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Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: Coordinate Multiscale U 3 Si 2 Modeling
Since the events at the Fukushima-Daiichi nuclear power plant in March 2011 significant research has unfolded at national laboratories, universities and other institutions into alternative materials that have potential enhanced accident tolerance when compared to traditional \uo~fuel zircaloy clad fuel rods. One of the potential replacement fuels is uranium silicide (\usi) for its higher thermal conductivity and uranium density. The lower melting temperature is of potential concern during postulated accident conditions. Another disadvantage for \usi~ is the lack of experimental data under power reactor conditions. Due to the aggressive development schedule for inserting some of the potential materials into lead test assemblies or rods by 2022~\cite{bragg-sitton_2014} multiscale multiphysics modeling approaches have been used to provide insight into these materials. \\ \noindent The purpose of this letter report is to highlight the multiscale modeling effort for \usi~fuel as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuel (ATF) High Impact Problem (HIP). This fiscal year two new models for \usi~fuel have been incorporated into the BISON fuel performance code~\cite{Williamson2012} based upon lower length scale simulations using the GRASS-SST rate theory code. The models are briefly described in the following sections.
DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings
The paper presents a method and process to establish a database of energy efficiency performance (DEEP) to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 35 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and domestic hot water. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of an on-going project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users’ decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit. DEEP will be migrated into the DEnCity - DOE’s Energy City, which integrates large-scale energy data for multi-purpose, open, and dynamic database leveraging diverse source of existing simulation data.
Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations
Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that the fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.
Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: Coordinate Multiscale FeCrAl Modeling
Since the events at the Fukushima-Daiichi nuclear power plant in March 2011 significant research has unfolded at national laboratories, universities and other institutions into alternative materials that have potential enhanced ac- cident tolerance when compared to traditional UO2 fuel zircaloy clad fuel rods. One of the potential replacement claddings are iron-chromium-alunimum (FeCrAl) alloys due to their increased oxidation resistance [1–4] and higher strength [1, 2]. While the oxidation characteristics of FeCrAl are a benefit for accident tolerance, the thermal neu- tron absorption cross section of FeCrAl is about ten times that of Zircaloy. This neutronic penalty necessitates thinner cladding. This allows for slightly larger pellets to give the same cold gap width in the rod. However, the slight increase in pellet diameter is not sufficient to compensate for the neutronic penalty and enriching the fuel beyond the current 5% limit appears to be necessary [5]. Current estimates indicate that this neutronic penalty will impose an increase in fuel cost of 15-35% [1, 2]. In addition to the neutronic disadvantage, it is anticipated that tritium release to the coolant will be larger because the permeability of hydrogen in FeCrAl is about 100 times higher than in Zircaloy [6]. Also, radiation-induced hardening and embrittlement of FeCrAl need to be fully characterized experimentally [7]. Due to the aggressive development schedule for inserting some of the potential materials into lead test assemblies or rods by 2022 [8] multiscale multiphysics modeling approaches have been used to provide insight into these the use of FeCrAl as a cladding material. The purpose of this letter report is to highlight the multiscale modeling effort for iron-chromium-alunimum (FeCrAl) cladding alloys as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program through its Accident Tolerant Fuel (ATF) High Impact Problem (HIP). The approach taken throughout the HIP is to utilize lower length scale approaches (e.g., density functional theory, cluster dynamics, rate theory, phase field, and Visco-Plastic- Self-Consistent (VPSC)) to develop more physically informed models at the engineering scale for use in the BISON [9] fuel performance code.
A simulation model for wind energy storage systems. Volume 3: Program descriptions
Program descriptions, flow charts, and program listings for the SIMWEST model generation program, the simulation program, the file maintenance program, and the printer plotter program are given. For Vol 2, see .
DEEP: Database of Energy Efficiency Performance
A database of energy efficiency performance (DEEP) is a presimulated database to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 10 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER [sic] prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air conditioning, plug loads, and domestic hot war. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center (NERSC) of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of the CEC PIER project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users' decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit.
Enhancements to ASHRAE Standard 90.1 Prototype Building Models
Since the last publication of the commercial prototype building models, PNNL has made numerous enhancements to the original prototype models compliant with the 2004, 2007, and 2010 editions of Standard 90.1. Those enhancements are described here and were made for several reasons: (1) to change or improve prototype design assumptions; (2) to improve simulation accuracy; (3) to improve simulation infrastructure; and (4) to add additional detail to the models needed to capture certain energy impacts from Standard 90.1 improvements.
Simulations of ultra-high-energy cosmic rays propagation
We compare two techniques for simulation of the propagation of ultra-high-energy cosmic rays (UHECR) in intergalactic space: the Monte Carlo approach and a method based on solving transport equations in one dimension. For the former, we adopt the publicly available tool CRPropa and for the latter, we use the code TransportCR, which has been developed by the first author and used in a number of applications, and is made available online with publishing this paper. While the CRPropa code is more universal, the transport equation solver has the advantage of a roughly 100 times higher calculation speed. We conclude that the methods give practically identical results for proton or neutron primaries if some accuracy improvements are introduced to the CRPropa code.
Accelerating Convergence of Free Energy Computations with Hamiltonian Simulated Annealing of Solvent (HSAS)
Coupling between binding of a ligand to a receptor and the displacement of a number of bound water molecules is a common event in molecular recognition processes. When the binding site is deeply buried and the exchange of water molecules with the bulk region is difficult to sample, the convergence and accuracy in free energy calculations can be severely compromised. Traditionally, Grand Canonical Monte Carlo (GCMC) based methods have been used to accelerate equilibration of water-at the expense, however, of lengthy trials before a molecular dynamics (MD) simulation. In this paper, a user-friendly and cost-efficient method, Hamiltonian simulated annealing of solvent in combination with lambda-exchange of free energy perturbation (FEP) is proposed to accelerate the sampling of water molecules in free energy calculations. As an illustrative example with reliable data from previous GCMC simulations, absolute binding affinity of camphor to cytochrome P450 was calculated. The simulated hydration state change in the buried binding pocket quantitatively agrees with GCMC simulations. Finally, it is shown that the new protocol significantly accelerates sampling of water in a buried binding pocket and the convergence of free energy, with negligible setup and computing costs compared to GCMC methods.
Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: U 3 Si 2 Modeling Capabilities
U 3 Si 2 fuel is a potential candidate replacement for UO 2 in light water reactor fuel rods due to its higher uranium density and thermal conductivity. Its lower melting temperature and significant gaseous swelling may be of concern. The aggressive development schedule of the United States Department of Energy’s (DOE) Advanced Fuels Campaign (AFC) to have potential accident tolerant fuels (ATF) concepts in lead test rods or assemblies by 2022 has necessitated the need for advanced modeling and simulation techniques to investigate the behavior of these materials under normal operating and accident conditions. This report presents the modeling capabilities for U 3 Si 2 fuel at the engineering (continuum) scale developed as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) high impact problem (HIP). In this report, the models incorporated into the BISON fuel for U 3 Si 2 are described and comparisons to conventional UO 2 for separate effects cases are presented.
Vibrational energy flow in the villin headpiece subdomain: Master equation simulations
We examine vibrational energy flow in dehydrated and hydrated villin headpiece subdomain HP36 by master equation simulations. Transition rates used in the simulations are obtained from communication maps calculated for HP36. In addition to energy flow along the main chain, we identify pathways for energy transport in HP36 via hydrogen bonding between residues quite far in sequence space. The results of the master equation simulations compare well with all-atom non-equilibrium simulations to about 1 ps following initial excitation of the protein, and quite well at long times, though for some residues we observe deviations between the master equation and all-atom simulations at intermediate times from about 1–10 ps. Those deviations are less noticeable for hydrated than dehydrated HP36 due to energy flow into the water.
Proton Straggling in Thick Silicon Detectors
Straggling functions for protons in thick silicon radiation detectors are computed by Monte Carlo simulation. Mean energy loss is constrained by the silicon stopping power, providing higher straggling at low energy and probabilities for stopping within the detector volume. By matching the first four moments of simulated energy-loss distributions, straggling functions are approximated by a log-normal distribution that is accurate for Vavilov k is greater than or equal to 0:3. They are verified by comparison to experimental proton data from a charged particle telescope.
Comparison of Proton Energy Deposition from Monte Carlo Simulations and Measurement Results
ProNova's mission is to deliver a lower-cost, smaller, lighter and more energy efficient solution for Proton Therapy without sacrificing performance and improving upon existing capabilities. A high current, isochronous cyclotron (Sumitomo P235) is used as the mono-energetic 230 MeV proton beam source for the ProNova SC360 Proton Therapy System installation at the Knoxville Provision Center for Proton Therapy (PCPT). The energy of the proton beam ranges from 70 MeV to 230 MeV in the treatment room by adjusting the thickness of a Beryllium degrader installed at the Energy Section Beamline (ESB) for each treatment room. In order to better characterize the proton energy loss in the treatment volume, the proton energy deposition in a water phantom located at the treatment position is analyzed in this study, including Monte Carlo simulation and experimental measurements for different energies. It is concluded that Monte Carlo simulations give a good prediction of proton energy deposition. The results also confirmed that at increasing beam energy, protons will have broader FWHM and decreased peak-to-entrance dose ratio. The ProNova SC360 design results in an energy spread that does not change linearly with the energy due to the unique degrader and ESB design. Monte Carlo simulation results match proton beam measurement well. The Monte Carlo simulation results, validated by measurement, will be used as proton beam data for Treatment Planning System commissioning. (authors)
G-189A analytical simulation of the integrated waste management-water system using radioisotopes for thermal energy
An analytical simulation of the RITE-Integrated Waste Management and Water Recovery System using radioisotopes for thermal energy was prepared for the NASA-Manned Space Flight Center (MSFC). The RITE system is the most advanced concept water-waste management system currently under development and has undergone extended duration testing. It has the capability of disposing of nearly all spacecraft wastes including feces and trash and of recovering water from usual waste water sources: urine, condensate, wash water, etc. All of the process heat normally used in the system is produced from low penalty radioisotope heat sources. The analytical simulation was developed with the G189A computer program. The objective of the simulation was to obtain an analytical simulation which can be used to (1) evaluate the current RITE system steady state and transient performance during normal operating conditions, and also during off normal operating conditions including failure modes; and (2) evaluate the effects of variations in component design parameters and vehicle interface parameters on system performance.
Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning
Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.
Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications
We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.
Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: FeCrAl Modeling Capabilities
Iron-chromium-aluminum alloys are a potential candidate replacement for zirconium-based claddings in light water reactor fuel rods due to their increased strength (lower creep rates and higher yield stress) and slower oxidation kinetics with steam. Their larger neutron absorption cross-section necessitates thinner claddings and larger fuel pellets to maintain the same as-fabricated fuel-to-clad gap. The aggressive development schedule of the United States Department of Energy's (DOE) Advanced Fuels Campaign (AFC) to have potential accident tolerant fuels (ATF) concepts in lead test rods or assemblies by 2022 has necessitated the need for advanced modeling and simulation techniques to investigate the behavior of these materials under normal operating and accident conditions. This report presents the modeling capabilities for FeCrAl claddings at the engineering (continuum) scale developed as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) high impact problem (HIP). Sensitivity studies of select material parameters have been completed to determine their significance in predictions of important fuel performance parameters such as fuel centerline and surface temperatures, cladding stresses and strains, fission gas released, and rod internal pressure. Comparative analyses of FeCrAl and Zircaloy-4 claddings for separate effects conditions are presented.