE-ID) to interface with the DOE headquarters (DOE-HQ) Electronic Time and Attendance (ETA) system for payroll. It features pop-up window pick lists for Work Breakdown Structure numbers and Hour Codes and has extensive processing that ensures that time and attendance reported by the employee fulfills U.S. Government/OMB requirements before Timekeepers process the data at the end of the two week payroll cycle using ETA. A tour of duty profile (e.g., ten hour day, four day week with Sunday, friday and Saturday off), previously established in the ETA system, is imported into the Time Card Entry System by the timekeepers. An individual''s profile establishes the basis for validation of time of day and number of hours worked per day. At the end of the two cycle, data is exported by the timekeepers from the Time Card Entry System into ETA files.

  7. Time Card Entry System

    Energy Science and Technology Software Center

    1996-05-07

    The Time Card Entry System was developed for the Department of Enegy, Idaho Operations Office (DOE-ID) to interface with the DOE headquarters (DOE-HQ) Electronic Time and Attendance (ETA) system for payroll. It features pop-up window pick lists for Work Breakdown Structure numbers and Hour Codes and has extensive processing that ensures that time and attendance reported by the employee fulfills U.S. Government/OMB requirements before Timekeepers process the data at the end of the two weekmore » payroll cycle using ETA. A tour of duty profile (e.g., ten hour day, four day week with Sunday, friday and Saturday off), previously established in the ETA system, is imported into the Time Card Entry System by the timekeepers. An individual''s profile establishes the basis for validation of time of day and number of hours worked per day. At the end of the two cycle, data is exported by the timekeepers from the Time Card Entry System into ETA files.« less

  8. USE OF MAILBOX APPROACH, VIDEO SURVEILLANCE, AND SHORT-NOTICE RANDOM INSPECTIONS TO ENHANCE DETECTION OF UNDECLARED LEU PRODUCTION AT GAS CENTRIFUGE ENRICHMENT PLANTS.

    SciTech Connect

    BOYER, B.D.; GORDON, D.M.; JO, J.

    2006-07-16

    Current safeguards approaches used by the IAEA at gas centrifuge enrichment plants (GCEPs) need enhancement in order to detect undeclared LEU production with adequate detection probability. ''Mailbox'' declarations have been used in the last two decades to verify receipts, production, and shipments at some bulk-handling facilities (e.g., fuel-fabrication plants). The operator declares the status of his plant to the IAEA on a daily basis using a secure ''Mailbox'' system such as a secure tamper-resistant computer. The operator agrees to hold receipts and shipments for a specified period of time, along with a specified number of annual inspections, to enable inspector access to a statistically large enough population of UF{sub 6} cylinders and fuel assemblies to achieve the desired detection probability. The inspectors can access the ''Mailbox'' during randomly timed inspections and then verify the operator's declarations for that day. Previously, this type of inspection regime was considered mainly for verifying the material balance at fuel-fabrication, enrichment, and conversion plants. Brookhaven National Laboratory has expanded the ''Mailbox'' concept with short-notice random inspections (SNRIs), coupled with enhanced video surveillance, to include declaration and verification of UF{sub 6} cylinder operational data to detect activities associated with undeclared LEU production at GCEPs. Since the ''Mailbox'' declarations would also include data relevant to material-balance verification, these randomized inspections would replace the scheduled monthly interim inspections for material-balance purposes; in addition, the inspectors could simultaneously perform the required number of Limited-Frequency Unannounced Access (LFUA) inspections used for HEU detection. This approach would provide improved detection capabilities for a wider range of diversion activities with not much more inspection effort than at present.

  9. Random matrix models for chiral and diquark condensation

    SciTech Connect

    Vanderheyden, B.; Jackson, A.D.

    2005-06-14

    We consider random matrix models for the thermodynamic competition between chiral symmetry breaking and diquark condensation in QCD at finite temperature and finite baryon density. The models produce mean field phase diagrams whose topology depends solely on the global symmetries of the theory. We discuss the block structure of the interactions that is imposed by chiral, spin, and color degrees of freedom and comment on the treatment of density and temperature effects. Extension of the coupling parameters to a larger class of theories allows us to investigate the robustness of the phase topology with respect to variations in the dynamics of the interactions. We briefly study the phase structure as a function of coupling parameters and the number of colors.

  10. WIPP Documents - All documents by number

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Note: Documents that do not have document numbers are not included in this listing. Large file size alert This symbol means the document may be a large file size. All documents by number Common document prefixes DOE/CAO DOE/TRU DOE/CBFO DOE/WIPP DOE/EA NM DOE/EIS Other DOE/CAO Back to top DOE/CAO 95-1095, Oct. 1995 Remote Handled Transuranic Waste Study This study was conducted to satisfy the requirements defined by the WIPP Land Withdrawal Act and considered by DOE to be a prudent exercise in

  11. Approximate resolution of hard numbering problems

    SciTech Connect

    Bailleux, O.; Chabrier, J.J.

    1996-12-31

    We present a new method for estimating the number of solutions of constraint satisfaction problems. We use a stochastic forward checking algorithm for drawing a sample of paths from a search tree. With this sample, we compute two values related to the number of solutions of a CSP instance. First, an unbiased estimate, second, a lower bound with an arbitrary low error probability. We will describe applications to the Boolean Satisfiability problem and the Queens problem. We shall give some experimental results for these problems.

  12. Probing lepton number violation on three frontiers

    SciTech Connect

    Deppisch, Frank F. [Department of Physics and Astronomy, University College London (United Kingdom)

    2013-12-30

    Neutrinoless double beta decay constitutes the main probe for lepton number violation at low energies, motivated by the expected Majorana nature of the light but massive neutrinos. On the other hand, the theoretical interpretation of the (non-)observation of this process is not straightforward as the Majorana neutrinos can destructively interfere in their contribution and many other New Physics mechanisms can additionally mediate the process. We here highlight the potential of combining neutrinoless double beta decay with searches for Tritium decay, cosmological observations and LHC physics to improve the quantitative insight into the neutrino properties and to unravel potential sources of lepton number violation.

  13. U.S. Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) U.S. Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 181,241 195,869 203,990 215,815 215,867 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) U.S. Natural

  14. South Dakota Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) South Dakota Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 72 69 74 68 65 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) South Dakota Natural Gas

  15. New Mexico Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) New Mexico Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 12,887 13,791 14,171 14,814 14,580 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) New Mexico

  16. New York Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) New York Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 988 1,170 1,589 1,731 1,697 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) New York Natural Gas

  17. North Dakota Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) North Dakota Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 5,561 7,379 9,363 11,532 12,799 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) North Dakota

  18. West Virginia Natural Gas Number of Oil Wells (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Oil Wells (Number of Elements) West Virginia Natural Gas Number of Oil Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's 2,373 2,509 2,675 2,606 2,244 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Gas Producing Oil Wells Number of Gas Producing Oil Wells (Summary) West Virginia

  19. The 17 GHz active region number

    SciTech Connect

    Selhorst, C. L.; Pacini, A. A.; Costa, J. E. R.; Gimnez de Castro, C. G.; Valio, A.; Shibasaki, K.

    2014-08-01

    We report the statistics of the number of active regions (NAR) observed at 17 GHz with the Nobeyama Radioheliograph between 1992, near the maximum of cycle 22, and 2013, which also includes the maximum of cycle 24, and we compare with other activity indexes. We find that NAR minima are shorter than those of the sunspot number (SSN) and radio flux at 10.7 cm (F10.7). This shorter NAR minima could reflect the presence of active regions generated by faint magnetic fields or spotless regions, which were a considerable fraction of the counted active regions. The ratio between the solar radio indexes F10.7/NAR shows a similar reduction during the two minima analyzed, which contrasts with the increase of the ratio of both radio indexes in relation to the SSN during the minimum of cycle 23-24. These results indicate that the radio indexes are more sensitive to weaker magnetic fields than those necessary to form sunspots, of the order of 1500 G. The analysis of the monthly averages of the active region brightness temperatures shows that its long-term variation mimics the solar cycle; however, due to the gyro-resonance emission, a great number of intense spikes are observed in the maximum temperature study. The decrease in the number of these spikes is also evident during the current cycle 24, a consequence of the sunspot magnetic field weakening in the last few years.

  20. Climate Zone Number 1 | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Zone Number 1 is defined as Very Hot - Humid(1A) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C Dry(1B) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C...

  1. Nevada Number of Natural Gas Consumers

    Annual Energy Outlook

    760,391 764,435 772,880 782,759 794,150 808,970 1987-2014 Sales 764,435 772,880 782,759 794,150 808,970 1997-2014 Commercial Number of Consumers 41,303 40,801 40,944 41,192 41,710 ...

  2. Washington Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update

    059,239 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1987-2014 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1997-2014 Commercial Number of Consumers 98,965 99,231...

  3. North Carolina Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update

    ,102,001 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1987-2014 Sales 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1997-2014 Commercial Number of Consumers 113,630...

  4. Pennsylvania Number of Natural Gas Consumers

    Annual Energy Outlook

    1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 618 606 604 540 627 666 1967-2014 Industrial Number of Consumers 4,745 4,624 5,007 5,066 5,024 5,084 1987-2014...

  5. The New Element Curium (Atomic Number 96)

    DOE R&D Accomplishments

    Seaborg, G. T.; James, R. A.; Ghiorso, A.

    1948-00-00

    Two isotopes of the element with atomic number 96 have been produced by the helium-ion bombardment of plutonium. The name curium, symbol Cm, is proposed for element 96. The chemical experiments indicate that the most stable oxidation state of curium is the III state.

  6. Oklahoma Number of Natural Gas Consumers

    Annual Energy Outlook

    924,745 914,869 922,240 927,346 931,981 937,237 1987-2014 Sales 914,869 922,240 927,346 931,981 937,237 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers ...

  7. New Mexico Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update

    560,479 559,852 570,637 561,713 572,224 614,313 1987-2014 Sales 559,825 570,592 561,652 572,146 614,231 1997-2014 Transported 27 45 61 78 82 1997-2014 Commercial Number of...

  8. Kansas Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update

    855,454 853,842 854,730 854,800 858,572 861,092 1987-2014 Sales 853,842 854,730 854,779 858,546 861,066 1997-2014 Transported 0 0 21 26 26 2004-2014 Commercial Number of Consumers...

  9. New Hampshire Number of Natural Gas Consumers

    Annual Energy Outlook

    96,924 95,361 97,400 99,738 98,715 99,146 1987-2014 Sales 95,360 97,400 99,738 98,715 99,146 1997-2014 Transported 1 0 0 0 0 2010-2014 Commercial Number of Consumers 16,937 16,645 ...

  10. Minnesota Number of Natural Gas Consumers

    Annual Energy Outlook

    423,703 1,429,681 1,436,063 1,445,824 1,459,134 1,472,663 1987-2014 Sales 1,429,681 1,436,063 1,445,824 1,459,134 1,472,663 1997-2014 Commercial Number of Consumers 131,801 132,163 ...

  11. Distribution of occupation numbers in finite Fermi systems and role of interaction in chaos and thermalization

    SciTech Connect

    Flambaum, V.V.; Izrailev, F.M. [School of Physics, University of New South Wales, Sydney 2052 (Australia)] [School of Physics, University of New South Wales, Sydney 2052 (Australia)

    1997-01-01

    A method is developed for calculation of single-particle occupation numbers in finite Fermi systems of interacting particles. It is more accurate than the canonical distribution method and gives the Fermi-Dirac distribution in the limit of large number of particles. It is shown that statistical effects of the interaction are absorbed by an increase of the effective temperature. Criteria for quantum chaos and statistical equilibrium are considered. All results are confirmed by numerical experiments in the two-body random interaction model. {copyright} {ital 1997} {ital The American Physical Society}

  12. Energy By The Numbers: An Energy Revolution | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy By The Numbers: An Energy Revolution Energy By The Numbers: An Energy Revolution

  13. Resilience of complex networks to random breakdown

    SciTech Connect

    Paul, Gerald; Sreenivasan, Sameet; Stanley, H. Eugene

    2005-11-01

    Using Monte Carlo simulations we calculate f{sub c}, the fraction of nodes that are randomly removed before global connectivity is lost, for networks with scale-free and bimodal degree distributions. Our results differ from the results predicted by an equation for f{sub c} proposed by Cohen et al. We discuss the reasons for this disagreement and clarify the domain for which the proposed equation is valid.

  14. Local volume fraction fluctuations in random media

    SciTech Connect

    Quintanilla, J.; Torquato, S.

    1997-02-01

    Although the volume fraction is a constant for a statistically homogeneous random medium, on a spatially local level it fluctuates. We study the full distribution of volume fraction within an observation window of finite size for models of random media. A formula due to Lu and Torquato for the standard deviation or {open_quotes}coarseness{close_quotes} associated with the {ital local} volume fraction {xi} is extended for the nth moment of {xi} for any n. The distribution function F{sub L} of the local volume fraction of five different model microstructures is evaluated using analytical and computer-simulation methods for a wide range of window sizes and overall volume fractions. On the line, we examine a system of fully penetrable rods and a system of totally impenetrable rods formed by random sequential addition (RSA). In the plane, we study RSA totally impenetrable disks and fully penetrable aligned squares. In three dimensions, we study fully penetrable aligned cubes. In the case of fully penetrable rods, we will also simplify and numerically invert a prior analytical result for the Laplace transform of F{sub L}. In all of these models, we show that, for sufficiently large window sizes, F{sub L} can be reasonably approximated by the normal distribution. {copyright} {ital 1997 American Institute of Physics.}

  15. Study Reveals Fuel Injection Timing Impact on Particle Number Emissions (Fact Sheet)

    SciTech Connect

    Not Available

    2012-12-01

    Start of injection can improve environmental performance of fuel-efficient gasoline direct injection engines.

  16. Louisiana Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    893,400 897,513 963,688 901,635 903,686 888,023 1987-2015 Sales 893,400 897,513 963,688 901,635 903,686 888,023 1997-2015 Transported 0 0 0 0 0 0 1997-2015 Commercial Number of Consumers 58,562 58,749 63,381 59,147 58,996 57,873 1987-2015 Sales 58,501 58,685 63,256 58,985 58,823 57,695 1998-2015 Transported 61 64 125 162 173 178 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 461 441 415 488 530 515 1967-2015 Industrial Number of Consumers 942 920 963 916 883 845 1987-2015 Sales

  17. Maine Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    21,142 22,461 23,555 24,765 27,047 31,011 1987-2015 Sales 21,141 22,461 23,555 24,765 27,047 31,011 1997-2015 Transported 1 0 0 0 0 0 2010-2015 Commercial Number of Consumers 9,084 9,681 10,179 11,415 11,810 11,888 1987-2015 Sales 7,583 8,081 8,388 9,481 9,859 10,216 1998-2015 Transported 1,501 1,600 1,791 1,934 1,951 1,672 1999-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 642 681 718 714 765 847 1967-2015 Industrial Number of Consumers 94 102 108 120 126 136 1987-2015 Sales 26 29

  18. Mississippi Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    436,840 442,479 442,840 445,589 440,252 439,359 1987-2015 Sales 436,840 439,511 440,171 442,974 440,252 439,359 1997-2015 Transported 0 2,968 2,669 2,615 0 0 2010-2015 Commercial Number of Consumers 50,537 50,636 50,689 50,153 49,911 49,821 1987-2015 Sales 50,503 50,273 50,360 49,829 49,870 49,766 1998-2015 Transported 34 363 329 324 41 55 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 419 400 352 388 445 395 1967-2015 Industrial Number of Consumers 980 982 936 933 943 930

  19. Missouri Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    348,549 1,342,920 1,389,910 1,357,740 1,363,286 1,369,204 1987-2015 Sales 1,348,549 1,342,920 1,389,910 1,357,740 1,363,286 1,369,204 1997-2015 Transported 0 0 0 0 0 0 2010-2015 Commercial Number of Consumers 138,670 138,214 144,906 142,495 143,134 141,216 1987-2015 Sales 137,342 136,843 143,487 141,047 141,587 140,144 1998-2015 Transported 1,328 1,371 1,419 1,448 1,547 1,072 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 441 451 378 453 509 435 1967-2015 Industrial Number of

  20. Montana Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    257,322 259,046 259,957 262,122 265,849 269,766 1987-2015 Sales 256,841 258,579 259,484 261,637 265,323 269,045 1997-2015 Transported 481 467 473 485 526 721 2005-2015 Commercial Number of Consumers 34,002 34,305 34,504 34,909 35,205 35,777 1987-2015 Sales 33,652 33,939 33,967 34,305 34,558 35,022 1998-2015 Transported 350 366 537 604 647 755 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 602 651 557 601 612 541 1967-2015 Industrial Number of Consumers 384 381 372 372 369 366

  1. Nebraska Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    510,776 514,481 515,338 527,397 522,408 525,165 1987-2015 Sales 442,413 446,652 447,617 459,712 454,725 457,504 1997-2015 Transported 68,363 67,829 67,721 67,685 67,683 67,661 1997-2015 Commercial Number of Consumers 56,246 56,553 56,608 58,005 57,191 57,521 1987-2015 Sales 40,348 40,881 41,074 42,400 41,467 41,718 1998-2015 Transported 15,898 15,672 15,534 15,605 15,724 15,803 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 569 568 468 555 567 512 1967-2015 Industrial Number of

  2. Alabama Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    778,985 772,892 767,396 765,957 769,900 768,568 1986-2015 Sales 778,985 772,892 767,396 765,957 769,900 768,568 1997-2015 Transported 0 0 0 0 0 0 1997-2015 Commercial Number of Consumers 68,163 67,696 67,252 67,136 67,847 67,746 1986-2015 Sales 68,017 67,561 67,117 67,006 67,718 67,619 1998-2015 Transported 146 135 135 130 129 127 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 397 371 320 377 406 368 1967-2015 Industrial Number of Consumers 3,039 2,988 3,045 3,143 3,244 3,300

  3. Alaska Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    121,166 121,736 122,983 124,411 126,416 128,605 1986-2015 Sales 121,166 121,736 122,983 124,411 126,416 128,605 1997-2015 Commercial Number of Consumers 12,998 13,027 13,133 13,246 13,399 13,549 1986-2015 Sales 12,673 12,724 13,072 13,184 13,336 13,529 1998-2015 Transported 325 303 61 62 63 20 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 1,225 1,489 1,515 1,411 1,338 1,363 1967-2015 Industrial Number of Consumers 3 5 3 3 1 4 1987-2015 Sales 2 2 3 2 1 4 1998-2015 Transported 1

  4. Arkansas Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    549,970 551,795 549,959 549,764 549,034 550,108 1986-2015 Sales 549,970 551,795 549,959 549,764 549,034 550,108 1997-2015 Commercial Number of Consumers 67,987 67,815 68,765 68,791 69,011 69,265 1986-2015 Sales 67,676 67,454 68,151 68,127 68,291 68,438 1998-2015 Transported 311 361 614 664 720 827 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 592 590 603 692 734 688 1967-2015 Industrial Number of Consumers 1,079 1,133 990 1,020 1,009 1,023 1986-2015 Sales 580 554 523 513 531

  5. Sensitivity in risk analyses with uncertain numbers.

    SciTech Connect

    Tucker, W. Troy; Ferson, Scott

    2006-06-01

    Sensitivity analysis is a study of how changes in the inputs to a model influence the results of the model. Many techniques have recently been proposed for use when the model is probabilistic. This report considers the related problem of sensitivity analysis when the model includes uncertain numbers that can involve both aleatory and epistemic uncertainty and the method of calculation is Dempster-Shafer evidence theory or probability bounds analysis. Some traditional methods for sensitivity analysis generalize directly for use with uncertain numbers, but, in some respects, sensitivity analysis for these analyses differs from traditional deterministic or probabilistic sensitivity analyses. A case study of a dike reliability assessment illustrates several methods of sensitivity analysis, including traditional probabilistic assessment, local derivatives, and a ''pinching'' strategy that hypothetically reduces the epistemic uncertainty or aleatory uncertainty, or both, in an input variable to estimate the reduction of uncertainty in the outputs. The prospects for applying the methods to black box models are also considered.

  6. North Dakota Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    23,585 125,392 130,044 133,975 137,972 141,465 1987-2015 Sales 123,585 125,392 130,044 133,975 137,972 141,465 1997-2015 Transported 0 0 0 0 0 0 2004-2015 Commercial Number of Consumers 17,823 18,421 19,089 19,855 20,687 21,345 1987-2015 Sales 17,745 18,347 19,021 19,788 20,623 21,283 1998-2015 Transported 78 74 68 67 64 62 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 578 596 543 667 677 577 1967-2015 Industrial Number of Consumers 307 259 260 266 269 286 1987-2015 Sales 255

  7. Oregon Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    682,737 688,681 693,507 700,211 707,010 717,999 1987-2015 Sales 682,737 688,681 693,507 700,211 707,010 717,999 1997-2015 Commercial Number of Consumers 77,370 77,822 78,237 79,276 80,480 80,877 1987-2015 Sales 77,351 77,793 78,197 79,227 80,422 80,772 1998-2015 Transported 19 29 40 49 58 105 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 352 390 368 386 353 319 1967-2015 Industrial Number of Consumers 1,053 1,066 1,076 1,085 1,099 1,117 1987-2015 Sales 821 828 817 821 839 853

  8. Rhode Island Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    25,204 225,828 228,487 231,763 233,786 236,323 1987-2015 Sales 225,204 225,828 228,487 231,763 233,786 236,323 1997-2015 Commercial Number of Consumers 23,049 23,177 23,359 23,742 23,934 24,088 1987-2015 Sales 21,507 21,421 21,442 21,731 21,947 22,084 1998-2015 Transported 1,542 1,756 1,917 2,011 1,987 2,004 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 454 468 432 490 551 499 1967-2015 Industrial Number of Consumers 249 245 248 271 266 260 1987-2015 Sales 57 53 56 62 62 48

  9. South Carolina Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    570,797 576,594 583,633 593,286 605,644 620,555 1987-2015 Sales 570,797 576,594 583,633 593,286 605,644 620,555 1997-2015 Commercial Number of Consumers 55,853 55,846 55,908 55,997 56,323 56,871 1987-2015 Sales 55,776 55,760 55,815 55,902 56,225 56,768 1998-2015 Transported 77 86 93 95 98 103 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 432 396 383 426 451 413 1967-2015 Industrial Number of Consumers 1,325 1,329 1,435 1,452 1,442 1,438 1987-2015 Sales 1,139 1,137 1,215 1,223

  10. South Dakota Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    69,838 170,877 173,856 176,204 179,042 182,568 1987-2015 Sales 169,838 170,877 173,856 176,204 179,042 182,568 1997-2015 Commercial Number of Consumers 22,267 22,570 22,955 23,214 23,591 24,040 1987-2015 Sales 22,028 22,332 22,716 22,947 23,330 23,784 1998-2015 Transported 239 238 239 267 261 256 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 495 492 406 523 522 434 1967-2015 Industrial Number of Consumers 580 556 574 566 575 578 1987-2015 Sales 453 431 445 444 452 449 1998-2015

  11. Tennessee Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    ,085,387 1,089,009 1,084,726 1,094,122 1,106,917 1,124,572 1987-2015 Sales 1,085,387 1,089,009 1,084,726 1,094,122 1,106,917 1,124,572 1997-2015 Commercial Number of Consumers 127,914 128,969 130,139 131,091 131,027 132,392 1987-2015 Sales 127,806 128,866 130,035 130,989 130,931 132,294 1998-2015 Transported 108 103 104 102 96 98 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 439 404 345 411 438 401 1967-2015 Industrial Number of Consumers 2,702 2,729 2,679 2,581 2,595 2,651

  12. Number of Gas Producing Oil Wells

    Gasoline and Diesel Fuel Update

    73 0 1 2 3 4 5 6 7 8 9 10 11 12 Number of Consumers Eligible Participating Table 26. Number of consumers eligible and participating in a customer choice program in the residential sector, 2015 Figure 26. Top Five States with Participants in a Residential Customer Choice Program, 2015 California 10,969,597 6,712,311 441,523 Colorado 1,712,153 1,254,056 0 Connecticut 531,380 1,121 340 District of Columbia 147,895 147,867 17,167 Florida 701,981 17,626 16,363 Georgia 1,777,558 1,468,084 1,468,084

  13. Utah Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    821,525 830,219 840,687 854,389 869,052 891,917 1987-2015 Sales 821,525 830,219 840,687 854,389 869,052 891,917 1997-2015 Commercial Number of Consumers 61,976 62,885 63,383 64,114 65,134 66,143 1987-2015 Sales 61,929 62,831 63,298 63,960 64,931 65,917 1998-2015 Transported 47 54 85 154 203 226 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 621 643 558 646 586 541 1967-2015 Industrial Number of Consumers 293 286 302 323 326 320 1987-2015 Sales 205 189 189 187 176 157 1998-2015

  14. Vermont Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    38,047 38,839 39,917 41,152 42,231 43,267 1987-2015 Sales 38,047 38,839 39,917 41,152 42,231 43,267 1997-2015 Commercial Number of Consumers 5,137 5,256 5,535 5,441 5,589 5,696 1987-2015 Sales 5,137 5,256 5,535 5,441 5,589 5,696 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 464 472 418 873 864 1,039 1967-2015 Industrial Number of Consumers 38 36 38 13 13 14 1987-2015 Sales 37 35 38 13 13 14 1998-2015 Transported 1 1 0 0 0 0 1999-2015 Average Consumption per Consumer (Thousand

  15. Washington Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    067,979 1,079,277 1,088,762 1,102,318 1,118,193 1,133,629 1987-2015 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1,133,629 1997-2015 Commercial Number of Consumers 99,231 99,674 100,038 100,939 101,730 102,266 1987-2015 Sales 99,166 99,584 99,930 100,819 101,606 102,129 1998-2015 Transported 65 90 108 120 124 137 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 517 567 534 553 535 489 1967-2015 Industrial Number of Consumers 3,372 3,353 3,338 3,320 3,355 3,385 1987-2015

  16. West Virginia Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    344,131 342,069 340,256 340,102 338,652 337,643 1987-2015 Sales 344,125 342,063 340,251 340,098 338,649 337,642 1997-2015 Transported 6 6 5 4 3 1 1997-2015 Commercial Number of Consumers 34,063 34,041 34,078 34,283 34,339 34,448 1987-2015 Sales 33,258 33,228 33,257 33,466 33,574 33,706 1998-2015 Transported 805 813 821 817 765 742 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 731 708 664 707 702 656 1967-2015 Industrial Number of Consumers 102 94 97 95 92 101 1987-2015 Sales 32

  17. Wisconsin Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    663,583 1,671,834 1,681,001 1,692,891 1,705,907 1,721,640 1987-2015 Sales 1,663,583 1,671,834 1,681,001 1,692,891 1,705,907 1,721,640 1997-2015 Transported 0 0 0 0 0 0 1997-2015 Commercial Number of Consumers 164,173 165,002 165,657 166,845 167,901 169,271 1987-2015 Sales 163,060 163,905 164,575 165,718 166,750 168,097 1998-2015 Transported 1,113 1,097 1,082 1,127 1,151 1,174 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 501 528 465 596 637 533 1967-2015 Industrial Number of

  18. Colorado Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    ,634,587 1,645,716 1,659,808 1,672,312 1,690,581 1,712,153 1986-2015 Sales 1,634,582 1,645,711 1,659,803 1,672,307 1,690,576 1,712,150 1997-2015 Transported 5 5 5 5 5 3 1997-2015 Commercial Number of Consumers 145,460 145,837 145,960 150,145 150,235 150,545 1986-2015 Sales 145,236 145,557 145,563 149,826 149,921 150,230 1998-2015 Transported 224 280 397 319 314 315 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 396 383 355 392 386 359 1967-2015 Industrial Number of Consumers

  19. Connecticut Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    490,185 494,970 504,138 513,492 522,658 531,380 1986-2015 Sales 489,380 494,065 503,241 512,110 521,460 530,309 1997-2015 Transported 805 905 897 1,382 1,198 1,071 1997-2015 Commercial Number of Consumers 54,842 55,028 55,407 55,500 56,591 57,403 1986-2015 Sales 50,132 50,170 50,312 48,976 51,613 54,165 1998-2015 Transported 4,710 4,858 5,095 6,524 4,978 3,238 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 741 815 764 836 905 914 1967-2015 Industrial Number of Consumers 3,063

  20. Delaware Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    50,458 152,005 153,307 155,627 158,502 161,607 1986-2015 Sales 150,458 152,005 153,307 155,627 158,502 161,607 1997-2015 Commercial Number of Consumers 12,861 12,931 12,997 13,163 13,352 13,430 1986-2015 Sales 12,706 12,656 12,644 12,777 12,902 12,967 1998-2015 Transported 155 275 353 386 450 463 1999-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 948 810 772 849 890 873 1967-2015 Industrial Number of Consumers 114 129 134 138 141 144 1987-2015 Sales 40 35 29 28 28 29 1998-2015

  1. Florida Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    675,551 679,199 686,994 694,210 703,535 701,981 1986-2015 Sales 661,768 664,564 672,133 679,191 687,766 685,828 1997-2015 Transported 13,783 14,635 14,861 15,019 15,769 16,153 1997-2015 Commercial Number of Consumers 60,854 61,582 63,477 64,772 67,461 65,313 1986-2015 Sales 41,750 41,068 41,102 40,434 41,303 37,647 1998-2015 Transported 19,104 20,514 22,375 24,338 26,158 27,666 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 888 869 861 926 928 961 1967-2015 Industrial Number of

  2. Hawaii Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    25,389 25,305 25,184 26,374 28,919 28,952 1987-2015 Sales 25,389 25,305 25,184 26,374 28,919 28,952 1998-2015 Commercial Number of Consumers 2,551 2,560 2,545 2,627 2,789 2,815 1987-2015 Sales 2,551 2,560 2,545 2,627 2,789 2,815 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 697 691 727 713 692 678 1980-2015 Industrial Number of Consumers 24 24 22 22 23 25 1997-2015 Sales 24 24 22 22 23 25 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 14,111 15,087 16,126

  3. Idaho Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    46,602 350,871 353,963 359,889 367,394 374,557 1987-2015 Sales 346,602 350,871 353,963 359,889 367,394 374,557 1997-2015 Commercial Number of Consumers 38,506 38,912 39,202 39,722 40,229 40,744 1987-2015 Sales 38,468 38,872 39,160 39,681 40,188 40,704 1998-2015 Transported 38 40 42 41 41 40 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 390 433 404 465 422 410 1967-2015 Industrial Number of Consumers 184 178 179 183 189 187 1987-2015 Sales 108 103 105 109 115 117 1998-2015

  4. Iowa Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    879,713 883,733 892,123 895,414 900,420 908,058 1987-2015 Sales 879,713 883,733 892,123 895,414 900,420 908,058 1997-2015 Commercial Number of Consumers 98,396 98,541 99,113 99,017 99,186 99,662 1987-2015 Sales 96,996 97,075 97,580 97,334 97,413 97,834 1998-2015 Transported 1,400 1,466 1,533 1,683 1,773 1,828 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 525 526 442 572 579 494 1967-2015 Industrial Number of Consumers 1,528 1,465 1,469 1,491 1,572 1,572 1987-2015 Sales 1,161

  5. Kentucky Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    758,129 759,584 757,790 761,575 761,935 764,946 1987-2015 Sales 728,940 730,602 730,184 736,011 737,290 742,011 1997-2015 Transported 29,189 28,982 27,606 25,564 24,645 22,935 1997-2015 Commercial Number of Consumers 84,707 84,977 85,129 85,999 85,630 85,961 1987-2015 Sales 80,541 80,392 80,644 81,579 81,338 81,834 1998-2015 Transported 4,166 4,585 4,485 4,420 4,292 4,127 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 435 407 361 435 467 412 1967-2015 Industrial Number of

  6. Wyoming Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    153,852 155,181 157,226 158,889 160,896 159,925 1987-2015 Sales 117,735 118,433 118,691 117,948 118,396 116,456 1997-2015 Transported 36,117 36,748 38,535 40,941 42,500 43,469 1997-2015 Commercial Number of Consumers 19,977 20,146 20,387 20,617 20,894 20,816 1987-2015 Sales 14,319 14,292 14,187 14,221 14,452 14,291 1998-2015 Transported 5,658 5,854 6,200 6,396 6,442 6,525 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 558 580 514 583 583 622 1967-2015 Industrial Number of

  7. Alaska Maximum Number of Active Crews Engaged in Seismic Surveying (Number

    Gasoline and Diesel Fuel Update

    of Elements) Seismic Surveying (Number of Elements) Alaska Maximum Number of Active Crews Engaged in Seismic Surveying (Number of Elements) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 0 0 2 3 3 3 1 1 0 0 0 0 2001 0 0 0 0 2 2 0 0 0 0 0 0 2002 2 2 2 2 2 2 2 2 2 2 2 1 2003 0 0 2 2 2 2 2 2

  8. Stockpile Stewardship Quarterly, Volume 2, Number 1

    National Nuclear Security Administration (NNSA)

    1 * May 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 2, Number 1 Inside this Issue 2 LANL and ANL Complete Groundbreaking Shock Experiments at the Advanced Photon Source 3 Characterization of Activity-Size-Distribution of Nuclear Fallout 5 Modeling Mix in High-Energy-Density Plasma 6 Quality Input for Microscopic Fission Theory 8 Fiber Reinforced Composites Under Pressure: A Case Study in

  9. Notices Total Estimated Number of Annual

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    372 Federal Register / Vol. 78, No. 181 / Wednesday, September 18, 2013 / Notices Total Estimated Number of Annual Burden Hours: 10,128. Abstract: Enrollment in the Federal Student Aid (FSA) Student Aid Internet Gateway (SAIG) allows eligible entities to securely exchange Title IV, Higher Education Act (HEA) assistance programs data electronically with the Department of Education processors. Organizations establish Destination Point Administrators (DPAs) to transmit, receive, view and update

  10. WIPP Site By The Numbers August 2015

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    0 ft. By the Numbers The Waste Isolation Pilot Plant (WIPP) is a Department of Energy facility designed to safely isolate defense- related transuranic (TRU) waste from people and the environment. WIPP, which began waste disposal operations in 1999, is located 26 miles outside of Carlsbad, New Mexico. Waste temporarily stored at sites around the country is shipped to WIPP and permanently disposed in rooms mined out of an ancient salt formation below the surface. TRU waste destined for WIPP

  11. Droplet Number Concentration Value Added Product

    Energy Science and Technology Software Center

    2015-08-06

    Cloud droplet number concentration is an important factor in understanding aerosol-cloud interactions. As aerosol concentration increases, it is expected that droplet number concentration (Nd) will increase and droplet size will decrease, for a given liquid water path. This will greatly affect cloud albedo as smaller droplets reflect more shortwave radiation; however, the magnitude and variability of these processes under different environmental conditions is still uncertain.McComiskey et al. (2009) have implemented a method, based onBoers andmore » Mitchell (1994), for calculating Nd from ground-based remote sensing measurements of optical depth and liquid water path. They show that the magnitude of the aerosol-cloud interactions (ACI) varies with a range of factors, including the relative value of the cloud liquid water path (LWP), the aerosol size distribution, and the cloud updraft velocity. Estimates of Nd under a range of cloud types and conditions and at a variety of sites are needed to further quantify the impacts of aerosol cloud interactions. In order to provide data sets for studying aerosol-cloud interactions, the McComiskey et al. (2009) method was implemented as the Droplet Number Concentration (NDROP) value-added product (VAP).« less

  12. Table B14. Number of Establishments in Building, Number of Buildings, 1999

    Energy Information Administration (EIA) (indexed site)

    4. Number of Establishments in Building, Number of Buildings, 1999" ,"Number of Buildings (thousand)" ,"All Buildings","Number of Establishments in Building" ,,"One","Two to Five","Six to Ten","Eleven to Twenty","More than Twenty","Currently Unoccupied" "All Buildings ................",4657,3528,688,114,48,27,251 "Building Floorspace" "(Square Feet)" "1,001 to 5,000

  13. U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number

    Energy Information Administration (EIA) (indexed site)

    of Elements) Acquifers Capacity (Number of Elements) U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49 2000's 49 39 38 43 43 44 44 43 43 43 2010's 43 43 44 47 46 47 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of

  14. Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 5 5 4 4 2000's 4 4 4 4 4 4 4 4 0 0 2010's 0 0 0 0 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: Number of Producing Gas

  15. On almost surely bounded semigroups of random linear operators

    SciTech Connect

    Zhang, Xia; Liu, Ming

    2013-05-15

    Menger proposed transferring the probabilistic notions of quantum mechanics to the underlying geometry. Following Menger's idea, the notion of random metric spaces is a random generalization of that of metric spaces and also plays an important role in the study of random operator equations. The main difficulty of this article is to work out a skill to give a property peculiar to a special semigroup of random operators, which is not involved in the classical case. Subsequently, some random operators equations are studied, in particular, we discuss the Schroedinger-type random equation, which considerably generalizes the corresponding result in the sense of Skorohod.

  16. Compare Activities by Number of Employees

    Energy Information Administration (EIA) (indexed site)

    this page, please call 202-586-8800. Employees per Building by Building Type Inpatient health care buildings averaged six times more employees per building than any other building...

  17. U.S. Maximum Number of Active Crews Engaged in Seismic Surveying (Number of

    Gasoline and Diesel Fuel Update

    Elements) Maximum Number of Active Crews Engaged in Seismic Surveying (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 615 717 624 481 563 655 728 848 NA 787 2010's 774

  18. Convergence properties of polynomial chaos approximations for L2 random variables.

    SciTech Connect

    Field, Richard V., Jr. (.,; .); Grigoriu, Mircea (Cornell University, Ithaca, NY)

    2007-03-01

    Polynomial chaos (PC) representations for non-Gaussian random variables are infinite series of Hermite polynomials of standard Gaussian random variables with deterministic coefficients. For calculations, the PC representations are truncated, creating what are herein referred to as PC approximations. We study some convergence properties of PC approximations for L{sub 2} random variables. The well-known property of mean-square convergence is reviewed. Mathematical proof is then provided to show that higher-order moments (i.e., greater than two) of PC approximations may or may not converge as the number of terms retained in the series, denoted by n, grows large. In particular, it is shown that the third absolute moment of the PC approximation for a lognormal random variable does converge, while moments of order four and higher of PC approximations for uniform random variables do not converge. It has been previously demonstrated through numerical study that this lack of convergence in the higher-order moments can have a profound effect on the rate of convergence of the tails of the distribution of the PC approximation. As a result, reliability estimates based on PC approximations can exhibit large errors, even when n is large. The purpose of this report is not to criticize the use of polynomial chaos for probabilistic analysis but, rather, to motivate the need for further study of the efficacy of the method.

  19. Health Code Number (HCN) Development Procedure

    SciTech Connect

    Petrocchi, Rocky; Craig, Douglas K.; Bond, Jayne-Anne; Trott, Donna M.; Yu, Xiao-Ying

    2013-09-01

    This report provides the detailed description of health code numbers (HCNs) and the procedure of how each HCN is assigned. It contains many guidelines and rationales of HCNs. HCNs are used in the chemical mixture methodology (CMM), a method recommended by the department of energy (DOE) for assessing health effects as a result of exposures to airborne aerosols in an emergency. The procedure is a useful tool for proficient HCN code developers. Intense training and quality assurance with qualified HCN developers are required before an individual comprehends the procedure to develop HCNs for DOE.

  20. The New Element Berkelium (Atomic Number 97)

    DOE R&D Accomplishments

    Seaborg, G. T.; Thompson, S. G.; Ghiorso, A.

    1950-04-26

    An isotope of the element with atomic number 97 has been discovered as a product of the helium-ion bombardment of americium. The name berkelium, symbol Bk, is proposed for element 97. The chemical separation of element 97 from the target material and other reaction products was made by combinations of precipitation and ion exchange adsorption methods making use of its anticipated (III) and (IV) oxidation states and its position as a member of the actinide transition series. The distinctive chemical properties made use of in its separation and the equally distinctive decay properties of the particular isotope constitute the principal evidence for the new element.

  1. Experimental Stations by Number | Stanford Synchrotron Radiation

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Lightsource Experimental Stations by Number Beam Line by Techniques Photon Source Parameters Station Type Techniques Energy Range Contact Person Experimental Station 1-5 X-ray Materials Small-angle X-ray Scattering (SAXS) focused 4600-16000 eV Christopher J. Tassone Tim J. Dunn Experimental Station 2-1 X-ray Powder diffraction Thin film diffraction Focused 5000 - 14500 eV Apurva Mehta Charles Troxel Jr Experimental Station 2-2 X-ray X-ray Absorption Spectroscopy 5000 to 37000 eV Ryan Davis

  2. Property:NumberOfLEDSTools | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Name NumberOfLEDSTools Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLEDSTools&oldid322418" Feedback Contact needs updating Image...

  3. Property:Number of Plants Included in Planned Estimate | Open...

    OpenEI (Open Energy Information) [EERE & EIA]

    Number of Plants Included in Planned Estimate Jump to: navigation, search Property Name Number of Plants Included in Planned Estimate Property Type String Description Number of...

  4. Property:Number of Color Cameras | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Color Cameras Jump to: navigation, search Property Name Number of Color Cameras Property Type Number Pages using the property "Number of Color Cameras" Showing 25 pages using this...

  5. Total number of longwall faces drops below 50

    SciTech Connect

    Fiscor, S.

    2009-02-15

    For the first time since Coal Age began its annual Longwall Census the number of faces has dropped below 50. A total of five mines operate two longwall faces. CONSOL Energy remains the leader with 12 faces. Arch Coal operates five longwall mines; Robert E. Murray owns five longwall mines. West Virginia has 13 longwalls, followed by Pennsylvania (8), Utah (6) and Alabama (6). A detailed table gives for each longwall installation, the ownership, seam height, cutting height, panel width and length, overburden, number of gate entries, depth of cut, model of equipment used (shearer, haulage system, roof support, face conveyor, stage loader, crusher, electrical controls and voltage to face). 2 tabs., 1 photo.

  6. Livermore Random I/O Testbench

    Energy Science and Technology Software Center

    2012-09-01

    LRIOT is a test bench framework that is designed to generate sophisticated I/O rates that can stress high-performance memory and storage systems, such as non-volatile random access memories (NVRAM)and storage class memory. Furthermore, LRIOT provides the capabilities to mix multiple types of concurrency, namely threading and task parallelism, as well as distributed execution using Message Passing Interface (MPI) libraries. It will be used by algorithm designers to generate access patterns that mimic their application's behavior,more » and by system designers to test high-performance NVRAM storage.« less

  7. Relativistic Random Phase Approximation At Finite Temperature

    SciTech Connect

    Niu, Y. F.; Paar, N.; Vretenar, D.; Meng, J.

    2009-08-26

    The fully self-consistent finite temperature relativistic random phase approximation (FTRRPA) has been established in the single-nucleon basis of the temperature dependent Dirac-Hartree model (FTDH) based on effective Lagrangian with density dependent meson-nucleon couplings. Illustrative calculations in the FTRRPA framework show the evolution of multipole responses of {sup 132}Sn with temperature. With increased temperature, in both monopole and dipole strength distributions additional transitions appear in the low energy region due to the new opened particle-particle and hole-hole transition channels.

  8. Large N (=3) Neutrinos and Random Matrix Theory (Journal Article...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Large N (3) Neutrinos and Random Matrix Theory Citation Details In-Document Search Title: Large N (3) Neutrinos and Random Matrix Theory You are accessing a ...

  9. The New Element Californium (Atomic Number 98)

    DOE R&D Accomplishments

    Seaborg, G. T.; Thompson, S. G.; Street, K. Jr.; Ghiroso, A.

    1950-06-19

    Definite identification has been made of an isotope of the element with atomic number 98 through the irradiation of Cm{sup 242} with about 35-Mev helium ions in the Berkeley Crocker Laboratory 60-inch cyclotron. The isotope which has been identified has an observed half-life of about 45 minutes and is thought to have the mass number 244. The observed mode of decay of 98{sup 244} is through the emission of alpha-particles, with energy of about 7.1 Mev, which agrees with predictions. Other considerations involving the systematics of radioactivity in this region indicate that it should also be unstable toward decay by electron capture. The chemical separation and identification of the new element was accomplished through the use of ion exchange adsorption methods employing the resin Dowex-50. The element 98 isotope appears in the eka-dysprosium position on elution curves containing berkelium and curium as reference points--that is, it precedes berkelium and curium off the column in like manner that dysprosium precedes terbium and gadolinium. The experiments so far have revealed only the tripositive oxidation state of eka-dysprosium character and suggest either that higher oxidation states are not stable in aqueous solutions or that the rates of oxidation are slow. The successful identification of so small an amount of an isotope of element 98 was possible only through having made accurate predictions of the chemical and radioactive properties.

  10. Random Selection for Drug Screening (Technical Report) | SciTech...

    Office of Scientific and Technical Information (OSTI)

    Subject: 99 GENERAL AND MISCELLANEOUSMATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; PROBABILITY; SAMPLING; DRUG ABUSE; DRUGS; CRIME DETECTION drug screenings, random selection ...

  11. Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

    SciTech Connect

    Cryns, Jackson W.; Hatchell, Brian K.; Santiago-Rojas, Emiliano; Silvers, Kurt L.

    2013-07-01

    Formal journal article Experimental analysis of a piezoelectric energy harvesting system for harmonic, random, and sine on random vibration Abstract: Harvesting power with a piezoelectric vibration powered generator using a full-wave rectifier conditioning circuit is experimentally compared for varying sinusoidal, random and sine on random (SOR) input vibration scenarios. Additionally, the implications of source vibration characteristics on harvester design are discussed. Studies in vibration harvesting have yielded numerous alternatives for harvesting electrical energy from vibrations but piezoceramics arose as the most compact, energy dense means of energy transduction. The rise in popularity of harvesting energy from ambient vibrations has made piezoelectric generators commercially available. Much of the available literature focuses on maximizing harvested power through nonlinear processing circuits that require accurate knowledge of generator internal mechanical and electrical characteristics and idealization of the input vibration source, which cannot be assumed in general application. In this manuscript, variations in source vibration and load resistance are explored for a commercially available piezoelectric generator. We characterize the source vibration by its acceleration response for repeatability and transcription to general application. The results agree with numerical and theoretical predictions for in previous literature that load optimal resistance varies with transducer natural frequency and source type, and the findings demonstrate that significant gains are seen with lower tuned transducer natural frequencies for similar source amplitudes. Going beyond idealized steady state sinusoidal and simplified random vibration input, SOR testing allows for more accurate representation of real world ambient vibration. It is shown that characteristic interactions from more complex vibrational sources significantly alter power generation and power processing

  12. Tennessee Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Tennessee Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 700 1990's 690 650 600 505 460 420 2000's 380 350 400 430 280 400 330 305 285 310 2010's 230 1,027 1,027 1,089 NA NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next

  13. South Dakota Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) South Dakota Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 53 1990's 54 54 38 47 55 56 61 60 59 60 2000's 71 68 69 61 61 69 69 71 71 89 2010's 102 155 159 133 128 124 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next

  14. Maryland Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Maryland Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 8 1990's 7 7 9 7 7 7 8 8 8 8 2000's 7 7 5 7 7 7 7 7 7 7 2010's 7 7 7 7 5 7 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages:

  15. Missouri Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Missouri Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4 1990's 8 6 5 8 12 15 24 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 19 15 7 6 NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring

  16. Nebraska Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Nebraska Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15 1990's 11 12 22 59 87 87 88 91 95 96 2000's 98 96 106 109 111 114 114 186 322 285 2010's 276 307 299 246 109 140 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next

  17. Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 18 1990's 19 16 16 18 19 17 18 17 15 19 2000's 17 20 18 15 15 15 14 18 21 24 2010's 26 28 24 24 12 14 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date:

  18. Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 108 1990's 111 110 112 113 104 100 102 141 148 99 2000's 152 170 165 195 224 227 231 239 261 261 2010's 269 274 281 300 338 329 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  19. Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 1990's 5 6 6 6 6 7 7 8 8 8 2000's 9 8 7 9 6 6 7 7 6 6 2010's 5 5 4 3 6 6 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages:

  20. Illinois Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Illinois Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241 1990's 356 373 382 385 390 372 370 372 185 300 2000's 280 300 225 240 251 316 316 43 45 51 2010's 50 40 40 34 36 35 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016

  1. Random unitary maps for quantum state reconstruction

    SciTech Connect

    Merkel, Seth T. [Institute for Quantum Computing, Waterloo, Ontario N2L 3G1 (Canada); Riofrio, Carlos A.; Deutsch, Ivan H. [Center for Quantum Information and Control (CQuIC), Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, 87131 (United States); Flammia, Steven T. [Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5 (Canada); Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California 93106 (United States)

    2010-03-15

    We study the possibility of performing quantum state reconstruction from a measurement record that is obtained as a sequence of expectation values of a Hermitian operator evolving under repeated application of a single random unitary map, U{sub 0}. We show that while this single-parameter orbit in operator space is not informationally complete, it can be used to yield surprisingly high-fidelity reconstruction. For a d-dimensional Hilbert space with the initial observable in su(d), the measurement record lacks information about a matrix subspace of dimension {>=}d-2 out of the total dimension d{sup 2}-1. We determine the conditions on U{sub 0} such that the bound is saturated, and show they are achieved by almost all pseudorandom unitary matrices. When we further impose the constraint that the physical density matrix must be positive, we obtain even higher fidelity than that predicted from the missing subspace. With prior knowledge that the state is pure, the reconstruction will be perfect (in the limit of vanishing noise) and for arbitrary mixed states, the fidelity is over 0.96, even for small d, and reaching F>0.99 for d>9. We also study the implementation of this protocol based on the relationship between random matrices and quantum chaos. We show that the Floquet operator of the quantum kicked top provides a means of generating the required type of measurement record, with implications on the relationship between quantum chaos and information gain.

  2. MENTOR QUESTIONNAIRE Name: Title: Email: Office Phone Number:

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    MENTOR QUESTIONNAIRE Name: Title: Email: Office Phone Number: Office Address: is interested in this program because: Are you willing to act as a mentor for ? Yes No Expectations of the Mentoring Program How long? 6-months minimum commitment. Are you willing to commit to the 6-months minimum timeframe? Yes No How much time? You decide with your mentee; 1-4 hours/month is recommended. Please return completed form to Ames Lab Human Resources, 105 TASF. Are you willing to commit 1-4 hours per month

  3. Maryland Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    071,566 1,077,168 1,078,978 1,099,272 1,101,292 1,113,342 1987-2015 Sales 923,870 892,844 867,627 852,555 858,352 875,150 1997-2015 Transported 147,696 184,324 211,351 246,717 242,940 238,192 1997-2015 Commercial Number of Consumers 75,192 75,788 75,799 77,117 77,846 78,138 1987-2015 Sales 54,966 53,778 52,383 52,763 53,961 53,651 1998-2015 Transported 20,226 22,010 23,416 24,354 23,885 24,487 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 898 891 846 923 961 898 1967-2015

  4. Massachusetts Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    389,592 1,408,314 1,447,947 1,467,578 1,461,350 1,478,072 1987-2015 Sales 1,387,842 1,406,447 1,445,934 1,464,120 1,457,055 1,471,658 1997-2015 Transported 1,750 1,867 2,013 3,458 4,295 6,414 1997-2015 Commercial Number of Consumers 144,487 138,225 142,825 144,246 139,556 140,533 1987-2015 Sales 128,256 121,065 124,099 124,963 120,803 121,754 1998-2015 Transported 16,231 17,160 18,726 19,283 18,753 18,779 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 499 586 511 692 758 750

  5. Michigan Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    3,152,468 3,153,895 3,161,033 3,180,349 3,192,807 3,213,910 1987-2015 Sales 2,952,550 2,946,507 2,939,693 2,950,315 2,985,315 3,016,548 1997-2015 Transported 199,918 207,388 221,340 230,034 207,492 197,362 1997-2015 Commercial Number of Consumers 249,309 249,456 249,994 250,994 253,127 254,484 1987-2015 Sales 217,325 213,995 212,411 213,532 219,240 222,427 1998-2015 Transported 31,984 35,461 37,583 37,462 33,887 32,057 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 611 656 578

  6. New Jersey Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    2,649,282 2,659,205 2,671,308 2,686,452 2,705,274 2,728,340 1987-2015 Sales 2,556,514 2,514,492 2,467,520 2,428,664 2,482,281 2,559,463 1997-2015 Transported 92,768 144,713 203,788 257,788 222,993 168,877 1997-2015 Commercial Number of Consumers 234,158 234,721 237,602 236,746 240,083 241,417 1987-2015 Sales 200,680 196,963 192,913 185,030 186,591 190,255 1998-2015 Transported 33,478 37,758 44,689 51,716 53,492 51,162 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 775 817 735

  7. California Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    0,542,584 10,625,190 10,681,916 10,754,908 10,781,720 10,969,597 1986-2015 Sales 10,469,734 10,545,585 10,547,706 10,471,814 10,372,973 10,539,966 1997-2015 Transported 72,850 79,605 134,210 283,094 408,747 429,631 1997-2015 Commercial Number of Consumers 439,572 440,990 442,708 444,342 443,115 446,510 1986-2015 Sales 399,290 390,547 387,760 387,806 385,878 391,672 1998-2015 Transported 40,282 50,443 54,948 56,536 57,237 54,838 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 564

  8. Ohio Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    3,240,619 3,236,160 3,244,274 3,271,074 3,283,968 3,294,010 1987-2015 Sales 1,418,217 1,352,292 855,055 636,744 664,114 670,508 1997-2015 Transported 1,822,402 1,883,868 2,389,219 2,634,330 2,619,854 2,623,502 1997-2015 Commercial Number of Consumers 268,346 268,647 267,793 269,081 269,758 269,981 1987-2015 Sales 92,621 85,877 51,308 35,966 37,035 36,612 1998-2015 Transported 175,725 182,770 216,485 233,115 232,723 233,369 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 583 601

  9. Texas Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    4,288,495 4,326,156 4,370,057 4,424,103 4,469,282 4,523,977 1987-2015 Sales 4,287,929 4,326,076 4,369,990 4,424,037 4,469,220 4,523,911 1997-2015 Transported 566 80 67 66 62 66 1997-2015 Commercial Number of Consumers 312,277 314,041 314,811 314,036 316,756 319,512 1987-2015 Sales 310,842 312,164 312,574 311,493 313,971 316,538 1998-2015 Transported 1,435 1,877 2,237 2,543 2,785 2,974 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 605 587 512 553 584 556 1967-2015 Industrial

  10. Georgia Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    1,740,587 1,740,006 1,739,543 1,805,425 1,759,394 1,777,558 1986-2015 Sales 321,290 321,515 319,179 377,652 319,109 320,228 1997-2015 Transported 1,419,297 1,418,491 1,420,364 1,427,773 1,440,285 1,457,330 1997-2015 Commercial Number of Consumers 124,759 123,454 121,243 126,060 122,578 123,307 1986-2015 Sales 32,318 32,162 31,755 36,556 31,850 31,850 1998-2015 Transported 92,441 91,292 89,488 89,504 90,728 91,457 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 482 458 428 454 482

  11. Illinois Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    ,842,206 3,855,942 3,878,806 3,838,120 3,870,670 3,876,362 1987-2015 Sales 3,568,120 3,594,047 3,605,796 3,550,217 3,570,339 3,545,188 1997-2015 Transported 274,086 261,895 273,010 287,903 300,331 331,174 1997-2015 Commercial Number of Consumers 291,395 293,213 297,523 282,743 294,391 295,869 1987-2015 Sales 240,197 241,582 244,480 225,913 235,097 231,769 1998-2015 Transported 51,198 51,631 53,043 56,830 59,294 64,100 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 680 735 632

  12. Indiana Number of Natural Gas Consumers

    Energy Information Administration (EIA) (indexed site)

    1,669,026 1,707,148 1,673,132 1,681,841 1,693,267 1,704,243 1987-2015 Sales 1,579,351 1,614,042 1,584,155 1,600,366 1,618,827 1,635,444 1997-2015 Transported 89,675 93,106 88,977 81,475 74,440 68,799 1997-2015 Commercial Number of Consumers 156,557 161,293 158,213 158,965 159,596 160,051 1987-2015 Sales 139,058 143,227 139,676 139,589 140,196 141,013 1998-2015 Transported 17,499 18,066 18,537 19,376 19,400 19,038 1998-2015 Average Consumption per Consumer (Thousand Cubic Ft.) 485 471 421 520 570

  13. Parameters affecting the resilience of scale-free networks to random failures.

    SciTech Connect

    Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran; Saia, Jared

    2005-09-01

    It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

  14. Federal Offshore--Gulf of Mexico Natural Gas Number of Oil Wells (Number of

    Gasoline and Diesel Fuel Update

    Condensate Wells (Number of Elements) Gas and Gas Condensate Wells (Number of Elements) Federal Offshore--Gulf of Mexico Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 0 NA 2000's NA 3,271 3,245 3,039 2,781 2,123 2,419 2,552 1,527 1,984 2010's 1,852 2,226 1,892 1,588 1,377 1,163 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  15. U.S. Maximum Number of Active Crews Engaged in Seismic Surveying (Number of

    Gasoline and Diesel Fuel Update

    Elements) Maximum Number of Active Crews Engaged in Seismic Surveying (Number of Elements) U.S. Maximum Number of Active Crews Engaged in Seismic Surveying (Number of Elements) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 0 0 62 63 59 63 58 61 59 63 62 65 2001 61 61 63 65 64 60 58 56 54 58 59 58 2002 54 57 54 50 51 50 52 50 56 57 50 43 2003 40 41 41 40 38 39 41 43 39 39 38 42 2004 43 45 45 45 44 49 48 49 48 48 49 50 2005 52 53 51 50 55 57 54 55 56 57 57 58 2006 55 57 59 58 58 57

  16. Alaska Maximum Number of Active Crews Engaged in Seismic Surveying (Number

    Gasoline and Diesel Fuel Update

    of Elements) Seismic Surveying (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 13 4 23 12

  17. Low-temperature random matrix theory at the soft edge

    SciTech Connect

    Edelman, Alan; Persson, Per-Olof; Sutton, Brian D.

    2014-06-15

    Low temperature random matrix theory is the study of random eigenvalues as energy is removed. In standard notation, ? is identified with inverse temperature, and low temperatures are achieved through the limit ? ? ?. In this paper, we derive statistics for low-temperature random matrices at the soft edge, which describes the extreme eigenvalues for many random matrix distributions. Specifically, new asymptotics are found for the expected value and standard deviation of the general-? Tracy-Widom distribution. The new techniques utilize beta ensembles, stochastic differential operators, and Riccati diffusions. The asymptotics fit known high-temperature statistics curiously well and contribute to the larger program of general-? random matrix theory.

  18. Methods and optical fibers that decrease pulse degradation resulting from random chromatic dispersion

    DOEpatents

    Chertkov, Michael; Gabitov, Ildar

    2004-03-02

    The present invention provides methods and optical fibers for periodically pinning an actual (random) accumulated chromatic dispersion of an optical fiber to a predicted accumulated dispersion of the fiber through relatively simple modifications of fiber-optic manufacturing methods or retrofitting of existing fibers. If the pinning occurs with sufficient frequency (at a distance less than or are equal to a correlation scale), pulse degradation resulting from random chromatic dispersion is minimized. Alternatively, pinning may occur quasi-periodically, i.e., the pinning distance is distributed between approximately zero and approximately two to three times the correlation scale.

  19. Coherent diffractive imaging using randomly coded masks

    SciTech Connect

    Seaberg, Matthew H.; D'Aspremont, Alexandre; Turner, Joshua J.

    2015-12-07

    We experimentally demonstrate an extension to coherent diffractive imaging that encodes additional information through the use of a series of randomly coded masks, removing the need for typical object-domain constraints while guaranteeing a unique solution to the phase retrieval problem. Phase retrieval is performed using a numerical convex relaxation routine known as “PhaseCut,” an iterative algorithm known for its stability and for its ability to find the global solution, which can be found efficiently and which is robust to noise. The experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-ray synchrotron and even free electron laser experiments.

  20. Dynamics of dispersive photon-number QND measurements in a micromaser

    SciTech Connect

    Kozlovskii, A. V. [Russian Academy of Sciences, Lebedev Physical Institute (Russian Federation)], E-mail: kozlovsk@sci.lebedev.ru

    2007-04-15

    A numerical analysis of dispersive quantum nondemolition measurement of the photon number of a microwave cavity field is presented. Simulations show that a key property of the dispersive atom-field interaction used in Ramsey interferometry is the extremely high sensitivity of the dynamics of atomic and field states to basic parameters of the system. When a monokinetic atomic beam is sent through a microwave cavity, a qualitative change in the field state can be caused by an uncontrollably small deviation of parameters (such as atom path length through the cavity, atom velocity, cavity mode frequency detuning, or atom-field coupling constants). The resulting cavity field can be either in a Fock state or in a super-Poissonian state (characterized by a large photon-number variance). When the atoms have a random velocity spread, the field is squeezed to a Fock state for arbitrary values of the system's parameters. However, this makes detection of Ramsey fringes impossible, because the probability of detecting an atom in the upper or lower electronic state becomes a random quantity almost uniformly distributed over the interval between zero and unity, irrespective of the cavity photon number.

  1. Texas Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Texas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 48,609 1990's 50,867 47,615 46,298 47,101 48,654 54,635 53,816 56,747 58,736 58,712 2000's 60,577 63,704 65,779 68,572 72,237 74,827 74,265 76,436 87,556 93,507 2010's 95,014 139,368 140,087 140,964 142,292 142,368 - = No Data Reported; -- = Not Applicable; NA = Not

  2. U.S. Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    (Number of Elements) U.S. Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,013,040 4,124,745 4,168,048 1990's 4,236,280 4,357,252 4,409,699 4,464,906 4,533,905 4,636,500 4,720,227 4,761,409 5,044,497 5,010,189 2000's 5,010,817 4,996,446 5,064,384 5,152,177 5,139,949 5,198,028 5,273,379 5,308,785 5,444,335 5,322,332 2010's 5,301,576 5,319,817 5,356,397 5,372,522 5,413,546 5,449,180 - = No Data

  3. U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 262,483 1990's 269,790 276,987 276,014 282,152 291,773 298,541 301,811 310,971 316,929 302,421 2000's 341,678 373,304 387,772 393,327 406,147 425,887 440,516 452,945 476,652 493,100 2010's 487,627 574,593 577,916 572,742 565,951 555,364 - = No Data Reported; -- = Not

  4. U.S. Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    (Number of Elements) U.S. Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 195,544 199,041 225,346 1990's 218,341 216,529 209,616 209,666 202,940 209,398 206,049 234,855 226,191 228,331 2000's 220,251 217,026 205,915 205,514 209,058 206,223 193,830 198,289 225,044 207,624 2010's 192,730 189,301 189,372 192,288 192,139 188,585 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  5. U.S. Natural Gas Number of Residential Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    (Number of Elements) U.S. Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 47,710,444 48,474,449 49,309,593 1990's 50,187,178 51,593,206 52,331,397 52,535,411 53,392,557 54,322,179 55,263,673 56,186,958 57,321,746 58,223,229 2000's 59,252,728 60,286,364 61,107,254 61,871,450 62,496,134 63,616,827 64,166,280 64,964,769 65,073,996 65,329,582 2010's 65,542,345 65,940,522 66,375,134 66,812,393

  6. Pennsylvania Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Pennsylvania Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 30,000 1990's 30,300 31,000 31,000 31,100 31,150 31,025 31,792 32,692 21,576 23,822 2000's 36,000 40,100 40,830 42,437 44,227 46,654 49,750 52,700 55,631 57,356 2010's 44,500 61,815 62,922 61,838 67,621 68,536 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Louisiana Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Louisiana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 16,309 1990's 16,889 15,271 13,512 15,569 12,958 14,169 15,295 14,958 18,399 16,717 2000's 15,700 16,350 17,100 16,939 20,734 18,838 17,459 18,145 19,213 18,860 2010's 19,137 19,318 19,345 18,802 18,660 18,382 - = No Data Reported; -- = Not Applicable; NA = Not

  8. Michigan Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Michigan Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,207 1990's 1,438 2,620 3,257 5,500 6,000 5,258 5,826 6,825 7,000 6,750 2000's 7,068 7,425 7,700 8,600 8,500 8,900 9,200 9,712 9,995 10,600 2010's 10,100 10,480 10,381 10,322 10,246 9,929 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  9. Mississippi Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Mississippi Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 543 1990's 585 629 507 620 583 535 568 560 527 560 2000's 997 1,143 979 427 1,536 1,676 1,836 2,315 2,343 2,320 2010's 1,979 1,703 1,666 1,632 1,594 1,560 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  10. Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,700 1990's 2,607 2,802 2,890 3,075 2,940 2,918 2,990 3,071 3,423 3,634 2000's 3,321 4,331 4,544 4,539 4,971 5,751 6,578 6,925 7,095 7,031 2010's 6,059 6,615 6,366 5,870 5,682 5,655 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  11. New Jersey Natural Gas Number of Commercial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Commercial Consumers (Number of Elements) New Jersey Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 200,387 206,261 212,496 1990's 217,548 215,408 212,726 215,948 219,061 222,632 224,749 226,714 234,459 232,831 2000's 243,541 212,726 214,526 223,564 223,595 226,007 227,819 230,855 229,235 234,125 2010's 234,158 234,721 237,602 236,746 240,083 241,417 - = No Data Reported; -- = Not Applicable; NA

  12. New Jersey Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) New Jersey Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,265 6,123 6,079 1990's 5,976 8,444 11,474 11,224 10,608 10,362 10,139 17,625 16,282 10,089 2000's 9,686 9,247 8,473 9,027 8,947 8,500 8,245 8,036 7,680 7,871 2010's 7,505 7,391 7,290 7,216 7,157 7,019 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure

  13. New York Natural Gas Number of Industrial Consumers (Number of Elements)

    Energy Information Administration (EIA) (indexed site)

    Industrial Consumers (Number of Elements) New York Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 23,276 24,654 27,426 1990's 25,008 28,837 28,198 23,833 21,833 22,484 15,300 23,099 5,294 6,136 2000's 6,553 6,501 3,068 2,984 2,963 3,752 3,642 7,484 7,080 6,634 2010's 6,236 6,609 5,910 6,311 6,313 6,030 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure

  14. Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 34,450 1990's 34,586 34,760 34,784 34,782 34,731 34,520 34,380 34,238 34,098 33,982 2000's 33,897 33,917 34,593 33,828 33,828 33,735 33,945 34,416 34,416 34,963 2010's 34,931 31,966 31,647 30,804 31,060 26,599 - = No Data Reported; -- = Not Applicable; NA = Not Available; W

  15. Oklahoma Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Oklahoma Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 27,443 1990's 24,547 28,216 28,902 29,118 29,121 29,733 29,733 29,734 30,101 21,790 2000's 21,507 32,672 33,279 34,334 35,612 36,704 38,060 38,364 41,921 43,600 2010's 44,000 51,712 51,472 50,606 50,044 49,852 - = No Data Reported; -- = Not Applicable; NA = Not

  16. Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,701 1990's 2,362 3,392 3,350 3,514 3,565 3,526 4,105 4,156 4,171 4,204 2000's 4,359 4,597 4,803 5,157 5,526 5,523 6,227 6,591 6,860 6,913 2010's 7,026 6,243 6,203 6,174 6,117 6,044 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  17. Arkansas Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Arkansas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,830 1990's 2,952 2,780 3,500 3,500 3,500 3,988 4,020 3,700 3,900 3,650 2000's 4,000 4,825 6,755 7,606 3,460 3,462 3,814 4,773 5,592 6,314 2010's 7,397 8,428 9,012 9,324 9,778 9,965 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. California Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) California Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,214 1990's 1,162 1,377 1,126 1,092 1,261 997 978 930 847 1,152 2000's 1,169 1,244 1,232 1,249 1,272 1,356 1,451 1,540 1,645 1,643 2010's 1,580 4,240 4,356 4,183 4,211 4,209 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. Colorado Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Colorado Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,125 1990's 5,741 5,562 5,912 6,372 7,056 7,017 8,251 12,433 13,838 13,838 2000's 22,442 22,117 23,554 18,774 16,718 22,691 20,568 22,949 25,716 27,021 2010's 28,813 43,792 46,141 46,883 46,876 46,322 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  20. District of Columbia Natural Gas Number of Commercial Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Commercial Consumers (Number of Elements) District of Columbia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 14,683 11,370 11,354 1990's 11,322 11,318 11,206 11,133 11,132 11,089 10,952 10,874 10,658 12,108 2000's 11,106 10,816 10,870 10,565 10,406 10,381 10,410 9,915 10,024 10,288 2010's 9,879 10,050 9,771 9,963 10,049 9,975 - = No Data Reported; -- = Not Applicable; NA = Not

  1. District of Columbia Natural Gas Number of Residential Consumers (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Residential Consumers (Number of Elements) District of Columbia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 134 130,748 134,758 134,837 1990's 136,183 136,629 136,438 135,986 135,119 135,299 135,215 134,807 132,867 137,206 2000's 138,252 138,412 143,874 136,258 138,134 141,012 141,953 142,384 142,819 143,436 2010's 144,151 145,524 145,938 146,712 147,877 147,895 - = No Data

  2. Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,310 1990's 1,307 1,334 1,333 1,336 1,348 1,347 1,367 1,458 1,479 1,498 2000's 1,502 1,533 1,545 2,291 2,386 2,321 2,336 2,350 525 563 2010's 620 914 819 921 895 899 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  3. Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 13,935 1990's 16,980 17,948 18,400 19,472 19,365 22,020 21,388 21,500 21,000 17,568 2000's 15,206 15,357 16,957 17,387 18,120 18,946 19,713 19,713 17,862 21,243 2010's 22,145 25,362 25,013 24,802 24,840 24,451 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  4. Kentucky Natural Gas Number of Gas and Gas Condensate Wells (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Gas and Gas Condensate Wells (Number of Elements) Kentucky Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11,248 1990's 11,713 12,169 12,483 12,836 13,036 13,311 13,501 13,825 14,381 14,750 2000's 13,487 14,370 14,367 12,900 13,920 14,175 15,892 16,563 16,290 17,152 2010's 17,670 12,708 13,179 14,557 NA NA - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  5. U.S. Natural Gas Number of Commercial Consumers - Sales (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) - Sales (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 4,823,842 4,599,494 2000's 4,576,873 4,532,034 4,588,964 4,662,853 4,644,363 4,698,626 4,733,822 2010's 4,584,884 4,556,220 4,518,745 4,491,326 4,528,749 4,559,406 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  6. U.S. Natural Gas Number of Commercial Consumers - Transported (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 220,655 410,695 2000's 433,944 464,412 475,420 489,324 495,586 499,402 539,557 2010's 716,692 763,597 837,652 881,196 884,797 889,774 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016

  7. U.S. Natural Gas Number of Industrial Consumers - Sales (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 182,424 157,050 2000's 157,806 152,974 143,177 142,816 151,386 146,450 135,070 2010's 129,119 124,552 121,821 123,124 122,502 120,426 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release

  8. U.S. Natural Gas Number of Industrial Consumers - Transported (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49,014 71,281 2000's 75,826 64,052 62,738 62,698 57,672 59,773 58,760 2010's 63,611 64,749 67,551 69,164 69,637 68,159 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date:

  9. U.S. Natural Gas Number of Residential Consumers - Sales (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 55,934,175 56,520,482 56,023,710 2000's 56,261,031 56,710,548 57,267,445 57,815,669 58,524,797 59,787,524 60,129,047 2010's 60,267,648 60,408,842 60,010,723 59,877,464 60,189,501 60,921,844 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  10. U.S. Natural Gas Number of Residential Consumers - Transported (Number of

    Energy Information Administration (EIA) (indexed site)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 252,783 801,264 2,199,519 2000's 2,978,319 3,576,181 3,839,809 4,055,781 3,971,337 3,829,303 4,037,233 2010's 5,274,697 5,531,680 6,364,411 6,934,929 7,007,250 6,952,017 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  11. Detailed Chemical Kinetic Reaction Mechanisms for Primary Reference Fuels for Diesel Cetane Number and Spark-Ignition Octane Number

    SciTech Connect

    Westbrook, C K; Pitz, W J; Mehl, M; Curran, H J

    2010-03-03

    For the first time, a detailed chemical kinetic reaction mechanism is developed for primary reference fuel mixtures of n-hexadecane and 2,2,4,4,6,8,8-heptamethyl nonane for diesel cetane ratings. The mechanisms are constructed using existing rules for reaction pathways and rate expressions developed previously for the primary reference fuels for gasoline octane ratings, n-heptane and iso-octane. These reaction mechanisms are validated by comparisons between computed and experimental results for shock tube ignition and for oxidation under jet-stirred reactor conditions. The combined kinetic reaction mechanism contains the submechanisms for the primary reference fuels for diesel cetane ratings and submechanisms for the primary reference fuels for gasoline octane ratings, all in one integrated large kinetic reaction mechanism. Representative applications of this mechanism to two test problems are presented, one describing fuel/air autoignition variations with changes in fuel cetane numbers, and the other describing fuel combustion in a jet-stirred reactor environment with the fuel varying from pure 2,2,4,4,6,8,8-heptamethyl nonane (Cetane number of 15) to pure n-hexadecane (Cetane number of 100). The final reaction mechanism for the primary reference fuels for diesel fuel and gasoline is available on the web.

  12. Fact #851 December 15, 2014 The Average Number of Gears used in Transmissions Continues to Rise

    Energy.gov [DOE]

    The number of gears in a transmission affects a vehicle's fuel economy and performance. The more gears a vehicle has, the more time the engine spends within an optimal operating range while the...

  13. Fact #874: May 25, 2015 Number of Electric Stations and Electric Charging Units Increasing

    Office of Energy Efficiency and Renewable Energy (EERE)

    There are more electric stations than any other alternative fuel (10,710 stations). The number of charging units is of particular importance for electric vehicles due to the length of time it takes...

  14. On the mixing time of geographical threshold graphs

    SciTech Connect

    Bradonjic, Milan

    2009-01-01

    In this paper, we study the mixing time of random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). We specifically study the mixing times of random walks on 2-dimensional GTGs near the connectivity threshold. We provide a set of criteria on the distribution of vertex weights that guarantees that the mixing time is {Theta}(n log n).

  15. Property:NEPA SerialNumber | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    SerialNumber Jump to: navigation, search Property Name NEPA SerialNumber Property Type String This is a property of type String. Pages using the property "NEPA SerialNumber"...

  16. Property:OutagePhoneNumber | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    OutagePhoneNumber Jump to: navigation, search Property Name OutagePhoneNumber Property Type String Description An outage hotline or 24-hour customer service number Note: uses...

  17. Virginia Natural Gas Number of Gas and Gas Condensate Wells ...

    Energy Information Administration (EIA) (indexed site)

    Gas and Gas Condensate Wells (Number of Elements) Virginia Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  18. Unmixing hyperspectral images using Markov random fields

    SciTech Connect

    Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2011-03-14

    This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.

  19. East Tennessee Technology Park by the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    East Tennessee Technology Park by the Numbers East Tennessee Technology Park by the Numbers September 13, 2016 - 12:15pm Addthis East Tennessee Technology Park by the Numbers East Tennessee Technology Park by the Numbers East Tennessee Technology Park by the Numbers East Tennessee Technology Park by the Numbers Statistics associated with decontaminating, decommissioning and demolishing the five gaseous diffusion buildings at the East Tennessee Technology Park. Notable figures from the EM

  20. Property:NumberOfLowEmissionDevelopmentStrategiesExample | Open...

    OpenEI (Open Energy Information) [EERE & EIA]

    issionDevelopmentStrategiesExample Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLowEmissionDevelopmentStrategiesExample&oldid326472...

  1. Property:NumberOfLowEmissionDevelopmentStrategiesExamples | Open...

    OpenEI (Open Energy Information) [EERE & EIA]

    sionDevelopmentStrategiesExamples Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLowEmissionDevelopmentStrategiesExamples&oldid323715...

  2. Property:NumberOfResourceAssessments | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Jump to: navigation, search This is a property of type Number. Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfResourceAssessments&oldid31439...

  3. Property:Number of Plants included in Capacity Estimate | Open...

    OpenEI (Open Energy Information) [EERE & EIA]

    Plants included in Capacity Estimate Jump to: navigation, search Property Name Number of Plants included in Capacity Estimate Property Type Number Retrieved from "http:...

  4. Local Energy Assurance Planning: Map of States with Number of...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    States with Number of Cities Selected Local Energy Assurance Planning: Map of States with Number of Cities Selected Map of the United States identifying the States with cities ...

  5. Synthesis, Structure, and Properties of Alternating and Random...

    Office of Scientific and Technical Information (OSTI)

    Synthesis, Structure, and Properties of Alternating and Random Poly(styrene-b-butadiene) Multiblock Copolymers Citation Details In-Document Search Title: Synthesis, Structure, and...

  6. Dimensions of invariant measures for continuous random dynamical systems

    SciTech Connect

    Bielaczyc, Tomasz; Horbacz, Katarzyna

    2015-03-10

    We consider continuous random dynamical systems with jumps. We estimate the dimension of the invariant measures and apply the results to a model of stochastic gene expression.

  7. Random-matrix approach to the statistical compound nuclear reaction...

    Office of Scientific and Technical Information (OSTI)

    Resource Type: Conference Resource Relation: Conference: Random Matrix Theory, Integrable Systems, and Topology in Physics ; 2015-11-02 - 2015-11-02 ; Stony Brook, New York, United ...

  8. 2015 Salishan Random Access (Conference) | SciTech Connect

    Office of Scientific and Technical Information (OSTI)

    Title: 2015 Salishan Random Access Authors: Vigil, Benny Manuel 1 ; Mattson, Tim 2 ; Coteus, Paul 3 ; Lucas, Bob 4 ; Michalak, Sarah 1 ; Choi, Sung-Eun 5 ; Mountain, ...

  9. Effects of a random spatial variation of the plasma density on the mode conversion in cold, unmagnetized, and stratified plasmas

    SciTech Connect

    Jung Yu, Dae; Kim, Kihong

    2013-12-15

    We study the effects of a random spatial variation of the plasma density on the mode conversion of electromagnetic waves into electrostatic oscillations in cold, unmagnetized, and stratified plasmas. Using the invariant imbedding method, we calculate precisely the electromagnetic field distribution and the mode conversion coefficient, which is defined to be the fraction of the incident wave power converted into electrostatic oscillations, for the configuration where a numerically generated random density variation is added to the background linear density profile. We repeat similar calculations for a large number of random configurations and take an average of the results. We obtain a peculiar nonmonotonic dependence of the mode conversion coefficient on the strength of randomness. As the disorder increases from zero, the maximum value of the mode conversion coefficient decreases initially, then increases to a maximum, and finally decreases towards zero. The range of the incident angle in which mode conversion occurs increases monotonically as the disorder increases. We present numerical results suggesting that the decrease of mode conversion mainly results from the increased reflection due to the Anderson localization effect originating from disorder, whereas the increase of mode conversion of the intermediate disorder regime comes from the appearance of many resonance points and the enhanced tunneling between the resonance points and the cutoff point. We also find a very large local enhancement of the magnetic field intensity for particular random configurations. In order to obtain high mode conversion efficiency, it is desirable to restrict the randomness close to the resonance region.

  10. VARIABLE TIME-INTERVAL GENERATOR

    DOEpatents

    Gross, J.E.

    1959-10-31

    This patent relates to a pulse generator and more particularly to a time interval generator wherein the time interval between pulses is precisely determined. The variable time generator comprises two oscillators with one having a variable frequency output and the other a fixed frequency output. A frequency divider is connected to the variable oscillator for dividing its frequency by a selected factor and a counter is used for counting the periods of the fixed oscillator occurring during a cycle of the divided frequency of the variable oscillator. This defines the period of the variable oscillator in terms of that of the fixed oscillator. A circuit is provided for selecting as a time interval a predetermined number of periods of the variable oscillator. The output of the generator consists of a first pulse produced by a trigger circuit at the start of the time interval and a second pulse marking the end of the time interval produced by the same trigger circuit.

  11. Finite Mach number spherical shock wave, application to shock ignition

    SciTech Connect

    Vallet, A.; Ribeyre, X.; Tikhonchuk, V.

    2013-08-15

    A converging and diverging spherical shock wave with a finite initial Mach number M{sub s0} is described by using a perturbative approach over a small parameter M{sub s}{sup ?2}. The zeroth order solution is the Guderley's self-similar solution. The first order correction to this solution accounts for the effects of the shock strength. Whereas it was constant in the Guderley's asymptotic solution, the amplification factor of the finite amplitude shock ?(t)?dU{sub s}/dR{sub s} now varies in time. The coefficients present in its series form are iteratively calculated so that the solution does not undergo any singular behavior apart from the position of the shock. The analytical form of the corrected solution in the vicinity of singular points provides a better physical understanding of the finite shock Mach number effects. The correction affects mainly the flow density and the pressure after the shock rebound. In application to the shock ignition scheme, it is shown that the ignition criterion is modified by more than 20% if the fuel pressure prior to the final shock is taken into account. A good agreement is obtained with hydrodynamic simulations using a Lagrangian code.

  12. Project Registration Number Assignments (Active) | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Active) Project Registration Number Assignments (Active) As of: May 2016 Provides a table of Project Registration Number Assignments (Active) Project Registration Number Assignment (Active) (511.76 KB) More Documents & Publications All Active DOE Technical Standards Document Project Registration Number Assignments (Completed

  13. Project Registration Number Assignments (Completed) | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Completed) Project Registration Number Assignments (Completed) As of: May 2016 Provides a table of Project Registration Number Assignments (Completed) Project Registration Number Assignments (Completed) (406.85 KB) More Documents & Publications All Active DOE Technical Standards Document Project Registration Number Assignments (Active

  14. A time-series approach to dynamical systems from classical and quantum worlds

    SciTech Connect

    Fossion, Ruben

    2014-01-08

    This contribution discusses some recent applications of time-series analysis in Random Matrix Theory (RMT), and applications of RMT in the statistial analysis of eigenspectra of correlation matrices of multivariate time series.

  15. UGE Scheduler Cycle Time

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    UGE Scheduler Cycle Time UGE Scheduler Cycle Time Genepool Cycle Time Genepool Daily Genepool Weekly Phoebe Cycle Time Phoebe Daily Phoebe Weekly What is the Scheduler Cycle? The...

  16. Random matrices and chaos in nuclear physics: Nuclear structure

    SciTech Connect

    Weidenmueller, H. A.; Mitchell, G. E. [Max-Planck-Institut fuer Kernphysik, D-69029 Heidelberg (Germany); North Carolina State University, Raleigh, North Carolina 27695 (United States) and Triangle Universities Nuclear Laboratory, Durham, North Carolina 27706 (United States)

    2009-04-15

    Evidence for the applicability of random-matrix theory to nuclear spectra is reviewed. In analogy to systems with few degrees of freedom, one speaks of chaos (more accurately, quantum chaos) in nuclei whenever random-matrix predictions are fulfilled. An introduction into the basic concepts of random-matrix theory is followed by a survey over the extant experimental information on spectral fluctuations, including a discussion of the violation of a symmetry or invariance property. Chaos in nuclear models is discussed for the spherical shell model, for the deformed shell model, and for the interacting boson model. Evidence for chaos also comes from random-matrix ensembles patterned after the shell model such as the embedded two-body ensemble, the two-body random ensemble, and the constrained ensembles. All this evidence points to the fact that chaos is a generic property of nuclear spectra, except for the ground-state regions of strongly deformed nuclei.

  17. Search for baryon-number and lepton-number violating decays of $Lambda$ hyperons using the CLAS detector at Jefferson Laboratory

    SciTech Connect

    McCracken, Michael E.

    2015-10-09

    We present a search for ten baryon-number violating decay modes of $\\Lambda$ hyperons using the CLAS detector at Jefferson Laboratory. Nine of these decay modes result in a single meson and single lepton in the final state ($\\Lambda \\rightarrow m \\ell$) and conserve either the sum or the difference of baryon and lepton number ($B \\pm L$). The tenth decay mode ($\\Lambda \\rightarrow \\bar{p}\\pi^+$) represents a difference in baryon number of two units and no difference in lepton number. We observe no significant signal and set upper limits on the branching fractions of these reactions in the range $(4-200)\\times 10^{-7}$ at the $90\\%$ confidence level.

  18. Search for baryon-number and lepton-number violating decays of $Lambda$ hyperons using the CLAS detector at Jefferson Laboratory

    DOE PAGES [OSTI]

    McCracken, Michael E.

    2015-10-09

    We present a search for ten baryon-number violating decay modes of $\\Lambda$ hyperons using the CLAS detector at Jefferson Laboratory. Nine of these decay modes result in a single meson and single lepton in the final state ($\\Lambda \\rightarrow m \\ell$) and conserve either the sum or the difference of baryon and lepton number ($B \\pm L$). The tenth decay mode ($\\Lambda \\rightarrow \\bar{p}\\pi^+$) represents a difference in baryon number of two units and no difference in lepton number. We observe no significant signal and set upper limits on the branching fractions of these reactions in the range $(4-200)\\times 10^{-7}$moreat the $90\\%$ confidence level.less

  19. System and method for simultaneously collecting serial number information from numerous identity tags

    DOEpatents

    Doty, Michael A.

    1997-01-01

    A system and method for simultaneously collecting serial number information reports from numerous colliding coded-radio-frequency identity tags. Each tag has a unique multi-digit serial number that is stored in non-volatile RAM. A reader transmits an ASCII coded "D" character on a carrier of about 900 MHz and a power illumination field having a frequency of about 1.6 Ghz. A one MHz tone is modulated on the 1.6 Ghz carrier as a timing clock for a microprocessor in each of the identity tags. Over a thousand such tags may be in the vicinity and each is powered-up and clocked by the 1.6 Ghz power illumination field. Each identity tag looks for the "D" interrogator modulated on the 900 MHz carrier, and each uses a digit of its serial number to time a response. Clear responses received by the reader are repeated for verification. If no verification or a wrong number is received by any identity tag, it uses a second digital together with the first to time out a more extended period for response. Ultimately, the entire serial number will be used in the worst case collision environments; and since the serial numbers are defined as being unique, the final possibility will be successful because a clear time-slot channel will be available.

  20. System and method for simultaneously collecting serial number information from numerous identity tags

    DOEpatents

    Doty, M.A.

    1997-01-07

    A system and method are disclosed for simultaneously collecting serial number information reports from numerous colliding coded-radio-frequency identity tags. Each tag has a unique multi-digit serial number that is stored in non-volatile RAM. A reader transmits an ASCII coded ``D`` character on a carrier of about 900 MHz and a power illumination field having a frequency of about 1.6 Ghz. A one MHz tone is modulated on the 1.6 Ghz carrier as a timing clock for a microprocessor in each of the identity tags. Over a thousand such tags may be in the vicinity and each is powered-up and clocked by the 1.6 Ghz power illumination field. Each identity tag looks for the ``D`` interrogator modulated on the 900 MHz carrier, and each uses a digit of its serial number to time a response. Clear responses received by the reader are repeated for verification. If no verification or a wrong number is received by any identity tag, it uses a second digital together with the first to time out a more extended period for response. Ultimately, the entire serial number will be used in the worst case collision environments; and since the serial numbers are defined as being unique, the final possibility will be successful because a clear time-slot channel will be available. 5 figs.

  1. Scaling laws of resistive magnetohydrodynamic reconnection in the high-Lundquist-number, plasmoid-unstable regime

    SciTech Connect

    Huang Yimin; Bhattacharjee, A.

    2010-06-15

    The Sweet-Parker layer in a system that exceeds a critical value of the Lundquist number (S) is unstable to the plasmoid instability. In this paper, a numerical scaling study has been done with an island coalescing system driven by a low level of random noise. In the early stage, a primary Sweet-Parker layer forms between the two coalescing islands. The primary Sweet-Parker layer breaks into multiple plasmoids and even thinner current sheets through multiple levels of cascading if the Lundquist number is greater than a critical value S{sub c}approx =4x10{sup 4}. As a result of the plasmoid instability, the system realizes a fast nonlinear reconnection rate that is nearly independent of S, and is only weakly dependent on the level of noise. The number of plasmoids in the linear regime is found to scales as S{sup 3/8}, as predicted by an earlier asymptotic analysis [N. F. Loureiro et al., Phys. Plasmas 14, 100703 (2007)]. In the nonlinear regime, the number of plasmoids follows a steeper scaling, and is proportional to S. The thickness and length of current sheets are found to scale as S{sup -1}, and the local current densities of current sheets scale as S{sup -1}. Heuristic arguments are given in support of theses scaling relations.

  2. Artifacts in digital coincidence timing

    DOE PAGES [OSTI]

    Moses, W. W.; Peng, Q.

    2014-10-16

    Digital methods are becoming increasingly popular for measuring time differences, and are the de facto standard in PET cameras. These methods usually include a master system clock and a (digital) arrival time estimate for each detector that is obtained by comparing the detector output signal to some reference portion of this clock (such as the rising edge). Time differences between detector signals are then obtained by subtracting the digitized estimates from a detector pair. A number of different methods can be used to generate the digitized arrival time of the detector output, such as sending a discriminator output into amore » time to digital converter (TDC) or digitizing the waveform and applying a more sophisticated algorithm to extract a timing estimator.All measurement methods are subject to error, and one generally wants to minimize these errors and so optimize the timing resolution. A common method for optimizing timing methods is to measure the coincidence timing resolution between two timing signals whose time difference should be constant (such as detecting gammas from positron annihilation) and selecting the method that minimizes the width of the distribution (i.e. the timing resolution). Unfortunately, a common form of error (a nonlinear transfer function) leads to artifacts that artificially narrow this resolution, which can lead to erroneous selection of the 'optimal' method. In conclusion, the purpose of this note is to demonstrate the origin of this artifact and suggest that caution should be used when optimizing time digitization systems solely on timing resolution minimization.« less

  3. TCES. Time Card Entry System

    SciTech Connect

    Montierth, B.

    1996-05-01

    The Time Card Entry System was developed for the Department of Enegy, Idaho Operations Office (DOE-ID) to interface with the DOE headquarters (DOE-HQ) Electronic Time and Attendance (ETA) system for payroll. It features pop-up window pick lists for Work Breakdown Structure numbers and Hour Codes and has extensive processing that ensures that time and attendance reported by the employee fulfills U.S. Government/OMB requirements before Timekeepers process the data at the end of the two week payroll cycle using ETA. A tour of duty profile (e.g., ten hour day, four day week with Sunday, friday and Saturday off), previously established in the ETA system, is imported into the Time Card Entry System by the timekeepers. An individual`s profile establishes the basis for validation of time of day and number of hours worked per day. At the end of the two cycle, data is exported by the timekeepers from the Time Card Entry System into ETA files.

  4. Export support of renewable energy industries. Task number 1, deliverable number 3. Final report

    SciTech Connect

    1998-01-14

    The United States Export Council for Renewable Energy (US/ECRE), a consortium of six industry associations, promotes the interests of the renewable energy and energy efficiency member companies which provide goods and services in biomass, geothermal, hydropower, passive solar, photovoltaics, solar thermal, wind, wood energy, and energy efficiency technologies. US/ECRE`s mission is to catalyze export markets for renewable energy and energy efficiency technologies worldwide. Under this grant, US/ECRE has conducted a number of in-house activities, as well as to manage activities by member trade associations, affiliate organizations and non-member contractors and consultants. The purpose of this document is to report on task coordination and effectiveness.

  5. Export support of renewable energy industries, grant number 1, deliverable number 3. Final report

    SciTech Connect

    1998-01-14

    The United States Export Council for Renewable Energy (US/ECRE), a consortium of six industry associations, promotes the interests of the renewable energy and energy efficiency member companies which provide goods and services in biomass, geothermal, hydropower, passive solar, photovoltaics, solar thermal, wind, wood energy, and energy efficiency technologies. US/ECRE`s mission is to catalyze export markets for renewable energy and energy efficiency technologies worldwide. Under this grant, US/ECRE has conducted a number of in-house activities, as well as to manage activities by member trade associations, affiliate organizations and non-member contractors and consultants. The purpose of this document is to report on grant coordination and effectiveness.

  6. Phone Numbers for Beam Lines and Other Services | Stanford Synchrotron

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Radiation Lightsource Phone Numbers for Beam Lines and Other Services The local area code for SSRL is 650. All numbers listed below should be dialed as 650-926-xxxx from other area codes. When calling an onsite location from within SSRL simply dial the 4-digit extension. When calling an offsite number within the 650 area code dial, dial 9 plus the 7-digit number. To call a number in another area code dial 9-1-area code - phone number. Beam Lines Beam Line Extension 1-4 5214 1-5 5215 2-1 5221

  7. Multiplexer and time duration measuring circuit

    DOEpatents

    Gray, Jr., James

    1980-01-01

    A multiplexer device is provided for multiplexing data in the form of randomly developed, variable width pulses from a plurality of pulse sources to a master storage. The device includes a first multiplexer unit which includes a plurality of input circuits each coupled to one of the pulse sources, with all input circuits being disabled when one input circuit receives an input pulse so that only one input pulse is multiplexed by the multiplexer unit at any one time.

  8. Property:ASHRAE 169 Climate Zone Number | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    5 + Adair County, Oklahoma ASHRAE 169-2006 Climate Zone + Climate Zone Number 3 + Adams County, Colorado ASHRAE 169-2006 Climate Zone + Climate Zone Number 5 + Adams County,...

  9. Multicanonical sampling of rare events in random matrices

    SciTech Connect

    Saito, Nen; Iba, Yukito; Hukushima, Koji

    2010-09-15

    A method based on multicanonical Monte Carlo is applied to the calculation of large deviations in the largest eigenvalue of random matrices. The method is successfully tested with the Gaussian orthogonal ensemble, sparse random matrices, and matrices whose components are subject to uniform density. Specifically, the probability that all eigenvalues of a matrix are negative is estimated in these cases down to the values of {approx}10{sup -200}, a region where simple random sampling is ineffective. The method can be applied to any ensemble of matrices and used for sampling rare events characterized by any statistics.

  10. Optimization of the random multilayer structure to break the random-alloy limit of thermal conductivity

    SciTech Connect

    Wang, Yan; Gu, Chongjie; Ruan, Xiulin

    2015-02-16

    A low lattice thermal conductivity (κ) is desired for thermoelectrics, and a highly anisotropic κ is essential for applications such as magnetic layers for heat-assisted magnetic recording, where a high cross-plane (perpendicular to layer) κ is needed to ensure fast writing while a low in-plane κ is required to avoid interaction between adjacent bits of data. In this work, we conduct molecular dynamics simulations to investigate the κ of superlattice (SL), random multilayer (RML) and alloy, and reveal that RML can have 1–2 orders of magnitude higher anisotropy in κ than SL and alloy. We systematically explore how the κ of SL, RML, and alloy changes relative to each other for different bond strength, interface roughness, atomic mass, and structure size, which provides guidance for choosing materials and structural parameters to build RMLs with optimal performance for specific applications.

  11. Idaho Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Idaho Site Cleanup By the Numbers Idaho Site Cleanup By the Numbers Idaho Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Idaho Site Cleanup By the Numbers

  12. Moab Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Moab Site Cleanup By the Numbers Moab Site Cleanup By the Numbers Moab Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Moab Site Cleanup By the Numbers (1.32

  13. ARM Evaluation Product : Droplet Number Concentration Value-Added Product

    Office of Scientific and Technical Information (OSTI)

    (Dataset) | Data Explorer Evaluation Product : Droplet Number Concentration Value-Added Product Title: ARM Evaluation Product : Droplet Number Concentration Value-Added Product Cloud droplet number concentration is an important factor in understanding aerosol-cloud interactions. As aerosol concentration increases, it is expected that droplet number concentration, Nd, will increase and droplet size decrease, for a given liquid water path (Twomey 1977), which will greatly affect cloud albedo

  14. Improving Ramsey spectroscopy in the extreme-ultraviolet region with a random-sampling approach

    SciTech Connect

    Eramo, R.; Bellini, M. [Istituto Nazionale di Ottica (INO-CNR), Largo E. Fermi 6, I-50125 Florence (Italy); European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Corsi, C.; Liontos, I. [European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Cavalieri, S. [European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Department of Physics, University of Florence, I-50019 Sesto Fiorentino, Florence (Italy)

    2011-04-15

    Ramsey-like techniques, based on the coherent excitation of a sample by delayed and phase-correlated pulses, are promising tools for high-precision spectroscopic tests of QED in the extreme-ultraviolet (xuv) spectral region, but currently suffer experimental limitations related to long acquisition times and critical stability issues. Here we propose a random subsampling approach to Ramsey spectroscopy that, by allowing experimentalists to reach a given spectral resolution goal in a fraction of the usual acquisition time, leads to substantial improvements in high-resolution spectroscopy and may open the way to a widespread application of Ramsey-like techniques to precision measurements in the xuv spectral region.

  15. Stochastic Seismic Response of an Algiers Site with Random Depth to Bedrock

    SciTech Connect

    Badaoui, M.; Mebarki, A.; Berrah, M. K.

    2010-05-21

    Among the important effects of the Boumerdes earthquake (Algeria, May 21{sup st} 2003) was that, within the same zone, the destructions in certain parts were more important than in others. This phenomenon is due to site effects which alter the characteristics of seismic motions and cause concentration of damage during earthquakes. Local site effects such as thickness and mechanical properties of soil layers have important effects on the surface ground motions.This paper deals with the effect of the randomness aspect of the depth to bedrock (soil layers heights) which is assumed to be a random variable with lognormal distribution. This distribution is suitable for strictly non-negative random variables with large values of the coefficient of variation. In this case, Monte Carlo simulations are combined with the stiffness matrix method, used herein as a deterministic method, for evaluating the effect of the depth to bedrock uncertainty on the seismic response of a multilayered soil. This study considers a P and SV wave propagation pattern using input accelerations collected at Keddara station, located at 20 km from the epicenter, as it is located directly on the bedrock.A parametric study is conducted do derive the stochastic behavior of the peak ground acceleration and its response spectrum, the transfer function and the amplification factors. It is found that the soil height heterogeneity causes a widening of the frequency content and an increase in the fundamental frequency of the soil profile, indicating that the resonance phenomenon concerns a larger number of structures.

  16. Social Security Number Reduction Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Social Security Number Reduction Project Social Security Number Reduction Project The document below provides information regarding acceptable uses of the Social Security Number (SSN). Baseline Inventory.pdf (23.65 KB) More Documents & Publications DOE Guidance on the Use of the SSN Manchester Software 1099 Reporting PIA, Idaho National Laboratory Occupational Medicine - Assistant PIA, Idaho National Laboratory

  17. On timing properties of LYSO-based calorimeters (Journal Article...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: On timing properties of LYSO-based calorimeters Citation Details ... Report Number(s): FERMILAB-PUB--15-243-PPD Journal ID: ISSN 0168-9002; PII: ...

  18. Toxic Substances Control Act (TSCA) chemical substances inventory: PMN number to EPA accession number link (for microcomputers). Data file

    SciTech Connect

    1995-11-01

    The PMN Number to EPA Accession Number Link Diskette provides a cross-reference of these numbers for commenced PMNs on the confidential portion of the TSCA Master Inventory File. Neither this cross-reference nor the additional information included is TSCA Confidential Business Information. Provided on the diskette for each confidential commenced PMN are the PMN Case Number, EPA Accession Number, Generic Name, and EPA special flags. The sequence of the file is in ascending PMN Case Number order with `P` case numbers sorted first, followed by `Y` case numbers. For more detailed information on the confidential portion of the TSCA Inventory, including generic names, users can consult the introductory material of the printed TSCA Inventory: 1985 Edition and its 1990 Supplement. New versions of this file may be issued in the future. No search software is provided with this DOS formatted diskette.

  19. Kubo relations and radiative corrections for lepton number washout

    SciTech Connect

    Bdeker, Dietrich; Laine, M. E-mail: laine@itp.unibe.ch

    2014-05-01

    The rates for lepton number washout in extensions of the Standard Model containing right-handed neutrinos are key ingredients in scenarios for baryogenesis through leptogenesis. We relate these rates to real-time correlation functions at finite temperature, without making use of any particle approximations. The relations are valid to quadratic order in neutrino Yukawa couplings and to all orders in Standard Model couplings. They take into account all spectator processes, and apply both in the symmetric and in the Higgs phase of the electroweak theory. We use the relations to compute washout rates at next-to-leading order in g, where g denotes a Standard Model gauge or Yukawa coupling, both in the non-relativistic and in the relativistic regime. Even in the non-relativistic regime the parametrically dominant radiative corrections are only suppressed by a single power of g. In the non-relativistic regime radiative corrections increase the washout rate by a few percent at high temperatures, but they are of order unity around the weak scale and in the relativistic regime.

  20. Random and Block Sulfonated Polyaramides as Advanced Proton Exchange

    Office of Scientific and Technical Information (OSTI)

    Membranes (Journal Article) | SciTech Connect Random and Block Sulfonated Polyaramides as Advanced Proton Exchange Membranes Citation Details In-Document Search Title: Random and Block Sulfonated Polyaramides as Advanced Proton Exchange Membranes Presented here is the experimental and computational characterization of two novel copolyaramide proton exchange membranes (PEMs) with higher conductivity than Nafion at relatively high temperatures, good mechanical properties, high thermal

  1. Deviations from the Gutenberg–Richter law on account of a random distribution of block sizes

    SciTech Connect

    Sibiryakov, B. P.

    2015-10-27

    This paper studies properties of a continuum with structure. The characteristic size of the structure governs the fact that difference relations are nonautomatically transformed into differential ones. It is impossible to consider an infinitesimal volume of a body, to which the major conservation laws could be applied, because the minimum representative volume of the body must contain at least a few elementary microstructures. The corresponding equations of motion are equations of infinite order, solutions of which include, along with usual sound waves, unusual waves with abnormally low velocities without a lower limit. It is shown that in such media weak perturbations can increase or decrease outside the limits. The number of complex roots of the corresponding dispersion equation, which can be interpreted as the number of unstable solutions, depends on the specific surface of cracks and is an almost linear dependence on a logarithmic scale, as in the seismological Gutenberg–Richter law. If the distance between one pore (crack) to another one is a random value with some distribution, we must write another dispersion equation and examine different scenarios depending on the statistical characteristics of the random distribution. In this case, there are sufficient deviations from the Gutenberg–Richter law and this theoretical result corresponds to some field and laboratory observations.

  2. Count-doubling time safety circuit

    DOEpatents

    Rusch, Gordon K.; Keefe, Donald J.; McDowell, William P.

    1981-01-01

    There is provided a nuclear reactor count-factor-increase time monitoring circuit which includes a pulse-type neutron detector, and means for counting the number of detected pulses during specific time periods. Counts are compared and the comparison is utilized to develop a reactor scram signal, if necessary.

  3. Quantifying the number of color centers in single fluorescent nanodiamonds by photon correlation spectroscopy and Monte Carlo simulation

    SciTech Connect

    Hui, Y.Y.; Chang, Y.-R.; Lee, H.-Y.; Chang, H.-C.; Lim, T.-S.; Fann Wunshain

    2009-01-05

    The number of negatively charged nitrogen-vacancy centers (N-V){sup -} in fluorescent nanodiamond (FND) has been determined by photon correlation spectroscopy and Monte Carlo simulations at the single particle level. By taking account of the random dipole orientation of the multiple (N-V){sup -} fluorophores and simulating the probability distribution of their effective numbers (N{sub e}), we found that the actual number (N{sub a}) of the fluorophores is in linear correlation with N{sub e}, with correction factors of 1.8 and 1.2 in measurements using linearly and circularly polarized lights, respectively. We determined N{sub a}=8{+-}1 for 28 nm FND particles prepared by 3 MeV proton irradiation.

  4. Savannah River National Laboratory By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    National Laboratory By the Numbers Savannah River National Laboratory By the Numbers Savannah River National Laboratory By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download

  5. Savannah River Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Site Cleanup By the Numbers Savannah River Site Cleanup By the Numbers Savannah River Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Savannah River Site

  6. Separations Process Research Unit (SPRU) Site Cleanup By the Numbers |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Separations Process Research Unit (SPRU) Site Cleanup By the Numbers Separations Process Research Unit (SPRU) Site Cleanup By the Numbers Separations Process Research Unit (SPRU) Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of

  7. Energy Technology Engineering Center (ETEC) Cleanup By the Numbers |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Energy Technology Engineering Center (ETEC) Cleanup By the Numbers Energy Technology Engineering Center (ETEC) Cleanup By the Numbers Energy Technology Engineering Center (ETEC) Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of

  8. Oak Ridge Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Oak Ridge Site Cleanup By the Numbers Oak Ridge Site Cleanup By the Numbers Oak Ridge Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Oak Ridge Site Cleanup By

  9. Paducah Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Paducah Site Cleanup By the Numbers Paducah Site Cleanup By the Numbers Paducah Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Paducah Site Cleanup By the

  10. Portsmouth Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Portsmouth Site Cleanup By the Numbers Portsmouth Site Cleanup By the Numbers Portsmouth Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Portsmouth Site

  11. Hanford Site Cleanup By the Numbers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Hanford Site Cleanup By the Numbers Hanford Site Cleanup By the Numbers Hanford Site Cleanup By the Numbers In 2015, EM developed site infographics highlighting each sites history and important metrics including: Decontamination and demolition of facilities and waste sites Secure storage of spent fuel Retrieval of radioactive sludge and saltcake from tanks Treatment of contaminated groundwater Waste safely stored in an underground repository Available for Download Hanford Site Cleanup By the

  12. Prediction of cloud droplet number in a general circulation model

    SciTech Connect

    Ghan, S.J.; Leung, L.R.

    1996-04-01

    We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

  13. Developing and Enhancing Workforce Training Programs: Number of Projects by

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    State | Department of Energy Developing and Enhancing Workforce Training Programs: Number of Projects by State Developing and Enhancing Workforce Training Programs: Number of Projects by State Map of the United States showing the location of Workforce Training Projects, funded through the American Recovery and Reinvestment Act Developing and Enhancing Workforce Training Programs: Number of Projects by State (389.21 KB) More Documents & Publications Workforce Development Wind Projects

  14. ORISE: Report shows number of health physics degrees for 2010

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    report shows number of health physics degrees increased for graduates, decreased for undergraduates in 2010 Decreased number of B.S. degrees remains higher than levels in the early 2000 FOR IMMEDIATE RELEASE Dec. 20, 2011 FY12-09 OAK RIDGE, Tenn.-The number of health physics graduate degrees increased for both master's and doctoral candidates in 2010, but decreased for bachelor's degrees, says a report released this year by the Oak Ridge Institute for Science and Education. The ORISE report,

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    SciTech Connect

    Engelhardt, M.E.

    1994-09-01

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    U.S. Department of Energy (DOE) - all webpages (Extended Search)

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    Office of Scientific and Technical Information (OSTI)

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    U.S. Department of Energy (DOE) - all webpages (Extended Search)

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    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

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    Office of Environmental Management (EM)

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