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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Acceleration of the particle-in-cell code Osiris with graphics processing units

Fully relativistic particle-in-cell (PIC) simulations are crucial for advancing our knowledge of plasma physics. Modern supercomputers based on graphics processing units (GPUs) offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes that can fully leverage their computational resources. In this work, this demand is addressed by adding GPU acceleration to the PIC code Osiris. An overview of the algorithm, which features a CUDA extension to the underlying Fortran architecture, is given. Detailed performance benchmarks for thermal plasmas are presented, which demonstrate excellent weak scaling on NERSC's Perlmutter supercomputer and high levels of absolute performance. The robustness of the code to model a variety of physical systems is demonstrated via simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing. Finally, measurements and analysis of energy consumption are provided that indicate that the GPU algorithm is up to ~14 times faster and ~7 times more energy efficient than the optimized CPU algorithm on a node-to-node basis. The described development addresses the PIC simulation community's computational demands both by contributing a robust and performant GPU-accelerated PIC code and by providing insight into efficient use of GPU hardware.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Impact of Coating Defects on Performance of Coated Zirconium Cladding

Research on accident tolerant fuels (ATF) has started after the Fukushima accident [1–3]. While efforts have been expended on both fuel and cladding ATF concepts, the bulk of work has been devoted to improved cladding. The overarching goal of these approaches is to extend the coping time available during a severe accident before any event would result in release of radioactivity to the public. The most basic ATF cladding concept is obtained by applying a thin coating of highly corrosion-resistant material on the surface of a licensed zirconium cladding alloy. This thin coating is intended to not interfere with the neutronic or mechanical performance of the base cladding under normal operating conditions. Different coating materials, thicknesses, coating processes, process parameters, and testing methods have an impact on the microstructure and mechanical properties and therefore on the results of the applied investigation methods. These challenges have motivated an initial focus on demonstrating that the presence of coatings do not perturb the critical performance benchmarks of uncoated material. Ongoing lead test assembly irradiations of coated zirconium concepts in commercial reactions is intended to establish baseline performance in this regard in the coming years.

36 MATERIALS SCIENCE↗
DOE OSTI2026

TChem-atm (v2.0.0): scalable performance-portable multiphase atmospheric chemistry

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high-performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. In a proof-of-concept integration with the particle-resolved model PartMC, TChem-atm reproduces the existing PartMC–CAMP implementation within solver tolerances and delivers substantial GPU speedups, especially for large particle populations. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. TChem-atm enables performance-portable execution across CPUs and GPUs, though optimal efficiency may require modest architecture-specific tuning (e.g., team and vector sizes), with up to a twofold improvement on the NVIDIA H100. It directly supports sectional and particle-resolved host models, while modal aerosol schemes require minor adaptation to provide particle-scale quantities such as representative diameters. By enabling chemically detailed, multiphase simulations with performance portability and host-model flexibility, TChem-atm facilitates the incorporation of advanced chemistry into atmospheric models.

Díaz-Ibarra, Oscar Homero [Sandia National Laborat↗
DOE OSTI2024

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO 2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AMVOS: Additive Manufacturing Video Object Segmentation Dataset

This dataset provides labeled video frames from four additive manufacturing (AM) processes for video object segmentation (VOS) tasks. It contains 90 video segments comprising 900 individually annotated frames across five AM datasets: laser hot-wire directed energy deposition (LHW-DED), tungsten inert gas wire arc additive manufacturing (TIG-WAAM), plasma arc welding (PAW), visible-light polymer extrusion (visPolymer), and near-infrared polymer extrusion (irPolymer). Each video segment consists of 10 contiguous frames with corresponding pixel-level object instance annotations. Depending on the process, two of four object classes are labeled per frame: Melt Pool, Feed Wire, Nozzle, or Material. Raw frames are provided as .jpg files and annotations as palettized .png files. The dataset follows the directory structure of established VOS benchmarks (DAVIS, YouTube-VOS, MOSE), enabling direct integration into VOS model training and evaluation pipelines for foundation model fine-tuning, domain adaptation, or zero-shot performance benchmarking. Data was collected at Oak Ridge National Laboratory's Manufacturing Demonstration Facility.

Wetzel, Jon [ORNL]↗
DOE OSTI2020

Updates to the n+ 63,65 Cu RRR+URR Evaluations [Slides]

This work updates neutron reactions on 63,65 Cu. It also discusses the motivations of the research which is to correct deficiencies in benchmark performance since critical assembly configurations use Cu as a reflector. This presentation discusses the incorporation of URR parameters fit to 2017 63 Cu(n, γ) measurement by Weigand et al., as well as preliminary work on angular distributions and states that validation against copper-sensitive ICSBEP benchmarks is ongoing. The presentation concludes by stating that R -matrix analysis coupled to consistent average parameters used in the URR have been performed for 63,65 Cu isotopes, that future work will focus on the angular distributions, and that an analysis of additional data measured on natural Cu sample is in progress.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Disentangling the gap between pure and mixed-gas performance of thin film composite membranes through improved cell design and testing methods

Testing thin film composite (TFC) membrane coupons at low stage-cuts (≤5%) in a sweep-gas permeation system is a common practice to obtain mixed-gas separation properties for benchmarking performance and making scale-up decisions. However, even under these idealized conditions, mixed-gas permeance and selectivity can be more than 30% lower than their pure-gas values, partially due to concentration polarization, an effect that typically intensifies with increased membrane permeance. This study investigates the effect of cell design on mixed-gas testing using PolyActive TM TFC membranes with pure-gas CO 2 permeance of 1700 – 3100 gas permeance unit (GPU), covering the permeance range of most state-of-the-art CO 2 /N 2 separation membranes. Here, we designed and 3D-printed a counter-current permeation cell with enhanced feed and sweep flow efficiency, resulting in a 33 – 41% increase in mixed-gas CO 2 permeance compared to traditional permeation cells. Furthermore, we compared sweep-gas and vacuum permeation methods using traditional permeation cells, revealing that the latter delivers 41% higher mixed-gas CO 2 permeance, because vacuuming effectively minimizes the downstream concentration polarization. These findings highlight the importance of cell design and permeation apparatus selection in lab-scale mixed-gas testing, with strong implications for module design and process optimization at the industrial scale.

mixed gas performance↗
DOE OSTI2021

A Data-Driven Framework for Automated Detection of Aircraft-Generated Signals in Seismic Array Data Using Machine Learning

Abstract Ground motions associated with aircraft overflights can cover a significant portion of the seismic data collected by shallowly emplaced seismometers, such as new nodal and Distributed Acoustic Sensing systems. This article describes the first published framework for automated detection of aircraft on single channel and multichannel seismic data. The seismic data are converted to spectrograms in a sliding time window and classified as aircraft or nonaircraft in each window using a deep convolutional neural network trained with analyst-labeled data. A majority voting scheme is used to convert the output from the sequence of sliding time windows onto a decision time sequence for each channel and to combine the binary classifications on the decision time sequences across multiple channels. Precision, recall, and F-score are used to quantify the detection performance of the algorithm on nodal data using fourfold time-series cross validation. By applying our framework to data from the Sage Brush Flats nodal array in Southern California, we provide a benchmark performance and demonstrate the advantage of using an array of sensors.

Geochemistry & Geophysics↗
DOE OSTI2021

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIR v ), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR v and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR v offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R 2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R 2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. In conclusion, future efforts to fully integrate the complementary strengths of NIR v and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

Sattar, Naw Safrin↗
DOE OSTI2022

ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale

We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms

Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.

Kan, Shuwen [Fordham University]↗
DOE OSTI2025

Transforming Windows from Energy Liabilities to Zero-Energy Assets: Next-Generation Solutions for Buildings

Windows have traditionally contributed to a building's HVAC load, but they can also become a source of net energy gain or even operate as zero-energy components. For heating applications, highly insulating windows can harness more solar heat than the energy lost through them, transforming windows from energy liabilities to assets. Dynamic glazings provide further benefits by regulating solar heat gain, reducing cooling loads in summer and heating demands in winter. This simulation study focuses on developing the next generation of zero-energy windows (ZEW) for residential new construction. Through annual energy simulations across climate zones 1-8, ZEW performance benchmarks were established based on current code-level buildings, and we've identified the regions where meeting ZEW standards are most achievable. This work evaluates both static and dynamic window technologies, assessing their effects on annual energy use and cost. Key findings demonstrate that ZEW performance is achievable across diverse climate zones, with specific regional requirements. Most climate zones from 3-8 can achieve ZEW with specific configurations, while some warm climates (1-2) appear challenging for ZEW implementation. Climate zones 4-6 consistently allow for zero energy window implementation, offering multiple pathways through either static or dynamic window technologies. Colder climate zones (7-8) ZEW products allow for higher SHGC values while requiring low U-values.

Yu, Lili↗
DOE OSTI2020

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Optical nanofiber testbeds for benchmarking membrane-waveguide photonic integrated circuit platforms toward on-chip quantum inertial sensing

Recent advances in cold atom interferometry with optical and magnetic atom guides have set the stage for quantum inertial sensors capable of operating in dynamic environments. In this work, we present three key innovations—evanescent-field (EF) atom guides, optical nanofiber testbeds, and membrane-waveguide photonic integrated circuit (PIC) platforms—to advance EF-guided atom interferometry. First, we demonstrate EF atom guides on optical nanofiber testbeds, which serve as performance benchmarks for our membrane-waveguide PIC platforms. Second, we achieve low-power (⁠ ~ 5 mW) guiding of freely moving, laser-cooled 133 Cs atoms in two-color, traveling-wave EF optical dipole traps at the novel, heat-efficient magic wavelengths of 793 and 937 nm (i.e., “793/937-nm EF atom guides”). Concurrently, we design and fabricate membrane-waveguide PIC platforms for these EF atom guides; in our prior work, we showed that these structures safely accommodate 4–6 times the required optical trap power under vacuum and enable dense cold atom generation via magneto-optical trapping in the vicinity of the optical wavguide for efficient loading. Third, we verify preserved atomic coherence via microwave fields and EF-coupled Doppler-free Raman beams; to our knowledge, this is the first report of coherence fringes driven by co-propagating EF-coupled Raman beams with only 150 nW of total optical power. By providing a direct comparison between optical nanofiber testbeds and membrane-waveguide PIC platforms, our results lay critical groundwork for the on-chip realization of EF-guided atom interferometry and the development of fully integrated, compact, lightweight, and low-power quantum accelerometers and gyroscopes.

Orozco, Adrian [Sandia National Laboratories (SNL-↗
DOE OSTI2020

A Framework for the Analysis of Compiler Optimizations

Compilers transform program source code to machine executable code. During this transformation, they perform a number of compiler optimizations to improve the performance of the generated executable code. Importantly, applying those optimizations depends on the source code structure, such as the parallel programming model used to parallelize an algorithm. Often, implementations of the same algorithm with different programming models have vastly different performance because the compiler optimized them differently. We create FAROS, a framework to structure and automate the analysis of compiler optimizations on programs. FAROS automates the building process, execution profiling, and analysis of compiler optimization of programs, through a configuration interface. It outputs compiler optimization reports to show which optimizations applied to which line of source code, leveraging compilation remarks output by the compiler. Also, FAROS supports benchmarking performance of different program versions by collecting execution time results. In this first release of FAROS, we provide a configuration file to analyze compiler optimization differences for sequential vs OpenMP compilation, including 38 programs consisting of HPC proxy/mini/large applications, and NAS and Rodina kernels for analysis.

Georgakoudis, Giorgis↗
DOE OSTI2022

Hardware Selection and Performance of Low-Cost Fluorometers

Access to and extensive use of fluorometric analyses is limited, despite its extensive utility in environmental transport and fate. Wide-spread application of fluorescent tracers has been limited by the prohibitive costs of research-grade equipment and logistical constraints of sampling, due to the need for high spatial resolutions and access to remote locations over long timescales. Recently, low-cost alternatives to research-grade equipment have been found to produce comparable data at a small fraction of the price for commercial equipment. Here, we prototyped and benchmarked performance of a variety of fluorometer components against commercial units, including performance as a function of tracer concentration, turbidity, and temperature, all of which are known to impact fluorometer performance. While component performance was found to be comparable to the commercial units tested, the best configuration tested obtained a functional resolution of 0.1 ppb, a working concentration range of 0.1 to >300 ppb, and a cost of USD 59.13.

42 ENGINEERING↗