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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Microstructural Engineering and Accelerated Test Method Development to Achieve Low Cost, High Performance Solutions for Hydrogen Storage and Delivery

This project made advancements in developing lower cost steel alloys with novel microstructural design for use in hydrogen refueling infrastructure such as storage, compressors, and dispensing components, and utilizing accelerated test methods to efficiently evaluate variations in alloy and microstructure design. The project specifically sought to design alloys with lower nickel contents to reduce alloy cost, which was accomplished through substituting manganese for nickel along with other alloy additions to control deformation characteristics known to be important for hydrogen embrittlement resistance. Through Mn substitutions for nickel, austenitic and duplex austenite-ferrite steels were successfully developed with lower cost than currently available commercial stainless steel products that are employed for hydrogen refueling infrastructure. The steels were processed to achieve comparable strength and toughness in hydrogen environments as the commercially available steels containing high Ni contents. To evaluate mechanical performance in hydrogen, a testing methodology was employed to compare ubiquitous laboratory testing using electrochemical hydrogen charging in a liquid electrolyte to less accessible high pressure gaseous testing. While the application of these steels is in high pressure gaseous environments, the electrochemical hydrogen charging tests produced comparable results. Additionally, the Los Alamos Neutron Scatting Center enabled characterization of deformation mechanisms of the steel alloys in the presence of hydrogen, which has been associated with steel alloy characteristics associated with hydrogen embrittlement. Finally, a fracture mechanics based test bed model was developed to predict the influence of hydrogen gas pressure and fatigue conditions on fatigue lifetimes of pressure vessel steels. Together, these developments can be employed to enable lower cost hydrogen fueling infrastructure and more reliable prediction of steel alloy components in hydrogen service conditions. In particular, the newly alloys have the potential to replace stainless steels, having demonstrated comparable performance at substantially reduced cost.

08 HYDROGEN↗
DOE OSTI2025

Engineering PHL7 for Improved Poly(Ethylene Terephthalate) Depolymerization via Rational Design and Directed Evolution

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has emerged as a promising approach for polyester recycling, and, to date, many natural and engineered PET hydrolase enzymes have been reported. For industrial use, PET hydrolases must achieve high depolymerization extent and exhibit excellent thermostability. Here, we engineered a natural PET hydrolase, Polyester Hydrolase Leipzig #7 (PHL7), through rational design and directed evolution using a high-throughput screening platform. Four new enzymes were engineered with enhanced properties compared with the parent enzyme, wild-type PHL7 (PHL7-WT), and other benchmark PET hydrolases, under the tested conditions. In bioreactors, the exemplary engineered enzyme, PHL7-Jemez, exhibited improved ability to depolymerize amorphous PET film compared with PHL7-WT at 2.9% and 20% substrate loadings, with 37% and 270% higher hydrolysis, respectively, after 48 h. This study develops several state-of-the-art PET hydrolases and demonstrates a directed evolution platform to engineer high-performance enzymes, which can accelerate enzyme discovery toward improved biocatalytic recycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗
DOE OSTI2025

Particle Beam Acceleration Using 3 Petawatt Laser Pulses

The Zettawatt-Equivalent Ultrashort pulse laser System (ZEUS) is presently operational at the Gerard Mourou Center for Ultrafast Optical Science (CUOS) at the University of Michigan. ZEUS is a significant upgrade of the previous high power laser systems at CUOS and consists of two beamlines thatoperate in perfect synchronization. The 500 TW beamline became operational in 2023, 2 PW operation started in 2025 and full 3 PW power levels will be available in 2026. It is presently the highest power laser system in the US. In this grant the high field science group at CUOS has leveraged this unique high power laser facility to investigate laser wake field acceleration (LWFA) in ultra-high power laser plasma interactions and have shown how this can scale for future electron–positron colliders at high energy. The dual beam experimental configuration enables flexibility for many frontier experiments in laser-driven acceleration research, in particular, enabling extended channelling/acceleration experiments, positron generation/acceleration experiments and proof-of-principle transverse pumping “dephasingless” electron acceleration experiment and theory. LWFA may be able to miniaturize particle accelerators for high energy physics and also enable new sources of ultrafast, extreme brightness and precise x-rays for a wide variety of applications. In laser wake field acceleration, an electron bunch “surfs” on the electron plasma wave (the “wake field”) generated by the ponderomotive force of an intense laser. The plasma wave has a strong longitudinal electric field that stays in phase with the relativistic driver. A relativistic charged particle may, therefore, remain in phase with the accelerating field over long distances and gain ultra-relativistic energies. The accelerating electric field strength that the plasma wave can support can be many orders of magnitude higher than that of conventional accelerators, which makes laser wakefield acceleration an exciting prospect as an advanced accelerator concept. In this research project we have investigated the scaling of this mechanism to laser powers of 2 PW and have measured the x-ray emission and radio frequency emission resulting from the acceleration process. We have also performed theoretical investigation of mechanisms to scale laser driven accelerators to much higher energy using dephasingless acceleration processes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Synthesis Methodology for Intelligent Memory Interfaces in Accelerator Systems

Domain-specific systems improve the performance of a specific set of applications compared to general-purpose processing systems by deploying custom hardware accelerators. These hardware accelerators are generated using high-level synthesis (HLS) tools. The HLS tools enable a comprehensive design space exploration to optimize the compute performance of the generated accelerators. However, they often ignore the challenges of implementing the accelerators in a system-on-chip, particularly how the accelerators access memory. Our work introduces a buffering system design that improves accelerators' memory accesses by intelligently employing burst transactions to prefetch useful data from external memory to on-chip local buffers. Our design is dynamic, parametric, and transparent to the accelerators generated by HLS tools. We derive the buffering system parameters using appropriate compiler-based analysis passes and memory channel latency constraints. The proposed buffering system design results in, on average, 8.8x performance improvements while lowering memory channel utilization on average by 53.2% for a set of PolyBench kernels.

Limaye, Ankur M. (ORCID:0000000194062584)↗
DOE OSTI2024

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

Abstract Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of multipoint query , i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow and inefficient when dealing with such objectives as they must acquire the full series of measurements, but return only the emittance, with each query. We propose a new information-theoretic algorithm, Multipoint-BAX , for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a virtual objective , i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20× faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

High average-flux laser-driven neutron source

Laser-driven neutron generation is an attractive alternative to more established methods for compact, short-pulse-duration neutron sources with applications in medical science, material science and imaging. Despite extensive investigation of various techniques, achieving performance comparable to nuclear reactors or conventional accelerators remains challenging. In this work, we generate a stable, high-repetition-rate laser-driven neutron source reaching a record average flux of 7.8 × 10 7 n/sr/s, improving on other existing laser-based sources by more than one order of magnitude. Our approach is based on a two-step process where electrons are accelerated to relativistic energies via laser wakefield acceleration (LWFA), and subsequently generate neutrons through Bremsstrahlung emission followed by photonuclear reactions in a tungsten converter. Experimental results, supported by Monte Carlo simulations, show a neutron flux of 3.0 × 10 7 n/cm 2 /s near the target, on par with some compact accelerator-based neutron sources. Additionally, a direct comparison with the target-normal sheath acceleration (TNSA) pitcher-catcher scheme, performed on the same laser system, reveals a significantly higher total neutron yield of 3.9 × 10 8 neutrons per shot, outperforming the TNSA scheme by several orders of magnitude. These findings represent a significant advancement towards the development of practical laser-driven neutron sources and highlight the advantages of LWFA-based neutron generation for future applications.

Vallières, Simon [Institut National de la Recherch↗
DOE OSTI2026

Energy–Performance Trade-offs in Privacy-Preserving Federated Learning on SmartNIC-Enabled HPC Systems

Federated learning (FL) is increasingly deployed on accelerator-rich high-performance computing (HPC) systems, yet the system-level energy cost of privacy-aware FL remains poorly understood, particularly across heterogeneous networking and server-placement options. We present a measurement-driven study of energy–performance trade-offs for FL on GH200-class nodes across three deployment configurations: CPU-Ethernet, CPU-InfiniBand (RDMA-capable), and a DPU-hosted FL server over InfiniBand using a BlueField-3 SmartNIC/DPU. Using NVIDIA FLARE (NVFLARE), we align node-level power telemetry with per-round timing extracted from NVFLARE logs to quantify time-to-solution (TTS), energy-to-solution (ETS), energy-delay product (EDP), and synchronization behavior for three transformer models (ALBERT, DistilBERT, BERT), trained with and without differential privacy (DP). We find that interconnect choice is the dominant driver of runtime and energy: host-managed InfiniBand consistently reduces communication overhead versus Ethernet, yielding lower TTS/ETS/EDP. In contrast, in our NVFLARE deployment, placing the FL server on the DPU does not consistently match CPU-InfiniBand performance and can be slower—especially for larger models—highlighting that server placement alone is not sufficient to guarantee end-to-end gains. Finally, under our fixed-round protocol, DP increases per-round cost and runtime variance; ETS increases largely in proportion to TTS because average node power remains relatively stable across configurations.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SST-GPU: A Scalable SST GPU Component for Performance Modeling and Profiling

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of unprecedented amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi-beam operation of LANSCE accelerator facility

The Los Alamos Neutron Science Center (LANSCE) accelerator facility has been in operation for 50 years performing important scientific support for national security. The unique feature of the LANSCE accelerator facility is multi-beam operation, delivering beams to five experimental areas. The near-term plans are to replace obsolete and almost end-of-life systems of the LANSCE linear accelerator with a modern 100-MeV Front End with significant improvement in beam quality. This paper summarizes experimental results obtained during the operation of the LANSCE accelerator facility and considers plans to expand the performance of the accelerator for near- and long-term operations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Uncertainty quantification for deep learning in particle accelerator applications

With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Accelerating Floating-Point Computations with Intel AMX

Intel AMX is a built-in component of recent Intel CPU architectures, first supported by the Intel Sapphire Rapids in 2023, that enables efficient dense matrix multiplications using mixed precision with low-precision data types. The popularity of mixed-precision algorithms has grown recently, primarily due to their use on GPUs to enhance the efficiency of HPC applications, particularly for the training of large language models. The availability of mixed precision on CPUs represents a cost-effective solution for applications where high speed is not critical. This report shows how to use the Intel AMX accelerator through examples in C++ and Python. The examples will focus on mixed-precision floating-point operations obtained by the use of bfloat16 (or BF16) to accelerate code in single precision. We employ a bottom-up methodology, starting from specific register instructions (TMUL operation) to higher-level applications in libraries such as Intel MKL, PyTorch, and TensorFlow, ensuring a comprehensive understanding of the accelerator's potential. Additionally, we provide insights into the expected performance gains when leveraging the accelerator on the Kestrel HPC machine at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Surface properties and RF performance of Vapor Diffused Nb3Sn on Nb after sequential anneals below 1000 °C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000 °C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900 °C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Surface Properties and RF Performance of Vapor Diffused Nb3Sn on NB After Sequential Anneals Below 1000°C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000°C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900°C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗