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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

VERA BWR progression problems

During the first phase of the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, the Virtual Environment for Reactor Applications (VERA) was developed with a focus on capabilities for high-fidelity, multiphysics simulation of pressurized water reactors (PWRs). During this development effort, a set of progression problems was created ranging from smaller pin cell calculations to larger 3D full-core calculations. These progression problems helped to guide the development of the software and served as benchmarks against which to test VERA. Since 2019, efforts have been made to extend the capabilities of VERA to model boiling water reactors (BWRs). Because BWR simulations come with many unique challenges, a set of BWR progression problems was developed to aid in this new effort. The BWR progression problems range from 2D lattice calculations to 3D mini-core problems, and reference neutronic solutions were computed using continuous-energy Monte Carlo codes. MPACT, one of the neutronics code in VERA, was benchmarked using the BWR progression problems. The code is capable of computing solutions to all problems. The eigenvalues computed by MPACT agree well with the Monte Carlo reference solutions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Coupling coarse-mesh CFD with fine-mesh CFD for modeling molten-salt reactors in the Virtual Test Bed (VTB)

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims at developing a simulation tool kit to accelerate the development and deployment of nuclear power technologies. NEAMS multiphysics tools have been designed to provide numerical simulation support for the design and licensing of GEN IV reactors. Pronghorn is NEAMS's coarse-mesh computational fluid dynamics (CFD) tool, which is designed to run 3D core transients in GEN IV reactors at a reduced computational cost. To increase their accuracy, coarse-mesh CFD simulations require calibrated closure coefficients. One way of computing these coefficients is via the Nek5000, NEAMS's high-fidelity CFD tool. This article discusses our current research lines in informing Pronghorn closure coefficients via Nek5000 to enable multiphysics simulations of the core cavity of the molten-salt fast reactor. We present an application in which Pronghorn mixing length turbulent viscosity has been calibrated from Nek5000 simulations. The resulting Pronghorn thermal-hydraulics model is then coupled to Griffin, the NEAMS neutron transport solver, to solve for the steady-state configuration of the molten-salt fast reactor. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Coupling coarse mesh CFD with fine mesh CFD for modeling for Molten Salt Reactors in the Virtual Test Bed VTB

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims at developing a simulation tool kit to accelerate the development and deployment of nuclear power technologies. NEAMS multiphysics tools have been designed to provide numerical simulation support for the design and licensing of GEN IV reactors. Pronghorn is NEAMS’s coarse-mesh computational fluid dynamics (CFD) tool, which is designed to run 3D core transients in GEN IV reactors at a reduced computational cost. To increase their accuracy, coarse-mesh CFD simulations require calibrated closure coefficients. One way of computing these coefficients is via the Nek5000, NEAMS’s high-fidelity CFD tool. This article discusses our current research lines in informing Pronghorn closure coefficients via Nek5000 to enable multiphysics simulations of the core cavity of the molten-salt fast reactor. We present an application in which Pronghorn mixing length turbulent viscosity has been calibrated from Nek5000 simulations. The resulting Pronghorn thermal-hydraulics model is then coupled to Griffin, the NEAMS neutron transport solver, to solve for the steady-state configuration of the molten-salt fast reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Microstructure homogenization of concrete used in nuclear power plants

Almost all nuclear power plants in the United States are operating past their intended lifetimes or are requesting lifetime extensions. Therefore, understanding changes to the concrete containment structure over time is crucial to evaluate the structure’s continued viability. Concrete materials are heterogeneous particulate composites that exhibit viscoelastic material properties, which can lead to slow deformation over time, causing stress redistribution and the potential for creep cracking. A code to generate random, three dimensional (3D) concrete microstructures has been developed and paired with finite element analysis to predict the long-term viscoelastic properties of concrete. Data from these simulations are used to develop constitutive equations for the viscoelastic behavior of the homogenized concrete. The codes in this work are used to virtualize laboratory experiments, to obtain long-term creep data in a faster, cheaper manner. To validate this work, the simulated creep behavior of concrete is compared to 800 d of experimental data that has been extended to 27 y of data using the Time-Temperature superposition (TTS) principal. Excellent agreement between the simulation results and experimental data is seen.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Linear Seismic Source Equivalents in 3D Nonlinear Models: Effects of Embedded Small-Scale, Near-Source Structures

Gaining a proper understanding of how Earth structure and other near-source properties affect estimates of explosion yield is important to the nonproliferation mission. The yields of explosion sources are often based on seismic moment or waveform amplitudes. Quantifying how the seismic waveforms or estimates of the source characteristics derived from those waveforms are influenced by natural or man-made structures within the near-source region, where the wavefield behaves nonlinearly, is required to understand the full range of uncertainty in those yield estimates. We simulate tamped chemical explosions using a nonlinear, shock physics code and couple the ground motions beyond the elastic radius to a linear elastic, full waveform seismic simulation algorithm through 3D media. In order to isolate the effects of simple small-scale 3D structures on the seismic wavefield and linear seismic source estimates, we embed spheres and cylinders close to the fully- tamped source location within an otherwise homogenous half-space. The 3 m diameters spheres, given their small size compared to the predominate wavelengths investigated, not surprisingly are virtually invisible with only negligible perturbations to the far-field waveforms and resultant seismic source time functions. Similarly, the 11 m diameter basalt sphere has a larger, but still relatively minor impact on the wavefield. However, the 11 m diameter air-filled sphere has the largest impact on both waveforms and the estimated seismic moment of any of the investigated cases with a reduction of ~25% compared to the tamped moment. This significant reduction is likely due in large part to the cavity collapsing from the shock instead of being solely due to diffraction effects . Although the cylinders have the same diameters as the 3 m spheres, their length of interaction with the wavefield produces noticeable changes to the seismic waveforms and estimated source terms with reductions in the peak seismic moment on the order of 10%. Both the cylinders and 11 m diameter spheres generate strong shear waves that appear to emanate from body force sources.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

An Enhanced Virtual Crack Closure Technique for Stress Intensity Factor Calculation along Arbitrary Crack Fronts and the Application in Hydraulic Fracturing Simulation

The virtual crack closure technique (VCCT) is widely used for calculating energy release rates along crack fronts and modeling the propagation of cracks in solid materials. Although the VCCT formulation for smooth crack fronts has been sufficiently addressed in the literature, the application of VCCT to a nonsmoothed crack front with sharp corners warrants further investigation. In this study, we describe an enhanced VCCT to calculate energy release rates and stress intensity factors for cracks with arbitrary shapes in 3D domains discretized on structured grids. The formulations of the enhanced VCCT were developed and implemented into a multiphysics simulation environment capable of simulating crack propagation in the framework of linear elastic fracture mechanics. Comparisons with existing analytical/numerical solutions and other VCCT approaches were performed to verify the enhanced VCCT in terms of SIF calculation along nonsmoothed crack fronts. We then applied the enhanced VCCT to a hydraulically driven penny-shaped fracture problem to further demonstrate its capability to simulate nonsmoothed fracture propagation.

02 PETROLEUM↗
DOE OSTI2023

Determination of the mutual orientation between proton CSA tensors mediated through band-selective 1 H– 1 H recoupling under fast MAS

We report the mutual orientation of nuclear spin interaction tensors provides critical information on the conformation and arrangement of molecules in chemicals, materials, and biological systems at an atomic level. Proton is a ubiquitous and important element in a variety of substances, and its NMR is highly sensitive due to their virtually 100% natural abundance and large gyromagnetic ratio. Nevertheless, the measurement of mutual orientation between the 1 H CSA tensors has remained largely untouched in the past due to strong 1 H– 1 H homonuclear interactions in a dense network of protons. In this study, we have developed a proton-detected 3D 1 H CSA/ 1 H CSA/ 1 H CS correlation method that utilizes three techniques to manage homonuclear interactions, namely fast magic-angle spinning, windowless C-symmetry-based CSA recoupling (windowless-ROCSA), and a band-selective 1 H– 1 H polarization transfer. The asymmetric 1 H CSA/ 1 H CSA correlated powder patterns produced by the C-symmetry-based methods are highly sensitive to the sign and asymmetry parameter of the 1 H CSA, and the Euler angle β as compared to the symmetric pattern obtained by the existing γ-encoded R-symmetry-based CSA/CSA correlation methods and allows a larger spectral area for data fitting. These features are beneficial for determining the mutual orientation between the nuclear spin interaction tensors with improved accuracy.

1H CSA↗
DOE OSTI2020

Development of the MPACT 69-group Library for Magnox Reactor Analysis using CASL VERA

The Consortium for Advanced Simulation of Light Water Reactors (CASL) has developed the CASL toolset, Virtual Environment for Reactor Analysis (VERA), for pressurized water reactor (PWR) analysis. Recently the CASL VERA was improved for Magnox reactor analysis, which required the development of a new cross section library and new geometrical and thermal feedback capabilities for graphite-moderated Magnoxreactors. The MPACT neutronics module of the CASL core simulator is a 3D whole core transport code, which requires a new cross section library with a different energy group structure due to the different neutronic characteristics of Magnox compared with PWR. A new 69-group structure was developed based on the MPACT 51-group structure to have more thermal energy groups and to be a subset of the SCALE 252-group structure. The ENDF/B-VII.1 MPACT 69-group library was developed for Magnox reactor analysis using the SCALE/AMPX and VERA-XSTools for which a super-homogenization method was applied, and transport cross sections were generated for graphite using a neutron leakage conservation method. Benchmark results show that new MPACT 69-group library works reasonably well for Magnox reactor analysis.

Kim, Kang Seog↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Using 3D position sensitivity to reveal response non-uniformities in CdZnTe, TlBr, and CsPbBr 3 detectors

Position sensitivity enables the correction of response non-uniformities in room-temperature semiconductor detectors caused by crystal defects and other factors. It can also be used to pinpoint the exact location of crystal defects responsible for the response variations. This work describes a technique for revealing and visualizing the detector regions affecting the charge collection efficiency in CdZnTe (CZT), TlBr, and CsPbBr 3 detectors configured as position-sensitive virtual Frisch-grid (VFG) devices. The technique correlates the photopeak events in energy spectra with their spatial distributions inside the detectors using the position information. By selecting the events from narrow energy intervals within a photopeak, we can visualize the detector volumes with particular charge collection efficiencies, which, in turn, correlate with the locations of electrode and crystal defects. Here, we demonstrate this technique in several examples. Columnar structures in the volume plots (position distribution maps) are consistent with signal losses near or at the anode in selected samples of CZT and TlBr. Structures exhibiting a distinct depth dependence are consistent with grain boundaries or other crystal defects.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

The Future of Visualization Technology Vis in Practice

Two decades ago, we witnessed an inflection point as graphics programming became commoditized. A comparable inflection point may be on the horizon, this time driven by the commoditization of immersive display technology with Augmented Reality (AR), Virtual Reality (VR), and Extended Reality (XR) technologies poised to redefine our interaction with data, environments, and collaboration. To drive the technology and its application, we should consider the lessons learned from successful large-scale immersive installations. Where these installations have thrived is by enhancing traditional workflows rather than entirely replacing them, supporting improved judgments of complex spaces, direct interaction in 3D, and embedding high-dimensional data.

AR/VR↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Status of the OECD/NEA Watts Bar unit 1 benchmark and calculations of local reactor core power data of the Zero Power physics tests

The paper aims to provide an update on the status of the Organization for Economic Cooperation and Development (OECD) / the Nuclear Energy Agency (NEA) Tennessee Valley Authority (TVA) Watts Bar 1 (WB1) Multi-Physics Multi-Cycle depletion benchmark and the results associated with Exercise 1. The benchmark relies on the set of benchmark progression problems developed by the Department of Energy (DOE) Consortium for the Advanced Simulation of Light Water Reactors (CASL) for the Virtual Environment for Reactor (VERA), which are based on real plant design and operational data. The OECD/NEA TVA WB1 benchmark is designed for validation of both traditional and novel high-fidelity multi-physics codes to analyze Pressurized Water Reactors (PWR) depletion cycles. The activities are conducted under the Expert Group on Reactor Systems Multi-Physics (EGMUP) at NEA/OECD. In this work, we analyze Exercise 1 of the benchmark: stand-alone Three-Dimensional (3D) neutronics at Start-up Zero Power Physics Test (ZPPT) at Hot Zero Power Conditions (HZP) and the resulting power maps. The code used to model the exercise is the continuous energy Monte Carlo code Serpent 2.1.31 along with the nuclear data library ENDF/B-VII.1. Serpent results are compared with the high-fidelity deterministic code MPACT, which is part of VERA. The paper presents three selected results from power calculations required output. The results compared are the normalized axially integrated radial core power maps, the normalized axial averaged core power shapes, and the normalized core hottest and coldest assemblies radial power maps. The Root Mean Square Deviation (RMSD) between Serpent and MPACT is 0.51 % for the axially integrated radial core power map, 1.41 % for the axial averaged core power shape, 2.42 % for the hottest assembly axial power shape, 0.88 % for the coldest assembly axial integrated hottest assembly radial pin power map, and 0.17 % for the axially integrated coldest assembly radial pin power map. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of a desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. Therefore, to measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Artificial generation of representative single Li-ion electrode particle architectures from microscopy data

Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi 0.5 Mn 0.3 Co 0.2 O 2 particles with full sub-particle grain detail. Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an ‘outer shell’ model, and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a ‘grain’ model. A random field model used to characterize and generate the outer shells, and a random tessellation model used to characterize and generate grain architectures, are combined to form a multi-scale model for the generation of virtual electrode particles with full-grain detail. This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Algorithms for coordinate reconstruction in position-sensitive virtual Frisch-grid detectors

Arrays of position-sensitive virtual Frisch-grid (VFG) CdZnTe (CZT) detectors provide a cost-effective solution for integrating large-area arrays for gamma-ray imaging and spectroscopy. These detectors employ high-aspect ratio CZT crystals (bars) with thicknesses up to 50 mm and cross-sections of up to 10 × 10 mm 2 . Despite the long drift distances of charge carriers in such crystals, the detectors have demonstrated excellent performance, achieving energy resolutions better than 1 % full width at half maximum (FWHM) at 662 keV and 3D position resolutions finer than 1 mm. The high spatial resolution is a critical feature of these detectors, as it enables correction of response non-uniformities caused by crystal defects, which remain present even in the highest-quality CZT material. Dislocations and dislocation walls are the primary defects responsible for variations in charge carrier losses as they drift from the interaction points toward the charge-collecting electrodes. The mechanism by which these defects affect carrier transport is generally well understood. Dislocations and sub-grain boundaries act as sinks for carrier-trapping centers, primarily impurities and secondary phases such as tellurium inclusions and precipitates. Here, these regions exhibit significantly higher carrier-trapping rates, leading to variations in the μτ-products. Because the locations of these micron-sized regions are fixed within the detector volume, fluctuations in the total collected charge arise from the random distribution of interaction sites. This results in non-uniform detector responses and degradation of energy resolution. However, by measuring the interaction-site locations with sufficient precision, charge-loss variations can be accurately corrected, allowing recovery of nearly intrinsic energy resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Flexible and Accessible 4D Subsurface Visualization Using a Web-Based Platform

4D subsurface visualization using a web platform can provide improved communication, education and outreach to non-experts and stakeholders. It allows for the improved understanding of complex relationships and interactions that occur in inaccessible locations of which many experimental testbeds exist in. The use of a web-based visualization tool lessens the cognitive impact further by reducing the need of specialized software. Users can define their own exploration of a 3D or 4D scene, adding and removing data as needed and are able to make informed decisions based on accurate model visualizations, while providing a collaborative tool that can be accessed on any platform using a browser window. The tool is also able to point to real time streams to display up-to-the-second data as an experiment is ongoing. This level of latency can also provide operators with essential information that can direct an experiment’s progress. Additionally, this tool can leverage augmented and virtual reality (AR, VR) capabilities of certain mobile devices and head-mounted displays, providing further engaging visualization possibilities.

Pratt, Martin J.↗