24 Search Results

We present an ab initio free energy model derived from a fully relativistic density functional theory (DFT) electronic structure with dynamic magnetism for δ-plutonium (face-centered cubic, fcc). The DFT model is extended with orbital-orbital interaction in a parameter free orbital polarization (OP) mechanism consistent with previous modeling of plutonium. Gibbs free energy is built from components associated with the temperature dependence of the electronic structure and the corresponding electronic entropy, lattice vibrations within an anharmonic lattice dynamics model, and dynamical fluctuations of the magnetization density, i.e. magnetic fluctuations. The fluctuation model consists of transverse and longitudinal modes driven by temperaturemore » induced excitations of the DFT + OP electronic structure. The ab initio model thus incorporates fluctuating states beyond the electronic ground state. Thanks to the dynamic magnetism, the theory predicts excellent thermodynamic properties and a Gibbs free energy in accord with CALPHAD and semi-empirical modeling developed from the thermodynamic observables. The magnetic fluctuations further explain anomalous behaviors of the thermal expansion in plutonium. Specifically, a thermal expansion for the δ-plutonium system turning from positive to negative at temperatures above room temperature, a tendency for gallium to reduce and remove the negative thermal expansion depending on composition, and a positive thermal expansion for the high temperature ϵ phase.« less

The small modular reactor (SMR) is a promising option with added safety features, economical manufacturing, reliable parts, portability, and scalable energy capacity that emits no greenhouse gas during its operating lifespan. The SMR safety systems, however, need to be evaluated for design and licensing. Thus, they require the verification and validation of the physics models and correlations. This study focuses on state-of-the-art condensation heat transfer analysis and a review of previous studies related to the passive containment cooling system (PCCS) of a SMR. In the PCCS of a SMR, due to its smaller size containment, filmwise condensation is dominant andmore » therefore emphasized in this study. Furthermore, previous condensation heat transfer studies for PCCSs did not make the SMR the primary focus, so a critical review for formulating the state-of-the-art is necessary. A previous review covered experimental condensation heat transfer studies with a brief overview of associated test facilities and empirical correlations. This review covers the empirical, resistance-layer and theoretical (numerical and commercial CFD) approaches.« less

Here this paper provides a literature review of methods and modeling techniques to estimate the cost of power system outages, along with the value of outage mitigation or system resilience. Regulators, policymakers, and infrastructure owners have a growing need to understand the methods for estimating the benefits of resilience improvements of electric infrastructure against natural and man-made disasters. There is a broad literature that estimates the cost of short-duration outages and a small but developing literature on estimating the cost of long-duration outages. This article reviews the models used to estimate the cost of outages and discusses their relative strengths.more » Additionally, this paper identifies key questions from stakeholders regarding resilience investment and maps them to the relevant models that would help answer them. We include recommendations for future work to include recent advances in regional economic modeling that can estimate region and demographic-specific costs and the distributional consequences of potential resilience projects.« less

Beavers (Castor) stand out among mammals for their unique capacity to fell trees using their large, ever-growing incisors. This routine consumption of resistant fodder induces prodigious wear in the lower incisors, despite this blunting effect the incisors maintain a remarkably sharp cutting edge. Notably, the enamel edges of their incisors show a highly complex two-part microstructure of which the biomechanical import is unknown. Here, using fracture analysis, nanoindentation, and wear testing on North American beaver (C. canadensis) incisors we test the microstructure's possible contribution to maintaining incisal sharpness. Although comparable in hardness, the inner enamel preferentially fails and readily wearsmore » at 2.5 times the rate of the outer enamel. The outer microstructure redirects all fractures in parallel, decreasing fracture coalescence. Conversely, the inner microstructure facilitates crack coalescence increasing the wear rate by isolating layers of enamel prisms that readily fragment. Together these two architectures form a microstructurally driven self-sharpening mechanism contained entirely within the thin enamel shell. Here, our results demonstrate that enamel microstructures exposed at the occlusal surface can markedly influence both enamel crest shape and surface texture in wearing dentitions. The methods introduced here open the door to exploring the biomechanical functionality and evolution of enamel microstructures throughout Mammalia.« less

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models formore » analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.« less

Here this study focused on state-of-the-art and review of condensation heat transfer for small modular reactors (SMR). Nuclear reactors adopt passive containment cooling systems (PCCS) for accident mitigation, containment integrity, and primarily to maintain the last barrier for radioactive particle release to the environment during and beyond design-basis accidents. However, improving the effectiveness of the PCCS is more critical for the SMR than for commercial reactors to ensure higher safety margins and compactness. In the PCCS of SMR, due to its smaller size containment, the filmwise condensation (FWC) is dominant. Therefore, this study emphasized the FWC. Earlier condensation studies formore » the PCCS did not make SMR the primary focus, so a critical review for formulating the state-of-the-art was necessary. Part-1 of this study covered the review of physics phenomena, previous experimental studies with a brief overview of associated test facilities and empirical correlations. This study identified a research gap with the condensation test data scaling relations by using the information and findings of the previous PCCS studies and applied them to the SMR system.« less

We report nuclear reaction data for neutron induced reactions on unstable nuclei are critical for a wide range of applications spanning studies of nuclear astrophysics, nuclear reactor designs, and radiochemistry diagnostics. However, nuclear data evaluations of the reaction cross sections are largely based on calculations due to the difficulty in performing this class of measurements and the resulting lack of experimental data. For neutron induced charged particle reactions at fast neutron energies, at the MeV scale, these cross section predictions are predominately driven by statistical Hauser-Feshbach calculations. In this work, we present partial and total ⁵⁹Ni(n, p) and ⁵⁹Ni(n, α)more » cross sections, measured directly with a radioactive ⁵⁹Ni target, and compare the results to the present nuclear data evaluations. In addition, the results from this work are compared to a recent study of the ⁵⁹Ni(n, xp) reaction cross section that was performed via an indirect surrogate ratio method. The expected energy trend of the cross section, based on the current work, is inconsistent with that of the surrogate work. This calls into question the reliability of that application of the surrogate ratio method and highlights the need for direct measurements on unstable nuclei, when feasible.« less

Demand response (DR) has become a key element in balancing the power grid as the contribution of time-varying renewable power generation increases. Chemical plants are appealing candidates for DR programs as they offer large, concentrated and flexible loads. DR participation calls for frequent production rate changes over time scales that overlap with the dominant dynamics of the plant. Production scheduling should therefore consider the process dynamics explicitly. Here we present a data-driven approach for modelling the scheduling-relevant dynamics based on historical closed-loop operating data using autoregressive with extra inputs (ARX) models. We introduce a new, linear scheduling problem formulation basedmore » on the ARX representation, and demonstrate its implementation on an industrial air separation unit.« less

Microscopic mean-field approaches have been successful in describing the most probable reaction outcomes in low-energy heavy-ion reactions. However, those approaches are known to severely underestimate dispersions of observables around the average values that has limited their applicability. Recently it has been shown that a quantal transport approach based on the stochastic mean-field (SMF) theory significantly improves the description, while its application has been limited so far to fragment mass and charge dispersions. In this work, we extend the quantal transport approach based on the SMF theory for relative kinetic energy dissipation and angular momentum transfer in low-energy heavy-ion reactions. Basedmore » on the SMF concept, analytical expressions are derived for the radial and tangential friction and associated diffusion coefficients. Those quantal transport coefficients are calculated microscopically in terms of single-particle orbitals within the time-dependent Hartree-Fock (TDHF) approach. As the first application of the proposed formalism, we consider the radial linear momentum dispersion, neglecting the coupling between radial and angular momenta. We analyze the total kinetic energy (TKE) distribution of binary reaction products in the ¹³⁶Xe +²⁰⁸Pb reaction at E_c.m. =526 MeV and compare with experimental data. From time evolution of single-particle orbitals in TDHF, the radial diffusion coefficient is computed on a microscopic basis, while a phenomenological treatment is introduced for the radial friction coefficient. By solving the quantal diffusion equation for the radial linear momentum, the dispersion of the radial linear momentum is obtained, from which one can construct the TKE distribution. We find that the calculations provide a good description of the TKE distribution for strongly damped events with large energy losses, TKEL ≳ 150 MeV. However, the calculations underestimate the TKE distribution for smaller energy losses. Further studies are needed to improve the technical details of calculations.« less

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural rootmore » architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.« less