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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 253 records · Page 14

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DOE OSTI2026

Part-scale evolution of fine-scale microstructural heterogeneity in solid-state additive manufacturing

Current solid-state additive manufacturing methods, refined through costly and time-consuming trial and error, have spurred interest in computational models that replicate material behavior under typical thermomechanical conditions (e.g., strain-rate ~ 102 s−1). These models, however, struggle to capture time-dependent microstructural evolution. In this work, Additive Friction-Stir Deposition (AFSD) is used as a representative case study for part-scale quantification of microstructure evolution at a fine spatial resolution (200 μm) by examining a liquid-nitrogen-cooled stop-action build via energy-dispersive X-ray diffraction coupled with a multi-channel detector. These results inform modeling efforts by linking process asymmetry to stored plastic strain, residual elastic strain, and texture development, and unlike current state-of-the-art characterization methods (e.g., EBSD or neutron diffraction), this approach provides both the spatial resolution and collection efficiency necessary to quantify fine-scale microstructural heterogeneity over large component volumes. As such, this technique provides essential validation data for computational models, e.g., crystal plasticity, enabling future prediction of heterogeneous behavior in AFSD and other additive manufacturing processes.

Franz, Cole [ORNL] (ORCID:0000000213465881)↗

NASA NTRSDate not supplied

Modelling Sonic Boom Propagation through Planetary Boundary Layer Turbulence near the Lateral Extent of the Carpet

Nonlinear acoustic propagation of sonic booms in the atmospheric boundary layer is considered in the context of a one-way solution to a third order wave equation. A split-step approach is utilized to compute different physical effects efficiently. Unlike previous approaches, the diffraction effects are computed exactly in the forward direction with no restriction on the angle of propagation. This results in more accurate modelling of sonic boom near the carpet edge. Heterogenous flow effects are incorporated with a wide-angle parabolic approximation. Nonlinear propagation is computed with a Burgers-Hayes method. Turbulence in the medium is constructed by the method of random Fourier modes. The turbulence spectra are constructed using an altitude dependent 3-dimensional von Karman spectrum. The turbulence field is considered frozen, as the eddy turnover time in the atmospheric boundary layer is generally much larger than the propagation time of the acoustic wave. Comparisons with benchmark predictions from the PCBoom software are conducted. The benchmark cases provide insight into the accuracy of the current prediction code for non-turbulent atmospheres. Future work is discussed regarding the prediction of shaped booms, boundary conditions, and predictions near the lateral extent of the boom carpet where previous approaches fail to capture the diffraction effects.

sonic boom↗

DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗

DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Here, we present a novel physics-informed neural networks (PINNs) framework for modeling interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks for any two subdomains separated by a sharp interface such that the neural networks differ only through their activation functions while the other parameters remain identical. The performance of I-PINNs, conventional PINNs, and other existing domain-decomposition PINNs methods such as extended PINNs (XPINNs) and multi-domain PINN (M-PINN) is compared through several one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems. The results demonstrate that I-PINNs provides a root-mean-square-error accuracy, at least two orders of magnitude better than conventional PINNs and XPINNs at approximately one-tenth of the computational cost of conventional PINNs and half the cost of XPINNs. Additionally, while I-PINNs and M-PINN provide comparable accuracies, M-PINN is found to be approximately 50% more expensive.

42 ENGINEERING↗

NASA NTRS1983

Human capabilities: Long residence - An uncharted field

In the context of the planned NASA space station, it is anticipated that a number of problems will be posed by permanent residence in space. Not only will the intensive training which has characterized earlier manned programs diminish under the pressure of increased launch rates, but the new and larger crews will socially, educationally, psychologically and physiologically constitute a far more heterogeneous group than those of the past. In addition, advanced computer technology will result in a degree of reliance on onboard data processing which will alter the relations of ground and space operators, requiring careful integration of people and technologies. Attention is given to questions of food, robotics, extravehicular activity, hygiene and environmental noise.

Chambers, A.↗

NASA NTRS1990

Finite-element models of continental extension

Numerical models of the initial deformation of extending continental lithosphere, computed to investigate the control of preexisting thermal and mechanical heterogeneities on the style of deformation, are presented. The finite element method is used to calculate deformation with a viscoelastic-plastic model for the lithosphere. Comparisons of the results of analytic models and finite-element models using this method show that good results may be obtained by the numerical technique, even with elements containing both brittle and viscoelastic sampling points. It is shown that the gross style of initial extensional deformation is controlled by the depth and width of the initial heterogeneity which localizes deformation.

Lynch, H. David↗

DOE OSTI2025

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗

DOE OSTI2025

Repositioning Quantum Cellular Automata for Dependable Quantum-Classical Systems

Quantum Cellular Automata (QCA) provides a structured model of distributed quantum computation with inherent locality and regularity properties that are suited to dependable execution. However, QCA remain largely absent from discussions on reproducibility, fault management, and orchestration in heterogeneous quantum-classical systems. We propose a dual-axis framework that situates QCA within both computation and physical realizability, revealing regions where robust, scalable, and hardware-constrained quantum dynamics may reside. By revisiting prior results through the lens of reproducibility and architecture resilience, we suggest that QCA offers a potential substrate for benchmarking and system-level co-design.

Stapleton, Nicholas [ORNL] (ORCID:0000000335305325↗

DOE OSTI2026

Trilinos: Enabling Scientific Computing across Diverse Hardware Architectures at Scale

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to “An Overview of the Trilinos project” by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This article also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.

Heterogeneous Hardware Architectures↗

DOE OSTI2024

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage.

database↗

DOE OSTI2026

Data-driven analysis to understand GPU hardware resource usage of optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money in expensive accelerators. Although related work develops optimizations to improve application performance, none studies how these optimizations impact hardware resource usage or average GPU utilization. Here, this paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multiobjective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization, and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

Computer science↗

DOE OSTI2026

Future Generation High Performance Computing Center (FG-HPCC): RFI Technical Considerations

Lawrence Livermore National Security, LLC (LLNS) is interested in receiving information about technologies that could be available in the 2029-2030 timeframe that may serve to enable the vision for a Future Generation High Performance Computing (HPC) Center (FG-HPCC) described in this document. The future HPC Center vision has been conceived to meet the future mission needs of the Advanced Simulation and Computing (ASC) Program within the National Nuclear Security Administration (NNSA). LLNS envisions a center composed not of many independent clusters, but of heterogeneous elements accessible to users as a single system. The capabilities will be integrated to create a scalable, flexible, yet tightly coupled computing center capable of integrated HPC, AI, and cloud-like workloads.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1982

NASTRAN of the HEP

Preliminary plans for the conversion of NASTRAN to DENELCOR's heterogeneous element processor (HEP) are presented. First, a brief history of computer architecture and the architecture of the HEP computer system are discussed. Second, a description of the HEP computer system is provided. Lastly, the preliminary NASTRAN conversion plans for link management, I/O management, memory management, and code optimization are discussed.

Brown, W. K.↗

NASA NTRS1994

Data communication network at the ASRM facility

This three-year project (February 1991 to February 1994) has involved analyzing and helping to design the communication network for the Advanced Solid Rocket Motor (ASRM) facility at Yellow Creek, near Iuka, MS. The principal concerns in the analysis were the bandwidth (both on average and in the worst case) and the expandability of the network. As the communication network was designed and modified, a careful evaluation of the bandwidth of the network, the capabilities of the protocol, and the requirements of the controllers and computers on the network was required. The overall network, which was heterogeneous in protocol and bandwidth, needed to be modeled, analyzed, and simulated to obtain some degree of confidence in its performance capabilities and in its performance under nominal and heavy loads. The results of our analysis did have an impact on the design and operation of the ASRM facility. During 1993 we analyzed many configurations of this basic network structure. The analyses are described in detail in Section 2 and 3 herein. Section 2 reports on an analysis of the whole network. The preliminary results of that research indicated that the most likely bottleneck as the network traffic increased would be the hubs. Thus a study of Cabletron hubs was initiated. The results of that study are in Section 3. Section 4 herein reports on the final network configuration analyzed. When the ASRM facility was mothballed in December of 1993, this was basically the planned and partially installed network. A briefing was held at NASA/MSFC on December 7, 1993, at which time our final analysis and conclusions were disseminated. This report contains a written record of most of the information disseminated at that briefing.

Moorhead, Robert J., III↗

NASA NTRS1994

Advanced information processing system: Authentication protocols for network communication

In safety critical I/O and intercomputer communication networks, reliable message transmission is an important concern. Difficulties of communication and fault identification in networks arise primarily because the sender of a transmission cannot be identified with certainty, an intermediate node can corrupt a message without certainty of detection, and a babbling node cannot be identified and silenced without lengthy diagnosis and reconfiguration . Authentication protocols use digital signature techniques to verify the authenticity of messages with high probability. Such protocols appear to provide an efficient solution to many of these problems. The objective of this program is to develop, demonstrate, and evaluate intercomputer communication architectures which employ authentication. As a context for the evaluation, the authentication protocol-based communication concept was demonstrated under this program by hosting a real-time flight critical guidance, navigation and control algorithm on a distributed, heterogeneous, mixed redundancy system of workstations and embedded fault-tolerant computers.

Harper, Richard E.↗

NASA NTRS1992

A computer model for one-dimensional mass and energy transport in and around chemically reacting particles, including complex gas-phase chemistry, multicomponent molecular diffusion, surface evaporation, and heterogeneous reaction

Various chemically reacting flow problems highlighting chemical and physical fundamentals rather than flow geometry are presently investigated by means of a comprehensive mathematical model that incorporates multicomponent molecular diffusion, complex chemistry, and heterogeneous processes, in the interest of obtaining sensitivity-related information. The sensitivity equations were decoupled from those of the model, and then integrated one time-step behind the integration of the model equations, and analytical Jacobian matrices were applied to improve the accuracy of sensitivity coefficients that are calculated together with model solutions.

Cho, S. Y.↗