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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Presentation of the MERC work-flow for the computation of a 2D radial reflector in a PWR

This paper presents a work-flow for computing an equivalent 2D radial reflector in a pressurized water reactor (PWR) core, in adequacy with a reference power distribution, computed with the method of characteristics (MOC) of the lattice code APOLLO2. The Multi-modelling Equivalent Reflector Computation (MERC) work-flow is a coherent association of the lattice code APOLLO2 and the core code COCAGNE, structured around the ADAO (Assimilation de Donnees et Aide a l'Optimisation) module of the SALOME platform, based on the data assimilation theory. This study leads to the computation of equivalent few-groups reflectors, that can be spatially heterogeneous, which have been compared to those obtained with the OPTEX similar methodology developed with the core code DONJON, as a first validation step. Subsequently, the MERC work-flow is used to compute the most accurate reflector in consistency with all the R and D choices made at Electricite de France (EDF) for the core modelling, in terms of number of energy groups and simplified transport solvers. We observe important reductions of the power discrepancies distribution over the core when using equivalent reflectors obtained with the MERC work-flow. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2021

Location and characterization of heterogeneous phases within Mary Rose wood

Preserving the Mary Rose oak hull for future generations is a major challenge due to the highly heterogeneous nature of waterlogged wooden artifacts, which contain polycrystalline, amorphous, and nanostructured materials that test traditional characterization methods. Effective conservation requires detailed knowledge of the distribution and chemical nature of these species to develop strategies for preventing multiple chemo-mechanical degradation pathways. Here, we apply synchrotron-based computed tomography total scattering methods to the Mary Rose keelson wood that provides valuable position-resolved structural information on multiple embedded species of different length and concentration scales. We identify 5 nm zinc sulfide nanoparticles in the wood, presumably deposits from bacteria operating on the sulfur energy cycle under the anaerobic conditions on the seabed. These are identified as precursors to acid attack on the wood upon removal to an aerobic environment. These insights inform not only next-generation conservation strategies, but also the efficacy and unforeseen issues of previous treatments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Modeling Analog Tile-Based Accelerators Using SST

Analog computing has been widely proposed to improve the energy efficiency of multiple important workloads including neural network operations, and other linear algebra kernels. To properly evaluate analog computing and explore more complex workloads such as systems consisting of multiple analog data paths, system level simulations are required. Moreover, prior work on system architectures for analog computing often rely on custom simulators creating signficant additional design effort and complicating comparisons between different systems. To remedy these issues, this report describes the design and implementation of a flexible tile-based analog accelerator element for the Structural Simulation Toolkit (SST). The element focuses on heavily on the tile controller—an often neglected aspect of prior work—that is sufficiently versatile to simulate a wide range of different tile operations including neural network layers, signal processing kernels, and generic linear algebra operations without major constraints. The tile model also interoperates with existing SST memory and network models to reduce the overall development load and enable future simulation of heterogeneous systems with both conventional digital logic and analog compute tiles. Finally, both the tile and array models are designed to easily support future extensions as new analog operations and applications that can benefit from analog computing are developed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

On the Steady Mechanical Response of a Heterogeneous Fuel Plate

Herein, an exact solution for a heterogeneous, discontinuous wide-beam is developed having three unique boundary conditions sets. These boundary condition sets were chosen based on their relevance to plate-type fueled reactors. This solution, with respective boundary condition sets contributes new insights to the field of engineering mechanics as applied within the nuclear engineering discipline. Herein, the exact solution is validated under a set of test cases. A comparison is then made against other relations that have been developed to provide similar engineering insights. Lastly, the solution is applied toward a presently relevant engineering problem regarding the design of a monolithic plate-type fuel. The outcome of this work provides a solution form for the computation of out-of-plane deflection of a heterogeneous, discontinuous wide-beam that can be easily applied in engineering mechanics and is flexible in use.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Memo regarding the Final Review of FY21 ASC L2 Milestone 7840: Neural Mini-Apps for Future Heterogeneous HPC Systems

The final review for the FY21 Advanced Simulation and Computing (ASC) Computational Systems and Software Environments (CSSE) L2 Milestone #7840 was conducted on August 25th, 2021 at Sandia National Laboratories in Albuquerque, New Mexico. The review committee/panel unanimously agreed that the milestone has been successfully completed, exceeding expectations on several of the key deliverables.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Computation of Effective Mechanical Properties and Mechanical Erosion Modeling of TPS Materials

The goal of this presentation is to provide a general overview of the multi-scale modeling formulation to determine if there is additional surface recession in Thermal Protection Systems (TPS) materials as a result of mechanical erosion due to high shear conditions during atmospheric entry. This modeling process is performed at different scales by leveraging two computational frameworks developed at NASA: the Porous Microstructure Analysis (PuMA) software, and the Porous material Analysis Toolbox based on OpenFOAM (PATO). PuMA specializes in computing effective macro-scale material properties by performing material response simulations on 3D digital micro-scale representations of porous micro-structures. The modeling of TPS materials at the micro-scale is essential to understand how they behave as part of a heat shield assembly. The first part of the presentation will detail the implementation of PuMA’s cell-centered finite volume elasticity solver, which allows the computation of macro-scale effective mechanical properties of heterogeneous and anisotropic materials such as fibrous and woven TPS composites. These homogenized mechanical properties are used by PATO’s mechanical erosion model to predict the TPS material’s recession at a larger scale. This work will also provide some examples of multi-scale analysis from the fiber level up to the unit cell. The second part of the presentation will focus on the macro-scale approach to determine if erosion at the heat shield’s surface occurs due to mechanical and thermal loads experienced during atmospheric entry. To accomplish this, a solid mechanics module was integrated within PATO enabling it to model the potential mechanical erosion in three steps: first, after obtaining the effective mechanical properties with PuMA, the implemented stress analysis solver computes the stress and the displacement fields for the TPS material using the wall shear stress tensor, computed using a CFD solver, as boundary conditions; then, regions on the surface where the stress meets the failure criteria are identified; finally, the failed material is removed and the mesh is redistributed accordingly. The outcome is a model capable of predicting the total recession in the material due to surface chemistry and mechanical erosion.

Mechanical Properties↗
DOE OSTI2018

A hybrid finite element-spectral boundary integral approach: Applications to dynamic rupture modeling in unbounded domains

The finite element method (FEM) and the spectral boundary integral method (SBI) have both been widely used in the study of dynamic rupture simulations along a weak interface. Here, we present a hybrid method that combines FEM and SBI through the consistent exchange of displacement and traction boundary conditions, thereby benefiting from the flexibility of FEM in handling problems with nonlinearities or small-scale heterogeneities and from the superior performance and accuracy of SBI. We validate the hybrid method using a benchmark problem from the Southern California Earthquake Center's dynamic rupture simulation validation exercises.We further demonstrate the capability and computational efficiency of the hybrid scheme for resolving off-fault heterogeneities by studying a 2D in-plane shear crack in two different settings: one where the crack is embedded in a high-velocity zone and another where it is embedded in a low-velocity zone. Finally, we discuss the potential of the hybrid method for addressing a wide range of problems in geophysics and engineering.

58 GEOSCIENCES↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

SU-E-I-01: A Fast, Analytical Pencil Beam Based Method for First Order X-Ray Scatter Estimation of Kilovoltage Cone Beam X-Rays

Purpose: To analytically estimate first-order x-ray scatter for kV cone beam x-ray imaging with high computational efficiency. Methods: In calculating first-order scatter using the Klein-Nishina formula, we found that by integrating the point-to-point scatter along an interaction line, a “pencil-beam” scatter kernel (BSK) can be approximated to a quartic expression when the imaging field is small. This BSK model for monoenergetic, 100keV x-rays has been verified on homogeneous cube and cylinder water phantoms by comparing with the exact implementation of KN formula. For heterogeneous medium, the water-equivalent length of a BSK was acquired with an improved Siddon's ray-tracing algorithm, which was also used in calculating pre- and post- scattering attenuation. To include the electron binding effect for scattering of low-kV photons, the mean corresponding scattering angle is determined from the effective point of scattered photons of a BSK. The behavior of polyenergetic x-rays was also investigated for 120kV x-rays incident to a sandwiched infinite heterogeneous slab phantom, with the electron binding effect incorporated. Exact computation and Monte Carlo simulations were performed for comparisons, using the EGSnrc code package. Results: By reducing the 3D volumetric target (o(n{sup 3})) to 2D pencil-beams (o(n{sup 2})), the computation expense can be generally lowered by n times, which our experience verifies. The scatter distribution on a flat detector shows high agreement between the analytic BSK model and exact calculations. The pixel-to-pixel differences are within (-2%, 2%) for the homogeneous cube and cylinder phantoms and within (0, 6%) for the heterogeneous slab phantom. However, the Monte Carlo simulation shows increased deviation of the BSK model toward detector periphery. Conclusion: The proposed BSK model, accommodating polyenergetic x-rays and electron binding effect at low kV, shows great potential in efficiently estimating the first-order scatter from small imaging fields. We are investigating more thoroughly to improve performance and explore applications.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

Revealing the Distribution of Metal Carboxylates in Oil Paint from the Micro‐ to Nanoscale

Abstract Oil paints comprise pigments, drying oils, and additives that together confer desirable properties, but can react to form metal carboxylates (soaps) that may damage artworks over time. To obtain information on soap formation and aggregation, we introduce a new tapping‐mode measurement paradigm for the photothermal induced resonance (PTIR) technique that enables nanoscale IR spectroscopy and imaging on highly heterogenous and rough paint thin sections. PTIR is used in combination with μ‐computed tomography and IR microscopy to determine the distribution of metal carboxylates in a 23‐year old oil paint of known formulation. Results show that heterogeneous agglomerates of Al‐stearate and a Zn‐carboxylate complex with Zn‐stearate nano‐aggregates in proximity are distributed randomly in the paint. The gradients of zinc carboxylates are unrelated to the Al‐stearate distribution. These measurements open a new chemically sensitive nanoscale observation window on the distribution of metal soaps that can bring insights for understanding soap formation in oil paint.

Ma, Xiao↗
DOE OSTI2013

Applications of Ensemble-based Data Assimilation Techniques for Aquifer Characterization using Tracer Data at Hanford 300 Area

Subsurface aquifer characterization often involves high parameter dimensionality and requires tremendous computational resources if employing a full Bayesian approach. Ensemble-based data assimilation techniques, including filtering and smoothing, are computationally efficient alternatives. Despite the increasing number of applications of ensemble-based methods in assimilating flow and transport related data for subsurface aquifer charaterization, most are limited to either synthetic studies or two-dimensional problems. In this study, we applied ensemble-based techniques for assimilating field tracer experimental data obtained from the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area. The forward problem was simulated using the massively-parallel three-dimensional flow and transport code PFLOTRAN to effectively deal with the highly transient flow boundary conditions at the site and to meet the computational demands of ensemble-based methods. This study demonstrates the effectiveness of ensemble-based methods for characterizing a heterogeneous aquifer by sequentially assimilating multiple types of data. The necessity of employing high performance computing is shown to enable increasingly mechanistic non-linear forward simulations to be performed within the data assimilation framework for a complex system with reasonable turnaround time.

data assimilation, ensemble kalman filter, inverse↗
DOE OSTI2015

Generic Crystalline Disposal Reference Case

A generic reference case for disposal of spent nuclear fuel and high-level radioactive waste in crystalline rock is outlined. The generic cases are intended to support development of disposal system modeling capability by establishing relevant baseline conditions and parameters. Establishment of a generic reference case requires that the emplacement concept, waste inventory, waste form, waste package, backfill/buffer properties, EBS failure scenarios, host rock properties, and biosphere be specified. The focus in this report is on those elements that are unique to crystalline disposal, especially the geosphere representation. Three emplacement concepts are suggested for further analyses: a waste packages containing 4 PWR assemblies emplaced in boreholes in the floors of tunnels (KBS-3 concept), a 12-assembly waste package emplaced in tunnels, and a 32-assembly dual purpose canister emplaced in tunnels. In addition, three failure scenarios were suggested for future use: a nominal scenario involving corrosion of the waste package in the tunnel emplacement concepts, a manufacturing defect scenario applicable to the KBS-3 concept, and a disruptive glaciation scenario applicable to both emplacement concepts. The computational approaches required to analyze EBS failure and transport processes in a crystalline rock repository are similar to those of argillite/shale, with the most significant difference being that the EBS in a crystalline rock repository will likely experience highly heterogeneous flow rates, which should be represented in the model. The computational approaches required to analyze radionuclide transport in the natural system are very different because of the highly channelized nature of fracture flow. Computational workflows tailored to crystalline rock based on discrete transport pathways extracted from discrete fracture network models are recommended.

Environmental Protection↗
DOE OSTI2014

Verification of sub-grid filtered drag models for gas-particle fluidized beds with immersed cylinder arrays

The accuracy of coarse-grid multiphase CFD simulations of fluidized beds may be improved via the inclusion of filtered constitutive models. In our previous study (Sarkar et al., Chem. Eng. Sci., 104, 399-412), we developed such a set of filtered drag relationships for beds with immersed arrays of cooling tubes. Verification of these filtered drag models is addressed in this work. Predictions from coarse-grid simulations with the sub-grid filtered corrections are compared against accurate, highly-resolved simulations of full-scale turbulent and bubbling fluidized beds. The filtered drag models offer a computationally efficient yet accurate alternative for obtaining macroscopic predictions, but the spatial resolution of meso-scale clustering heterogeneities is sacrificed.

42 ENGINEERING↗
DOE OSTI2021

Prediction of Probabilistic Shock Initiation Thresholds of Energetic Materials Through Evolution of Thermal-Mechanical Dissipation and Reactive Heating

The ignition threshold of an energetic material (EM) quantifies the macroscopic conditions for the onset of self-sustaining chemical reactions. The threshold is an important theoretical and practical measure of material attributes that relate to safety and reliability. Historically, the thresholds are measured experimentally. In this work, we present a new Lagrangian computational framework for establishing the probabilistic ignition thresholds of heterogeneous EM out of the evolutions of coupled mechanical-thermal-chemical processes using mesoscale simulations. Furthermore, the simulations explicitly account for microstructural heterogeneities, constituent properties, and interfacial processes and capture processes responsible for the development of material damage and the formation of hotspots in which chemical reactions initiate. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, reactive chemical heating, gaseous product generation, and convective heat transfer. To determine the ignition threshold, the minimum macroscopic loading required to achieve self-sustaining chemical reactions with a rate of reactive heat generation exceeding the rate of heat loss due to conduction and other dissipative mechanisms is determined. Probabilistic quantification of the processes and the thresholds are obtained via the use of statistically equivalent microstructure sample sets (SEMSS). The predictions are in agreement with available experimental data.

36 MATERIALS SCIENCE↗