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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗
DOE OSTI2019

Simultaneous inversion of shear modulus and traction boundary conditions in biomechanical imaging

We present a formulation to simultaneously invert for a heterogeneous shear modulus field and traction boundary conditions in an incompressible linear elastic plane stress model. Our approach utilizes scalable deterministic methods, including adjoint-based sensitivities and quasi-Newton optimization, to reduce the computational requirements for large-scale inversion with partial differential equation (PDE) constraints. Here, we address the use of regularization for such formulations and explore the use of different types of regularization for the shear modulus and boundary traction. We apply this PDE-constrained optimization algorithm to a synthetic dataset to verify the accuracy in the reconstructed parameters, and to experimental data from a tissue-mimicking ultrasound phantom. In all of these examples, we compare inversion results from full-field and sparse data measurements.

42 ENGINEERING↗
DOE OSTI2014

Performance Impact of I/O on QMCPack Simulations at the Petascale and Beyond

Traditional petascale applications, such as QMCPack, can scale their computations to completely utilize modern supercomputers like Titan, but they cannot scale their I/O. To preserve scalability, scientists cannot save data at the granularity needed to enable scientific discovery and are forced to use large intervals between two checkpoint calls. In this paper, we work to increase the granularity of the I/O in QMCPack simulations without increasing the I/O associated overhead or compromising the scalability of the simulations. Our solution redesigns the I/O algorithms used by QMCPack to gather finer-grained data at high frequencies and integrate the ADIOS API to select effective I/O methods without major code changes. The extension of a tool such as Skel to mimic the variable I/O in QMCPack allows us to predict the I/O performance of the code when using ADIOS methods at the petascale. We show how I/O libraries like ADIOS allow us to increase the amount of scientific data extracted from QMCPack simulations at the granularity desired by the scientists while keeping the I/O overhead below 10%. We also show how the impact of checkpoint I/O for the QMCPack code using ADIOS is below 5% when using preventive tactics for checkpointing at the petascale and beyond.

Herbein, Stephen N↗
DOE OSTI2019

Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer

The rapid growth and wide applicability of Deep Learning (DL) frameworks poses challenges to computing centers which need to deploy and support the software, and also to domain scientists who have to keep up with the system environment and scale up scientific exploration through DL. We offer recommendations for deploying and scaling DL frameworks on the Summit supercomputer, currently atop the Top500 list, at the Oak Ridge National Laboratory Leadership Computing Facility (OLCF). We discuss DL software deployment in the form of containers, and compare performance of native-built frameworks and containerized deployment. Software containers show no noticeable negative performance impact and exhibit faster Python loading times and promise easier maintenance. To explore strategies for scaling up DL model training campaigns, we assess DL compute kernel performance, discuss and recommend I/O data formats and staging, and identify communication needs for scalable message exchange for DL runs at scale. We recommend that users take a step-wise tuning approach beginning with algorithmic kernel choice, node I/O configuration, and communications tuning as best-practice. We present baseline examples of scaling efficiency 87% for a DL run of ResNet50 running on 1024 nodes (6144 V100 GPUs).

Yin, Junqi↗
DOE OSTI2018

NERSC Slurm Software Configurations and Plugins

This software can be used to tune the HPC operations of a Cray system running Slurm workload manager software. This includes many of the customizations that NERSC has made over the past three years. In particular these customizations allow for complex site-local policies to be implemented and managed in a scalable way, or add unique capabilities to slurm via our plugins, including: * performance software integration and compute node staging * temporary directory/dynamic user content management automation * software defined networking integration

Jacobsen, Douglas↗
DOE OSTI2021

A Geometry-Driven Longitudal Topic Model

A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods. Dimensionality reduction tools from computational geometry are applied to learn the intrinsic manifold on which the latent, temporal topics reside. Then shortest path distances on the manifold are used to link together these topics. The proposed framework permits visualization of the low-dimensional embedding which provides clear interpretation of the complex, high-dimensional trajectories that may exist among latent topics. Practical application of the proposed framework is demonstrated through its ability to capture and effectively visualize natural progression of latent COVID-19 related topics learned from Twitter data. Interpretability of the trajectories is achieved by comparing to real-world events. In addition, the framework permits study of spatial variation in Twitter behavior for learned topics. The analysis demonstrates that the proposed framework is able to capture granular-level impact of COVID-19 on public discussions. We end by arguing that Twitter data, when analyzed within the proposed framework, can serve as a valuable supplementary data stream for COVID-related studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Multi-Instance learning Framework for Seismic Detectors

In this report, we construct and test a framework for fusing the predictions of a ensemble of seismic wave detectors. The framework is drawn from multi-instance learning and is meant to improve the predictive skill of the ensemble beyond that of the individual detectors. We show how the framework allows the use of multiple features derived from the seismogram to detect seismic wave arrivals, as well as how it allows only the most informative features to be retained in the ensemble. The computational cost of the "ensembling" method is linear in the size of the ensemble, allowing a scalable method for monitoring multiple features/transformations of a seismogram. The framework is tested on teleseismic and regional p-wave arrivals at the IMS (International Monitoring System) station in Warramunga, NT, Australia and the PNSU station in University of Utah's monitoring network.

58 GEOSCIENCES↗
NASA NTRS2003

An Overview Of NASA's Solar Sail Propulsion Project

Research conducted by the In-Space Propulsion (ISP) Technologies Projects is at the forefront of NASA's efforts to mature propulsion technologies that will enable or enhance a variety of space science missions. The ISP Program is developing technologies from a Technology Readiness Level (TRL) of 3 through TRL 6. Activities under the different technology areas are selected through the NASA Research Announcement (NRA) process. The ISP Program goal is to mature a suite of reliable advanced propulsion technologies that will promote more cost efficient missions through the reduction of interplanetary mission trip time, increased scientific payload mass fraction, and allowing for longer on-station operations. These propulsion technologies will also enable missions with previously inaccessible orbits (e.g., non-Keplerian, high solar latitudes). The ISP Program technology suite has been prioritized by an agency wide study. Solar Sail propulsion is one of ISP's three high-priority technology areas. Solar sail propulsion systems will be required to meet the challenge of monitoring and predicting space weather by the Office of Space Science s (OSS) Living with a Star (LWS) program. Near-to-mid-term mission needs include monitoring of solar activity and observations at high solar latitudes. Near-term work funded by the ISP solar sail propulsion project is centered around the quantitative demonstration of scalability of present solar sail subsystem designs and concepts to future mission requirements through ground testing, computer modeling and analytical simulations. This talk will review the solar sail technology roadmap, current funded technology development work, future funding opportunities, and mission applications.

Garbe, Gregory↗
NASA NTRS2003

HYDRA : High-speed simulation architecture for precision spacecraft formation simulation

e Hierarchical Distributed Reconfigurable Architecture- is a scalable simulation architecture that provides flexibility and ease-of-use which take advantage of modern computation and communication hardware. It also provides the ability to implement distributed - or workstation - based simulations and high-fidelity real-time simulation from a common core. Originally designed to serve as a research platform for examining fundamental challenges in formation flying simulation for future space missions, it is also finding use in other missions and applications, all of which can take advantage of the underlying Object-Oriented structure to easily produce distributed simulations. Hydra automates the process of connecting disparate simulation components (Hydra Clients) through a client server architecture that uses high-level descriptions of data associated with each client to find and forge desirable connections (Hydra Services) at run time. Services communicate through the use of Connectors, which abstract messaging to provide single-interface access to any desired communication protocol, such as from shared-memory message passing to TCP/IP to ACE and COBRA. Hydra shares many features with the HLA, although providing more flexibility in connectivity services and behavior overriding.

formation flying↗
DOE OSTI2022

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Fast Fault Injection and Sensitivity Analysis for Collective Communications, In: 2015 IEEE International Conference on Cluster Computing

The collective communication operations, which are widely used in parallel applications for global communication and synchronization are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack of fast evaluation method to investigate the impacts of faulty collective operations. The traditional random fault injection methods relying on a large amount of fault injection tests to ensure statistical significance require a significant amount of resources and time. These methods result in prohibitive evaluation cost when applied to the collectives. In this paper, we introduce a novel tool named Fast Fault Injection and Sensitivity Analysis Tool (FastFIT) to conduct fast fault injection and characterize the application sensitivity to faulty collectives. The tool achieves fast exploration by reducing the exploration space and predicting the application sensitivity using Machine Learning (ML) techniques. A basis for these techniques are implicit correlations between MPI semantics, application context, critical application features, and application responses to faulty collective communications. The experimental results show that our approach reduces the fault injection points and tests by 97% for representative benchmarks (NAS Parallel Benchmarks (NPB)) and a realistic application (Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)) on a production supercomputer. Further, we statistically generalize the application sensitivity to faulty collective communications for these workloads, and present correlation between application features and the sensitivity.

Computer Science↗
DOE OSTI2019

Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa Workshop Report

In the last few decades, high-throughput technologies using various “omics” have enabled unprecedented views of biological systems at the molecular level. In parallel, the integration of omic datasets using computational modeling has provided new understanding of biological processes in organisms relevant to the U.S. Department of Energy’s (DOE) missions in energy and the environment. Collectively, these developments have spearheaded the advancement of systems biology, which can be viewed as a holistic approach for deciphering the complexity of biological systems. However, as high-throughput omic technologies and integrative systems biology efforts have improved our understanding of some biological systems, analyzing and finding meaningful answers within these massive datasets remain extremely challenging, in large part due to the lack of fundamental knowledge of gene function. Indeed, all sequenced genomes, both microbes and plants, contain large numbers of genes of “unknown function” that significantly limit scientists’ ability to model, predict, and engineer organisms with enhanced functions relevant to DOE. Current methodologies can be employed to decipher gene function, but they are typically slow, laborious, inefficient, and not scalable. This “bottleneck” in genome understanding could be broken with new, innovative, and transformative experimental tools, datasets, and computation that can define gene function on a massive and high-throughput scale compatible with the pace of DNA sequencing. In light of this grand challenge, DOE’s Office of Biological and Environmental Research (BER) convened the Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa workshop on November 1–2, 2018. This workshop brought together leaders in microbiology, plant sciences, technology, and computation, who collectively identified the experimental and data analysis gaps preventing large-scale gene function determination as well as opportunities for overcoming these gaps.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

A Partitioned - Task Parallel Implementation of the NASA Multiscale Analysis Tool for High Performance Computing

The NASA Multiscale Analysis Tool (NASMAT) is a platform for multiscale modeling of composites which can perform analysis of materials with any arbitrary number of length scales. The platform supports modularity, scalability, and interoperability using recursive procedures and data structures. A Macro solver driven parallelization scheme often limits the capability of NASMAT to scale as it has access to limited memory and number of cores (often one core/thread) and often forces to implement macro solver specific changes to the platform. In this work, a partitioned task-parallel approach is adopted, where the parallelization strategy adopted for NASMAT is independent of the macro solver and the computational resources are managed independently. The programming architecture takes into account the hierarchy of multiple scales (task-dependence) and the heterogeneous nature (dynamic load balancing) of computation through implementation of a hierarchy-informed task parallel model. The partitioned nature of the framework further extends the “plug and play” capability of NASMAT. preCICE, an open-source library for coupling multiphysics solver in a partitioned manner, is adopted to integrate NASMAT with an external macro solver by implementing a NASMAT adapter for preCICE. Speedup and scalability of the framework is studied for micromechanical models of varying size.

task-parallel↗
DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Energy DataBus (Fact Sheet)

NREL has developed the Energy DataBus, an open-sourced software that collects massive amounts of energy-related data at second-to-second intervals; stores it in a massive, scalable database; and turns it into useful information.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗