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  1. Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

    Ku, I-Ting; Zhou, Yong; Hecobian, Arsineh; ... - Atmospheric Environment (1994)
    Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful casemore » to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8–C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C8–C10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.« less

  2. Operational and Mission Highlights A Monthly Summary of Top Achievements December 2023

    Anaya, Lillian; Adkins, Kenneth
    This report contains a summary of highlights and top achievements from Los Alamos National Laboratory from December 2023, including Nuclear Security, Science Technology & Engineering, Mission Operations, and Community Relations.

  3. ANS-8 Nuclear Criticality Safety Consensus Standards -- Current Initiatives

    Bowen, Douglas
    The nuclear criticality safety (NCS) consensus standards are developed as using rigorous procedures of the Standards Board of the American Nuclear Society. These procedures have been accredited by the American National Standards Institute, Inc., as meeting the criteria for American National Standards. The Nuclear Criticality Safety Consensus Committee (NCSCC) that approved all 18 NCS consensus standards is balanced to ensure that competent, concerned, and varied interests have had an opportunity to participate. The ANS-8 subcommittee (ANS-8) consists of 17 NCS experts with many years of experience as end users of ANS standards who serve on standard working groups to developmore » and maintain standards. ANS-8 ensures that the technical content of the standards is adequate for NCS community use. Attempts are made to ensure that ANS-8 consists of NCS professionals with a diverse range of experience such that all standards are applicable to as many sites as possible. ANS-8 is a very active subcommittee, and some active projects in progress are discussed in this paper: basis statement development for all standards, development of a glossary for consistency of definitions across all ANS-8 standards, and Considering the Criticality Safety Support Group (CSSG) Recommendation 2016-04 to the ANS Standards Board for changes in several ANS-8 standards.« less

  4. Stochastic finite volume method for uncertainty quantification of transient flow in gas pipeline networks

    Tokareva, S.; Zlotnik, A.; Gyrya, V. - Applied Mathematical Modelling
    We develop a weakly intrusive framework to simulate the propagation of uncertainty in solutions of generic hyperbolic partial differential equation systems on graph-connected domains with nodal coupling and boundary conditions. The method is based on the Stochastic Finite Volume (SFV) approach and can be applied for uncertainty quantification (UQ) of the dynamical state of fluid flow over actuated transport networks. The numerical scheme has specific advantages for modeling intertemporal uncertainty in time-varying boundary parameters, which cannot be characterized by strict upper and lower (interval) bounds. We describe the scheme for a single pipe, and then formulate the controlled junction Riemannmore » problem (JRP) that enables the extension to general network structures. In conclusion, we demonstrate the method's capabilities and performance characteristics using a standard benchmark test network.« less

  5. Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

    Petkov, Plamen
    In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolutionmore » of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.« less

  6. AI for Technoscientific Discovery: A Human-Inspired Architecture

    Tsao, J.; Abbott, R.; Crowder, D.; ... - Journal of Creativity (Online)
    We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete:more » its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.« less

  7. Mandrel diameter effect on ring-pull testing of nuclear fuel cladding

    Beck, Peter; Hayne, Mathew; Liu, Cheng; ... - Journal of Nuclear Materials
    Inconsistencies in ring-pull testing methods for thin-walled tubes make it difficult to compare a material's mechanical properties in the hoop direction presented in past publications. The effect of test setup, specifically mandrel diameter, was investigated for nuclear fuel cladding tubes of the iron-chromium-aluminium (FeCrAl) alloy, C26M (Fe-12Cr-6Al-2Mo). Mandrel diameter for ring-pull testing can change how the ring deforms and friction effects, further convoluting the results from testing. To address the impact of mandrel size in relation to ring diameter, gaugeless ring-pull samples were tested across a range of mandrel diameters and compared to uniaxial tensile tests from the same tubemore » of material. Two groups of samples were tested: an as-received, low ductility sample set and an annealed, high ductility sample set, both cut from the same extruded tube. By pairing systematic variation of mandrel geometry with analysis of local strain gradients using digital image correlation, this study investigates the effect of test fixture geometry on apparent mechanical properties and effective strain fields and provides guidance for comparing previously published data. Certain features in uniaxial stress-strain curves (namely the yield strength, ultimate tensile strength, and uniform elongation) appeared to have analogies in the effective stress-strain curves from ring-pull testing. These corresponding features are compared to expose biases in the analysis of material properties using gaugeless ring-pull and to provide novel guidance on test setup during experimental design.« less

  8. The present and future of QCD

    Achenbach, P.; Adhikari, D.; Afanasev, A.; ... - Nuclear Physics. A
    This White Paper presents the community inputs and scientific conclusions from the Hot and Cold QCD Town Meeting that took place September 23-25, 2022 at MIT, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 424 physicists registered for the meeting. The meeting highlighted progress in Quantum Chromodynamics (QCD) nuclear physics since the 2015 LRP (LRP15) and identified key questions and plausible paths to obtaining answers to those questions, defining priorities for our research over the coming decade. In defining the priority of outstanding physics opportunities for the future, both prospects formore » the short (~ 5 years) and longer term (5-10 years and beyond) are identified together with the facilities, personnel and other resources needed to maximize the discovery potential and maintain United States leadership in QCD physics worldwide. This White Paper is organized as follows: In the Executive Summary, we detail the Recommendations and Initiatives that were presented and discussed at the Town Meeting, and their supporting rationales. Section 2 highlights major progress and accomplishments of the past seven years. It is followed, in Section 3, by an overview of the physics opportunities for the immediate future, and in relation with the next QCD frontier: the EIC. Section 4 provides an overview of the physics motivations and goals associated with the EIC. Section 5 is devoted to the workforce development and support of diversity, equity and inclusion. This is followed by a dedicated section on computing in Section 6. Section 7 describes the national need for nuclear data science and the relevance to QCD research.« less

  9. Recovery of valuable metals from electronic waste using a novel ammonia-based hydrometallurgical process

    Lin, Peijia
    The growing quantity of waste electrical and electronic equipment (WEEE), also known as electronic waste (E-waste), has been an area of growing public concern. The abundance of valuable metals contained in waste printed circuit boards (WPCBs) has made it a promising secondary resource, especially for Cu and Au. Although the recovery of metals from WPCBs via hydrometallurgical routes has been studied extensively over the past 20 years, most of the research has been limited in the laboratory. In current hydrometallurgical processes, strong acids and expensive oxidizers are often used to ensure a high recovery of metals without considering the sustainabilitymore » aspects of the environment and economics. To improve upon current hydrometallurgical offerings, the current study seeks to develop an energy-saving, environmentally friendly, economic and sustainable process to efficiently recover the valuable metals from real-world end-of-life WPCBs. The new contributions presented in this study are 1) design and evaluation of a comprehensive hydrometallurgical flowsheet; 2) employment of real end-of-life PCBs as feed materials in an investigation on Cu-NH3 leaching kinetics; 3) further application of kinetic model on a counter flow process simulation; and 4) evaluation of the influences by co-existing metals in Au-S2O3 leaching and recommendation for favorable leaching conditions.« less

  10. Quantum Annealing for Real-World Machine Learning Applications

    Kumar nath, Rajdeep; Thapliyal, Himanshu; Humble, Travis
    Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data andmore » high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.« less
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