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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

VESIcal Part I: An open-source thermodynamic model engine for mixed volatile (H2O-CO2) solubility in silicate melts

Thermodynamics has been fundamental to the interpretation of geologic data and modeling of geologic systems for decades. However, more recent advancements in computational capabilities and a marked increase in researchers’ accessibility to computing tools has outpaced the functionality and extensibility of currently available modeling tools. Here we present VESIcal (Volatile Equilibria and Saturation Identification calculator): the first comprehensive modeling tool for H 2 O, CO 2 , and mixed (H 2 O-CO 2 ) solubility in silicate melts that: a) allows users access to seven commonly used models, plus easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an excel spreadsheet for simple post-modeling analysis; e) integrates advanced plotting capabilities directly within the tool; and f) provides all of these within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python. The tool is presented within this manuscript, which is a Jupyter notebook containing worked examples accessible to python users with a range of skill levels. The basic functions of VESIcal can also be access via a web app (https://vesical.anvil.app). The VESIcal python library is open-source and available for download at https://github.com/kaylai/VESIcal.

K. Iacovino↗
DOE OSTI2022

Development of a Data Platform for High-Temperature Gas-cooled Reactor (HTGR) Nuclear Energy University Program (NEUP) Thermal-Fluid Experiments

Since the U.S. Department of Energy (DOE)’s Office of Nuclear Energy (NE) initiated the Nuclear Energy University Program (NEUP) in 2009, a total of 30 NEUP projects focused on High-Temperature Gas-cooled Reactor (HTGR) thermal-fluid experiments were funded up to fiscal year (FY) 2021. This represents a total DOE investment of approximately $23M over the 12-year period, covering thermal fluid phenomena important to both pebble bed and prismatic HTGR designs. The NEUP projects have produced a large amount of high-quality experimental and computational data that were published in final project reports, journal articles, dissertations, and conference proceedings, but in most cases the actual data sets and supporting information such as facility and instrumentation descriptions were not publicly disseminated to the HTGR community. To the authors’ best knowledge, a data platform that organizes and summarizes these NEUP-funded projects for HTGR research does not currently exist. To improve access to this HTGR validation data and optimize the return on the significant investment made by DOE, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects with the aim of developing a public-access data platform that can be used to retrieve computational fluid dynamics (CFD) and system code validation data and guide future NEUP investments. This paper summarizes the status of the current ART-GCR database, provides an overview of the NEUP-funded HTGR-related research projects from FY2009 to FY2021 and identify validation knowledge gaps still existing in HTGR thermal-fluid research.

42 ENGINEERING↗
DOE OSTI2017

Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive

Metagenomics, the study of all microbial species cohabitants in an environment, usually produces large amount of sequence data varying from several GBs to a few TBs. Analyzing metagenomics data includes both data-intensive and compute-intensive steps, making the entire process hard to scale. Here we aim to optimize a metagenomics application that partitions the shortgun metagenomics sequences based on their species of origin. Our solution combines MapReduce-based BioPig analytic toolkit with MPI to provide scalability in respective to both data and compute. We also made some improvements to the existing BioPig toolkit by using simplified data types and compressed k-mer storage. These optimizations leads up to 193× speedup for the computing-intensive step and 9.6× speedup over the entire pipeline. Our optimized application is also capable of processing datasets that are 16 times larger on the same hardware platform. These conclusions indicate integrating heterogeneous technologies such as Hadoop and MPI is quite efficient to solve large genomics problems that are both data-intensive and compute-intensive.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Further Investigation of the Support System Effects and Wing Twist on the NASA Common Research Model

An experimental investigation of the NASA Common Research Model was conducted in the NASA Langley National Transonic Facility and NASA Ames 11-foot Transonic Wind Tunnel Facility for use in the Drag Prediction Workshop. As data from the experimental investigations was collected, a large difference in moment values was seen between the experiment and computational data from the 4th Drag Prediction Workshop. This difference led to a computational assessment to investigate model support system interference effects on the Common Research Model. The results from this investigation showed that the addition of the support system to the computational cases did increase the pitching moment so that it more closely matched the experimental results, but there was still a large discrepancy in pitching moment. This large discrepancy led to an investigation into the shape of the as-built model, which in turn led to a change in the computational grids and re-running of all the previous support system cases. The results of these cases are the focus of this paper.

Rivers, Melissa B.↗
DOE OSTI2024

FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data

The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗
NASA NTRS1975

Dispersion analysis techniques within the space vehicle dynamics simulation program

The Space Vehicle Dynamics Simulation (SVDS) program was evaluated as a dispersion analysis tool. The Linear Error Analysis (LEA) post processor was examined in detail and simulation techniques relative to conducting a dispersion analysis using the SVDS were considered. The LEA processor is a tool for correlating trajectory dispersion data developed by simulating 3 sigma uncertainties as single error source cases. The processor combines trajectory and performance deviations by a root-sum-square (RSS process) and develops a covariance matrix for the deviations. Results are used in dispersion analyses for the baseline reference and orbiter flight test missions. As a part of this study, LEA results were verified as follows: (A) Hand calculating the RSS data and the elements of the covariance matrix for comparison with the LEA processor computed data. (B) Comparing results with previous error analyses. The LEA comparisons and verification are made at main engine cutoff (MECO).

Snow, L. S.↗
NASA NTRS1978

A new two-dimensional oscillating wing apparatus for unsteady aerodynamics research

An apparatus for experimental research into unsteady transonic flows is described. The apparatus, as installed in the NASA-Ames 11 by 11 Foot Transonic Wind Tunnel, can impart full two-degree-of-freedom motions at reduced frequencies to 0.3, oscillatory amplitudes to tn-2 degs, mean angles to 12 degs, Mach numbers to 1.4 and Reynolds numbers to 12x10. The test wing is fully instrumented for dynamic waveform measurements and the data can be acquired, processed, and displayed in real-time with a new computational data acquisition system. Following a description of the apparatus, sample data from a recently completed test program is presented.

Davis, S. S.↗
NASA NTRS1998

Evaluation of Methods for Multidisciplinary Design Optimization (MDO)

The NASA Langley Multidisciplinary Design Optimization (MDO) method evaluation study seeks to arrive at a set of guidelines for using promising MDO methods by accumulating and analyzing computational data for such methods. The data are collected by conducting a series of reproducible experiments. This report documents all computational experiments conducted in Phase I of the study. This report is a companion to the paper titled Initial Results of an MDO Method Evaluation Study by N. M. Alexandrov and S. Kodiyalam (AIAA-98-4884).

Kodiyalam, Srinivas↗
NASA NTRS1999

Backus Effect on a Perpendicular Errors in Harmonic Models of Real vs. Synthetic Data

Measurements of geomagnetic scalar intensity on a thin spherical shell alone are not enough to separate internal from external source fields; moreover, such scalar data are not enough for accurate modeling of the vector field from internal sources because of unmodeled fields and small data errors. Spherical harmonic models of the geomagnetic potential fitted to scalar data alone therefore suffer from well-understood Backus effect and perpendicular errors. Curiously, errors in some models of simulated 'data' are very much less than those in models of real data. We analyze select Magsat vector and scalar measurements separately to illustrate Backus effect and perpendicular errors in models of real scalar data. By using a model to synthesize 'data' at the observation points, and by adding various types of 'noise', we illustrate such errors in models of synthetic 'data'. Perpendicular errors prove quite sensitive to the maximum degree in the spherical harmonic expansion of the potential field model fitted to the scalar data. Small errors in models of synthetic 'data' are found to be an artifact of matched truncation levels. For example, consider scalar synthetic 'data' computed from a degree 14 model. A degree 14 model fitted to such synthetic 'data' yields negligible error, but amplifies 4 nT (rmss) added noise into a 60 nT error (rmss); however, a degree 12 model fitted to the noisy 'data' suffers a 492 nT error (rmms through degree 12). Geomagnetic measurements remain unaware of model truncation, so the small errors indicated by some simulations cannot be realized in practice. Errors in models fitted to scalar data alone approach 1000 nT (rmss) and several thousand nT (maximum).

Voorhies, C. V.↗
NASA NTRS1994

Dynamic Load Balancing for Computational Plasticity on Parallel Computers

The simulation of the computational plasticity on a complex structure remains a formidable computational task, especially when a highly nonlinear, complex material model was used. It appears that the computational requirements for a such problem can only be satisfied by massively parallel architectures. In order to effectively harness the tremendous computational power provided by such architectures, it is imperative to investigate and to study the algorithmic and implementation issues pertaining to dynamic load balancing for computational plasticity on a highly parallel, distributed-memory, multiple-instruction, multiple-data computers. This paper will measure the effectiveness of the algorithms developed in handling the dynamic load balancing.

Pramono, Eddy↗
DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
NASA NTRS2017

Computational Modeling to Limit the Impact Displays and Indicator Lights Have on Habitable Volume Operational Lighting Constraints

NASA has demonstrated an interest in improving astronaut health and performance through the installment of a new lighting countermeasure on the International Space Station. The Solid State Lighting Assembly (SSLA) system is designed to positively influence astronaut health by providing a daily change to light spectrum to improve circadian entrainment. Unfortunately, existing NASA standards and requirements define ambient light level requirements for crew sleep and other tasks, yet the number of light-emitting diode (LED) indicators and displays within a habitable volume is currently uncontrolled. Because each of these light sources has its own unique spectral properties, the additive lighting environment ends up becoming something different from what was planned or researched. Restricting the use of displays and indicators is not a solution because these systems provide beneficial feedback to the crew. The research team for this grant used computer-based computational modeling and real-world lighting mockups to document the impact that light sources other than the ambient lighting system contribute to the ambient spectral lighting environment. In particular, the team was focused on understanding the impacts of long-term tasks located in front of avionics or computer displays. The team also wanted to understand options for mitigating the changes to the ambient light spectrum in the interest of maintaining the performance of a lighting countermeasure. The project utilized a variety of physical and computer-based simulations to determine direct relationships between system implementation and light spectrum. Using real-world data, computer models were built in the commercially available optics analysis software Zemax Optics Studio(c). The team also built a mockup test facility that had the same volume and configuration as one of the Zemax models. The team collected over 1200 spectral irradiance measurements, each representing a different configuration of the mockup. Analysis of the data showed a measurable impact on ambient light spectrum. This data showed that obvious design techniques exist that can be used to bind the ambient light spectrum closer to the planned spectral operating environment for the observer's eye point. The following observations should be considered when designing an operational environment that is dominated by computer displays. When more light is directed into the field of view of the observer, the greater the impact it will make on various human factors issues that depend on spectral shape and intensity. Because viewing angle has a large part to play in the amount of light flux on the crewmember's retina, beam shape, combined with light source location is an important factor for determining percent probable incident flux on the observer from any combination of light sources. Computer graphics design and display lumen output are major factors influencing the amount of spectrally intense light projected into the environment and in the viewer's direction. Use of adjustable white point display software was useful only if the predominant background color was white and if it matched the ambient light system's color. Display graphics that used a predominantly black background had the least influence on unplanned spectral energy projected into the environment. Percent reflectance makes a difference in total energy reflected back into an environment, and within certain architectural geometries, reflectance can be used to control the amount of a light spectrum that is allowed to perpetuate in the environment. Data showed that room volume and distance from significant light sources influence the total spectrum in a room. Smaller environments had a homogenizing effect on total light spectrum, whereas light from multiple sources in larger environments was less mixed. The findings indicated above should be considered when making recommendations for practice or standards for architectural systems. The ambient lighting system, surface reflectance, and display and indicator implementation all factor into the users' spectral environment. A variety of low-cost solutions exist to mitigate the impact of light from non-architectural lighting systems, and much potential for system automation and integration of display systems with the ambient environment. This team believes that proper planning can be used to avoid integration problems and also believes that human-in-the-loop evaluations, real-world test and measurement, and computer modeling can be used to determine how changes to a process, display graphics, and architecture will help maintain the planned spectral operating lighting environment.

Clark, T. A.↗
DOE OSTI2022

ASC FY2023 Implementation Plan Revision 0

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) Program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Advanced Simulation and Computing FY20 Implementation Plan (Rev. 0)

The Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, surety, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable the National Nuclear Security Administration (NNSA) to support stockpile requirements. The ultimate goal of the Stewardship Program, and thus the Advanced Simulation and Computing (ASC) Program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗