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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Role of Cyclic Phase Transitions in Additive Manufacturing of Metals and Alloys - Lessons Learned from Welding Science

Additive manufacturing (also known as 3D printing) of metals is considered to be a disruptive technology, able to produce limited number of high value components with topologically optimized geometries and functionalities. Realization of the above potential for real-world applications is stifled by lack of standard computational design tools; component certifications, varied starting powder feed stock compositions, methods to probe thermomechanical processes, microstructural homogeneity, residual stress, as well as, anisotropic static- and dynamic-material properties. Detailed research of direct energy deposition, laser- and electron-powder bed additive manufacturing demonstrates that the underlying physics of these processes are very similar to welding, except for complex boundary conditions. This paper will review published literature and on-going research with reference to fundamental aspects of heat and mass transfer, solidification under large (103 to 105 K/m) thermal gradients and (10-3 to 100 m/s) liquid solid-interface velocities, as well as, solid->solid transformation under repeated thermal excursions. Case studies on model based qualification of Ni- and Ti- alloy builds made by additive manufacturing (AM), based on the above fundamental knowledge will be discussed.

Babu, Sudarsanam Suresh↗
DOE OSTI2018

OES Task 10 WEC Heaving Sphere Performance Modelling Verification

OES Task 10 Modelling, Verification and Validation of Wave Energy Converters (WECs) is a task under the IEA Technology Collaboration Program for Ocean Energy Systems (OES). The long-term goals are to assess the accuracy of, and establish confidence in, the use of numerical WEC models. to determine a range of validity of existing computational modelling tools, to identify uncertainty related to simulation methodologies and finally to define future research. To some extent, this project builds on the experience from a similar effort carried out to verify modelling of wind turbines as part of the IEA Wind Task 30 on wind OC3-OC5.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Improvements to Modeling Capabilities of ATF Concepts in the BISON Fuel Performance Code

Research into accident tolerant materials for the fuel and cladding in Light Water Reactors has been at the forefront of the nuclear fuel research community since the events that oc- curred at the Fukushima Daiichi nuclear power plant in March 2011. Accident tolerant materials are defined as those that provide significantly increased response time in the event of an accident while providing similar or improved performance as the conventional UO2/zirconium-based cladding fuel rods dur- ing normal operation [1]. In particular, qualitative attributes for materials with enhanced accident tolerance include im- proved reaction (e.g., oxidation) kinetics with steam, resulting in slower hydrogen (or other combustible gases) generation rate, while maintaining acceptable thermo-mechanical prop- erties, fuel-clad interactions, and fission-product behavior [1]. Through its Office of Nuclear Energy the United States De- partment of Energy (U.S. DOE) has accelerated research in this area through the Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). The goal of the AFC Accident Tolerant Fuel (ATF) program is to guide se- lection of promising concepts for insertion into a commercial reactor as part of a lead test rod or assembly in 2022. Given the aggressive development schedule, it is impossi- ble to perform a comprehensive set of experiments to provide material characterization data. Therefore, the AFC is utilizing computational analysis tools in an effort to understand the proposed accident tolerant materials. The two materials of interest in this work are iron-chromium-aluminum (FeCrAl) cladding and U3Si2. From a fuel performance standpoint, the correlations available to describe the behavior of these two materials is limited. Therefore, as additional experimental data becomes available and correlations are updated, the fuel performance models must be updated to represent the current state of the art understanding of these materials. In this paper we demonstrate the importance of utilizing the latest state-of-the-art models for fuel performance simula- tions by comparing old and new thermal conductivity models for U3Si2 fuel and old and new oxidation models for FeCrAl cladding alloys using the BISON [2, 3] fuel performance code under normal operating conditions. The results indicate that the latest thermal conductivity model for U3Si2 results in fuel centerline temperatures that are on average approximately 3% higher than the previous model. For the oxidation behavior of FeCrAl alloys the new model predicts observable but small ox- ide thickness (~6 µm) compared to the previous model which predicted zero oxide scale formation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Lignin Utilization

Valorization of lignin has massive potential economic and sustainability benefits for the lignocellulosic biorefinery. However, challenges remain to realize lignin conversion to coproducts, especially related to selective, high yield depolymerization to monomers and quantitative analytics on lignin, the latter of which is critical for accurate process modeling. Towards these goals, the Lignin Utilization project focuses on catalytic lignin deconstruction chemistries for C-O and C-C bond cleavage, development of analytical chemistry techniques to quantitatively characterize lignin, and syntheses of requisite model compounds for understanding lignin transformations. This work is done with and supports multiple BETO projects, including the Biological Lignin Valorization (BLV) project, the Separations Consortium, and others. Outcomes of Lignin Utilization for analytics and synthesis include 1) development of new mass spectrometry (MS) methods to characterize lignin dimers/oligomers in process streams, 2) deployment of a computational-experimental tool (with Biochemical Process Modeling and Simulation) to identify lignin-derived compounds with high fidelity from MS, and 3) delivery of >40 unique compounds. From a catalysis perspective, we have developed new oxidative approaches to cleave C-O and C-C bonds and produce >50% bio-available aromatic monomers for biological funneling for the BLV project and have developed recoverable bases for base-catalyzed deconstruction of lignin.

BIOMASS FUELS↗
DOE OSTI2017

Improvements to Modeling Capabilities of ATF Concepts in the BISON Fuel Performance Code

Research into accident tolerant materials for the fuel and cladding in Light Water Reactors has been at the forefront of the nuclear fuel research community since the events that occurred at the Fukushima Daiichi nuclear power plant in March 2011. Accident tolerant materials are defined as those that provide significantly increased response time in the event of an accident while providing similar or improved performance as the conventional UO{sub 2}/zirconium-based cladding fuel rods during normal operation. In particular, qualitative attributes for materials with enhanced accident tolerance include improved reaction (e.g., oxidation) kinetics with steam, resulting in lower hydrogen (or other combustible gases) generation rate, while maintaining acceptable thermo-mechanical properties, fuel-clad interactions, and fission-product behavior. Through it's Office of Nuclear Energy, the United States Department of Energy (U.S. DOE) has accelerated research in this area through the Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). The goal of the AFC Accident Tolerant Fuel (ATF) program is to guide selection of promising concepts for insertion into a commercial reactor as part of a lead test rod or assembly in 2022. Given the aggressive development schedule, it is impossible to perform a comprehensive set of experiments to provide material characterization data. Therefore, the AFC is utilizing computational analysis tools in an effort to understand the proposed accident tolerant materials. The two materials of interest in this work are iron-chromium-aluminum (FeCrAl) cladding and U{sub 3}Si{sub 2}. From a fuel performance standpoint, the correlations available to describe the behavior of these two materials is limited. Therefore, as additional experimental data becomes available and correlations are updated, the fuel performance models must be updated as well to assure that the material models reflect the latest experimental findings. In this paper we demonstrate the importance of utilizing the latest state-of-the-art models for fuel performance simulations by comparing the change in the updated thermal conductivity model for U{sub 3}Si{sub 2} fuel and oxidation model for FeCrAl cladding alloys using the BISON fuel performance code under normal operating conditions. The results indicate that the latest thermal conductivity model for U{sub 3}Si{sub 2} results in fuel centerline temperatures that are on average approximately 3% higher than the previous model. For the oxidation behavior of FeCrAl alloys the new model predicts observable but small oxide thickness (6 μm) compared to the previous model which predicted essentially zero oxide scale formation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Defect Thermodynamics and Transport Properties of Perovskite and Fluorite Materials for Solid-Oxide and Proton Conducting Oxide Cells Evaluated Based on Density Functional Theory Modeling

Density functional theory based defect thermodynamic modeling was performed to determine the effect of humidity and H2/O2 gas pressure on various defect chemistry and transport properties of perovskite and fluorite oxides for solid-oxide and proton-conducting-oxide cell applications, with inclusion of the electronic-conducting oxides (as electrodes) and insulating oxides (as electrolytes). Automatic defect generation workflow and first-principles charged defect analysis were implemented on NETL Joule supercomputer for modeling defect equilibria and transport properties of insulating oxides as electrolytes in SOCs and proton-conducting ceramic cells. A GNU Octave defect model subroutines were developed to facilitate defect modeling of electronic conducting oxides in a wide range of operating conditions guided by modeling and experiments. The model includes the hydride defect formation reaction under reducing conditions and allows to incorporate nonstoichiometry effects on the defect thermodynamic parameters. The developed model serves as a platform to facilitate fundamental understanding of the defect thermodynamics in SOC oxide materials and can be used as a novel tool in computational materials screening for SOC and other energy applications.

Lee, Yueh-Lin↗
DOE OSTI2026

Stochastic Thermo-Hydro Modeling and Neural Network Surrogate Development for Thermal Resource Assessment of the Galleries-to-Calories Geobattery

The Galleries-to-Calories Geobattery concept explores the use of abandoned coal mine workings for large-scale thermal energy transport and storage. The system involves injecting waste heat from a supercomputing facility into flooded mine galleries, where groundwater flow can store and transport thermal energy for potential recovery in downgradient district heating and cooling applications. To evaluate the feasibility and performance of the Geobattery under geological and operational uncertainty, we developed a suite of stochastic thermo-hydrological (TH) simulations using Monte Carlo sampling of key uncertain parameters (e.g., permeability, porosity, thermal conductivity, specific heat capacity) and operating conditions (e.g., injection rate, injection temperature). Results identified injection rate and temperature as the most influential parameters governing thermal front propagation, while the geometry of the room-and-pillar structure played a critical role in directing the extent and orientation of thermal advancement. Optimal combinations of material properties for maximizing heat recovery were also determined. To address the high computational cost of coupled-process stochastic modeling, we trained a neural network surrogate model on 24,000 physics-based realizations, achieving an R² > 0.99 and MAE < 0.1 for temperature predictions at monitoring locations. This surrogate enabled an additional 100,000 realizations for global sensitivity analysis and probabilistic thermal resource assessment. The integrated stochastic physics–surrogate modeling framework offers a computationally efficient tool for quantifying uncertainty, identifying key drivers, and informing early-stage design decisions for Geobattery systems.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2026

CoLoRe-2LPT: Lyman-$α$ mock catalogues for the validation of DESI cosmological analyses

The Lyman-$α$ (Ly$α$) forest has become a crucial probe for studying the large-scale structure of the universe at high redshift ($z > 2$), providing powerful constraints on Baryon Acoustic Oscillations (BAO) and the full-shape (FS) clustering of matter. As a key ingredient for upcoming BAO and FS analyses, we present a new generation of fast cosmological Ly$α$ mocks based on second-order Lagrangian perturbation theory (2LPT). These new mocks significantly improve upon previous log-normal approaches, both at accurately capturing small scale clustering and at recovering the non-linear broadening of the BAO peak. They are able to reproduce Ly$α$ statistics within $10\%$ of the latest DESI measurement; including the Ly$α$ bias and the redshift-space distortion $β$ parameter, mean transmitted flux, and 1D power spectrum. The corresponding quasar (QSO) clustering is also improved with respect to previous approaches, calibrated against high-resolution Abacus simulations, recovering the observational QSO linear bias to less than $5\%$ and improving redshift-space distortions via 2LPT velocities and the addition of Fingers-of-God effects. Furthermore, these mocks incorporate high column density systems and metal lines, allowing us to explore the effects and systematics induced by these astrophysical contaminants. This new set of mocks has been key for enhancing the modeling and validation of the DESI DR2 Ly$α$ full shape cosmological analysis. This work provides a physically motivated and computationally efficient tool for simulating current and next-generation Ly$α$ surveys and validating FS and BAO analysis.

Bernal, M.F. Ruiz-Herrera [Madrid, CIEMAT] (ORCID:↗
DOE OSTI2022

Accelerating Simulation for High-Fidelity PV Inverter System Reliability Assessment with High-Performance Computing

The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.

high-performance computing↗
DOE OSTI2016

UQ Toolkit v. 3.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterization and propagation of uncertainties in computational models. This library provides Bayesian inference tools to infer uncertain model parameters, Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, methods for constructing Karhunen-Loève representations of stochastic processes, and global sensitivity analysis tools used to compute Sobol indices in order to characterize the importance of uncertain inputs or the interactions between them. The basis for many of these methods relies on representing random variables with Polynomial Chaos expansions (PCEs). This library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For the propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Sargsyan, Khachik↗
DOE OSTI2017

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Final Technical Report for Predictive Computing for Condensed Matter

This grant supported research into the development of computational methods to predict the properties of materials to high precision. The overarching purpose of such tools is to aid materials discovery by computation, without the need for costly materials synthesis or experimental measurements. A wide variety of computational tools were developed during this grant. Using these tools, we demonstrated that we could achieve the stated goal of computational modeling with predictive accuracy in several prototypical materials: a molecular crystal of benzene, a solid composed entirely of hydrogen, and a model solid intended to mimic the high temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

FEL Simulation Using Distributed Computing

While simulation tools are available and have been used regularly for simulating light sources, the increasing availability and lower cost of GPU-based processing opens up new opportunities. This poster highlights a method of how accelerating and parallelizing code processing through the use of COTS software interfaces.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

iMicrobe: Tools and data-driven discovery platform for the microbiome sciences

Background: Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. Findings: The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). Conclusions: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A comprehensive modeling method for proton exchange membrane electrolyzer development

Hydrogen attracts significant interests as an effective energy carrier that can be derived from renewable sources. Hydrogen production using a proton-exchange membrane (PEM) electrolyzer can efficiently convert renewable power via water splitting in wide scales—from large, centralized generation to on-site production. Mathematical models with multiple scales and fidelities facilitate the continuing improvements of PEM electrolyzer development to improve performance, cost, and reliability. The model scopes and methods are presented in this paper, which also introduces a comprehensive PEM electrolysis modeling tool based on computational fluid dynamics (CFD) software, ANSYS/Fluent. The modeling tool incorporates electrochemical model of a PEM electrolysis cell to simulate the performance of coupled thermal-fluid, species transport, and electrochemical processes in a product-scale cell or stack by leveraging the powerful meshing generation and CFD solver of ANSYS/Fluent. The thermal-fluid modeling includes liquid water/gas two-phase flow and simulates a PEM electrolysis cell by using Fluent user-defined functions as add-on modules accounting for PEM-specific species transport and electrochemical processes. The modeling outcomes expediate PEM electrolyzer scaling up from basic material development and laboratory testing.

08 HYDROGEN↗
DOE OSTI2017

Advanced Scientific Computing Research Exascale Requirements Review. An Office of Science review sponsored by Advanced Scientific Computing Research, September 27-29, 2016, Rockville, Maryland

The widespread use of computing in the American economy would not be possible without a thoughtful, exploratory research and development (R&D) community pushing the performance edge of operating systems, computer languages, and software libraries. These are the tools and building blocks — the hammers, chisels, bricks, and mortar — of the smartphone, the cloud, and the computing services on which we rely. Engineers and scientists need ever-more specialized computing tools to discover new material properties for manufacturing, make energy generation safer and more efficient, and provide insight into the fundamentals of the universe, for example. The research division of the U.S. Department of Energy’s (DOE’s) Office of Advanced Scientific Computing and Research (ASCR Research) ensures that these tools and building blocks are being developed and honed to meet the extreme needs of modern science. See also http://exascaleage.org/ascr/ for additional information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates

Characterizing glycans and glycoconjugates in the context of three-dimensional structures is important in understanding their biological roles and developing efficient therapeutic agents. Computational modeling and molecular simulation have become an essential tool complementary to experimental methods. In this work, we present a computational tool, Glycan Modeler for in silico N–/O –glycosylation of the target protein and generation of carbohydrate-only systems. In our previous study, we developed Glycan Reader , a web-based tool for detecting carbohydrate molecules from a PDB structure and generation of simulation system and input files. As integrated into Glycan Reader in CHARMM-GUI, Glycan Modeler (Glycan Reader & Modeler) enables to generate the structures of glycans and glycoconjugates for given glycan sequences and glycosylation sites using PDB glycan template structures from Glycan Fragment Database (http://glycanstructure.org/fragment-db). Our benchmark tests demonstrate the universal applicability of Glycan Reader & Modeler to various glycan sequences and target proteins. We also investigated the structural properties of modeled glycan structures by running 2-μs molecular dynamics simulations of HIV envelope protein. The simulations show that the modeled glycan structures built by Glycan Reader & Modeler have the similar structural features compared to the ones solved by X-ray crystallography. We also describe the representative examples of glycoconjugate modeling with video demos to illustrate the practical applications of Glycan Reader & Modeler . Glycan Reader & Modeler is freely available at http://charmm-gui.org/input/glycan.

59 BASIC BIOLOGICAL SCIENCES↗