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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The U.S. Department of Energy Computational Science Graduate Fellowship, 1991-2021: Follow-Up Study Shows Major Impact on Recipients and the Scientific Workforce

Since 1991, the U.S. Department of Energy Computational Science Graduate Fellowship (DOE CSGF) has addressed DOE National Laboratory needs as well as demands in the national workforce for trained professionals in computational science and engineering. Sponsored by the Department of Energy's Office of Science and the National Nuclear Security Administration, the DOE CSGF supports doctoral students in the pursuit of novel scientific or engineering discoveries using high-performance computing (HPC) resources. To meet the program’s core requirements, recipients participate in multidisciplinary studies, carry out at least one 12-week DOE laboratory research practicum, and contribute to an annual program review where the fellows present their research for sponsor review. The Krell Institute, which as managed the fellowship on behalf of the DOE since 1997, has commissioned several follow-up studies to examine the DOE CSGF recipients’ characteristics, fellows’ outcomes and professional accomplishments, alumni’s career paths and achievements, and recipients’ impact on national priorities through research and education.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The VTK-m User's Guide (V. 2.2)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
DOE OSTI2024

Focused Ion Beam Tomography of Alloy 617 Corroded in Molten Chloride Salt

Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials.

Copeland-Johnson, Trishelle↗
NASA NTRS2016

Overview of the NASA Glenn Flux Reconstruction Based High-Order Unstructured Grid Code

A computational fluid dynamics code based on the flux reconstruction (FR) method is currently being developed at NASA Glenn Research Center to ultimately provide a large- eddy simulation capability that is both accurate and efficient for complex aeropropulsion flows. The FR approach offers a simple and efficient method that is easy to implement and accurate to an arbitrary order on common grid cell geometries. The governing compressible Navier-Stokes equations are discretized in time using various explicit Runge-Kutta schemes, with the default being the 3-stage/3rd-order strong stability preserving scheme. The code is written in modern Fortran (i.e., Fortran 2008) and parallelization is attained through MPI for execution on distributed-memory high-performance computing systems. An h- refinement study of the isentropic Euler vortex problem is able to empirically demonstrate the capability of the FR method to achieve super-accuracy for inviscid flows. Additionally, the code is applied to the Taylor-Green vortex problem, performing numerous implicit large-eddy simulations across a range of grid resolutions and solution orders. The solution found by a pseudo-spectral code is commonly used as a reference solution to this problem, and the FR code is able to reproduce this solution using approximately the same grid resolution. Finally, an examination of the code's performance demonstrates good parallel scaling, as well as an implementation of the FR method with a computational cost/degree- of-freedom/time-step that is essentially independent of the solution order of accuracy for structured geometries.

High-Order Methods↗
DOE OSTI2026

ORCHA: A performance portability system for extreme heterogeneity

Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.

Lee, Youngjun↗
NASA NTRS2019

Computational Performance of Progressive Damage Analysis of Composite Laminates using Abaqus/Explicit with 16 to 512 CPU Cores

The computational scaling performance of progressive damage analysis using Abaqus/ Explicit is evaluated and quantified using from 16 to 512 CPU cores. Several analyses were conducted on varying numbers of cores to determine the scalability of the code on five NASA high performance computing systems. Two finite element models representative of typical models used for progressive damage analysis of composite laminates were used. The results indicate a 10 to 15 times speed up scaling from 24 to 512 cores. The run times were modestly reduced with newer generations of CPU hardware. If the number of degrees of freedom is held constant with respect to the number of cores, the model size can be increased by a factor of 20, scaling from 16 to 512 cores, with the same run time. An empirical expression was derived relating run time, the number of cores, and the number of degrees of freedom. Analysis cost was examined in terms of software tokens and hardware utilization. Using additional cores reduces token usage since the computational performance increases more rapidly than the token requirement with increasing number of cores. The in- crease in hardware cost with increasing cores was found to be modest. Overall the results show relatively good scalability of the Abaqus/Explicit code on up to 512 cores.

Bergan, A. C.↗
DOE OSTI2025

Aligned carbon nanotubes for 1,000x improvement in computing (CRADA Final Report)

As part of the Cyclotron Road program, the Aligned Carbon team worked to improve and bring to market their technology to manufacture carbon nanotubes (CNTs) for integrated circuits. There are multiple applications which can benefit from the use of CNT based transistors in integrated circuits, including high performance computing, RF data transmission, and biosensing. Each of these applications faces a problem of poor starting CNT material to come to market. Aligned Carbon offers material with the highest ceiling of performance based around a fully aligned, uniform, on-wafer growth of CNTs and our proprietary purification process to remove metallic CNTs leaving only the semi-conducting ones behind. Further, Aligned Carbon has been guided by scalable manufacturing processes that will fit seamlessly into the integrated circuit foundry model of chip fabrication.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Parallel aeroelastic computations for wing and wing-body configurations

The objective of this research is to develop computationally efficient methods for solving fluid-structural interaction problems by directly coupling finite difference Euler/Navier-Stokes equations for fluids and finite element dynamics equations for structures on parallel computers. This capability will significantly impact many aerospace projects of national importance such as Advanced Subsonic Civil Transport (ASCT), where the structural stability margin becomes very critical at the transonic region. This research effort will have direct impact on the High Performance Computing and Communication (HPCC) Program of NASA in the area of parallel computing.

Byun, Chansup↗
NASA NTRS1994

Applications of massively parallel computers in telemetry processing

Telemetry processing refers to the reconstruction of full resolution raw instrumentation data with artifacts, of space and ground recording and transmission, removed. Being the first processing phase of satellite data, this process is also referred to as level-zero processing. This study is aimed at investigating the use of massively parallel computing technology in providing level-zero processing to spaceflights that adhere to the recommendations of the Consultative Committee on Space Data Systems (CCSDS). The workload characteristics, of level-zero processing, are used to identify processing requirements in high-performance computing systems. An example of level-zero functions on a SIMD MPP, such as the MasPar, is discussed. The requirements in this paper are based in part on the Earth Observing System (EOS) Data and Operation System (EDOS).

El-Ghazawi, Tarek A.↗
NASA NTRS2002

Load Balancing Strategies for Multi-Block Overset Grid Applications

The multi-block overset grid method is a powerful technique for high-fidelity computational fluid dynamics (CFD) simulations about complex aerospace configurations. The solution process uses a grid system that discretizes the problem domain by using separately generated but overlapping structured grids that periodically update and exchange boundary information through interpolation. For efficient high performance computations of large-scale realistic applications using this methodology, the individual grids must be properly partitioned among the parallel processors. Overall performance, therefore, largely depends on the quality of load balancing. In this paper, we present three different load balancing strategies far overset grids and analyze their effects on the parallel efficiency of a Navier-Stokes CFD application running on an SGI Origin2000 machine.

Djomehri, M. Jahed↗
DOE OSTI2025

Nek5000/RS performance on advanced GPU architectures

The authors explore performance scalability of the open-source thermal-fluids code, NekRS, on the U.S. Department of Energy's leadership computers, Crusher, Frontier, Summit, Perlmutter, and Polaris. Particular attention is given to analyzing performance and time-to-solution at the strong-scale limit for a target efficiency of 80%, which is typical for production runs on the DOE's high-performance computing systems. Several examples of anomalous behavior are also discussed and analyzed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Parallel Domain Decomposition Formulation and Software for Large-Scale Sparse Symmetrical/Unsymmetrical Aeroacoustic Applications

The overall objectives of this research work are to formulate and validate efficient parallel algorithms, and to efficiently design/implement computer software for solving large-scale acoustic problems, arised from the unified frameworks of the finite element procedures. The adopted parallel Finite Element (FE) Domain Decomposition (DD) procedures should fully take advantages of multiple processing capabilities offered by most modern high performance computing platforms for efficient parallel computation. To achieve this objective. the formulation needs to integrate efficient sparse (and dense) assembly techniques, hybrid (or mixed) direct and iterative equation solvers, proper pre-conditioned strategies, unrolling strategies, and effective processors' communicating schemes. Finally, the numerical performance of the developed parallel finite element procedures will be evaluated by solving series of structural, and acoustic (symmetrical and un-symmetrical) problems (in different computing platforms). Comparisons with existing "commercialized" and/or "public domain" software are also included, whenever possible.

Nguyen, D. T.↗
NASA NTRSDate not supplied

Advances in PSP Testing in LaRC High Reynolds Number Facilities

The use of luminescent coatings for the global measurement of surface aerodynamic properties at full-flight Reynolds numbers has been ongoing at the NASA Langley Research Center since the late 1990s, beginning with Temperature Sensitive Paint (TSP) for boundary layer analysis at the 0.3-m Transonic Cryogenics Tunnel. Since then, significant work has been made to extend these measurements to pressure using Pressure Sensitive Paint (PSP) as well as develop systems for application in larger scale, full-flight Reynolds number facilities, such as the National Transonic Facility (NTF). Recently, a system for the measurement of time-resolved pressure using recently developed unsteady PSP (uPSP) formulations has been designed and implemented in the Transonic Dynamics Tunnel (TDT). The use of PSP (or uPSP) in either of these large scale, full-fight Reynolds number wind tunnels is complicated by the fact that both facilities operate best in oxygen deficient environments; however, the PSP technique relies on the presence of oxygen to work. The NTF is typically operated in cryogenic conditions, which is achieved using liquid nitrogen. In this facility, temperatures can reach as low as 116 K (-250 °F) with nominal oxygen concentrations of less than 50 ppm. This has resulted in the development of specialized PSP formulations that can operate in the cryogenic environment with the introduction of low amounts of oxygen (typically less than 2000 ppm) for PSP response. Likewise, to achieve full-flight Reynolds number conditions in the TDT, the atmosphere in the tunnel is replaced with a “heavy gas” of R-134A (1,1,1,2-tetrafluoroethane), which also has minimal oxygen native in the flow. The use of uPSP in this case will also depend on the introduction of trace amounts of oxygen, with the precise concentrations to be determined in an upcoming test. This presentation will describe some recent advancements that have been made for PSP measurements in both facilities. For the NTF system, there has been significant development of enhanced lighting for use at cryogenic conditions, as well as improvements in the application efficiency of the PSP. Furthermore, there are efforts underway to incorporate advanced data analysis techniques to acquire additional surface aerodynamic properties using the PSP technique that can yield not only improved experimental efficiency, but also provide data needed for next generation vehicle design and development. For the uPSP system in TDT, significant efforts to improve the data transfer rates to a high-performance computing environment for analysis are underway and performance and initial results from the system in the heavy-gas environment will be presented.

Pressure Sensitive Paint↗
DOE OSTI2025

A Hybrid Dynamic/Steady-State Tool With Protection Simulation for Cascading-Outage Analysis of Extreme Events in Power Systems

The bulk electric power grid is subject to vulnerabilities from component outages, which in certain combinations (extreme events) might lead to cascading outages. Some of these outages can be severe enough to trigger brownouts and blackouts. Much is known about mitigating the first few failures near the beginning of a cascade, but there are few established methods and tools for directly analyzing the risks of cascading component outages over a longer time scale. Current power system tools have limited ability to perform detailed and accurate cascading-outage analysis, which could be computationally intensive. The Dynamic Contingency Analysis Tool (DCAT) enables power system planning engineers to more realistically assess the consequences of extreme contingencies and potential cascading events across their systems and interconnections. DCAT has several unique features: (i) detailed hybrid dynamic and steady-state analysis of power systems to mimic real-world cascading outages, (ii) detailed modeling of protection systems embedded in the dynamic simulation, (iii) simulation of corrective action after transients, (iv) simulation of islanding , and (v) high-performance computing capability to simulate a large number of contingencies in a reasonable time. DCAT outputs will help find technically sound solutions to reduce the risk of cascading outages. This paper provides details of DCAT methodology and shows its capabilities with extreme events on real-world cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The Viskores User's Guide, Release 1.1

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created Viskores: the visualization toolkit for multi/many-core architectures. Viskores supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. Viskores also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although Viskores provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Viskores User's Guide (V.1.0)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created Viskores: the visualization toolkit for multi-/many-core architectures. Viskores supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. Viskores also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although Viskores provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction.

97 MATHEMATICS AND COMPUTING↗