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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pulsar’s Application in Energy Systems: Review of Current Status, Challenges, and Opportunities

To accelerate progress toward the realization of advanced energy systems, this review explores the potential of pulsar technology to create a more stable, economical, and environmentally friendly energy infrastructure. Pulsars, with their precise and reliable timing characteristics, have emerged as a promising tool for enhancing energy systems. This review begins by examining the development history of pulsar technology, shedding light on its evolution and the milestones achieved. It then provides a comprehensive summary of the current state of research, highlighting recent advancements and breakthroughs in this field. It also explores transformative pulsar applications in energy systems, including improved grid stability, advanced energy synchronization, and efficient energy storage management. However, implementing pulsar-related technologies presents significant technical, economic, and operational challenges. This review examines these hurdles and proposes strategies to overcome them, emphasizing the need for innovation, interdisciplinary collaboration, and supportive policies to fully integrate pulsar technologies into sustainable energy systems.

application↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2020

FY20 ASC IC L2 Milestone 7180: Performance Portability of SIERRA Mechanics Applications to ATS-1 and ATS-2. Executive Summary

The overall goal of this work was to accelerate simulations supporting the nuclear deterrence (ND) mission through improved performance of key algorithms in the ASC IC Sierra multi-physics application suite. This work focused on porting and optimizing algorithms for the graphics processing units (GPU) on the second ASC advanced technology system (ATS-2), while maintaining or improving performance on commodity technology systems (CTS) and ATS-1. Furthermore, these algorithmic developments used the ASC developed Kokkos performance portability abstraction library to maintain high performance across platforms using identical code, and enable sustainable reduced-cost migration and performance optimization to emerging hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ultra-Fast High-Precision Metallic Nanoparticle Synthesis using Laser-Accelerated Protons

Laser-driven proton acceleration, as produced during the interaction of a high-intensity (I > 1 × 10 18 W/cm 2 ), short pulse (<1 ps) laser with a solid target, is a prosperous field of endeavor for manifold applications in different domains, including astrophysics, biomedicine and materials science. These emerging applications benefit from the unique features of the laser-accelerated particles such as short duration, intense flux and energy versatility, which allow obtaining unprecedented temperature and pressure conditions. In this paper, we show that laser-driven protons are perfectly suited for producing, in a single sub-ns laser pulse, metallic nanocrystals with tunable diameter ranging from tens to hundreds of nm and very high precision. Our method relies on the intense and very quick proton energy deposition, which induces in a bulk material an explosive boiling and produces nanocrystals that aggregate in a plasma plume composed by atoms detached from the proton-irradiated surface. The properties of the obtained particles depend on the deposited proton energy and on the duration of the thermodynamical process. Suitably controlling the irradiated dose allows fabricating nanocrystals of a specific size with low polydispersity that can easily be isolated in order to obtain a monodisperse nanocrystal solution. Molecular Dynamics simulations confirm our experimental results.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Improved Single-Ion Conductivity of Polymer Electrolyte via Accelerated Segmental Dynamics

Single-ion conducting polymer electrolytes (SICPEs) have many advantages for solid-state battery applications, while low conductivities remain the bottleneck for their practical applications. In this paper, a strategy that can significantly improve the ionic conductivity of SICPEs based on the concept of accelerated segmental dynamics was demonstrated by the evaluation of the thermal property, rheological behavior, morphology analysis, molecular dynamics simulation, and conductivity performance. With both “soft” poly(dimethylsiloxane) (PDMS) backbone and poly(ethylene glycol) (PEG) side chains, the obtained SICPE possesses faster segmental relaxation and higher lithium-ion conductivity. With lithium “transference” number close to unity, the obtained SICPE also shows excellent electrochemical stability against lithium metal electrodes. The clear relationship established between the segmental dynamics of polymer electrolyte and its ionic conductivity should contribute to achieve a solid electrolyte with improved ionic conductivity toward the next-generation solid-state battery.

Segmental Dynamics↗
DOE OSTI2024

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗
DOE OSTI2025

SDA: a symbolic differential algebra package in C++

Truncated Power Series Algebra (TPSA), or Differential Algebra (DA), is a well-established tool in accelerator physics, commonly used for generating high-order maps of dynamic systems, as well as in symplectic tracking, normal form analysis, verified integration, optimization, and fast multipole methods. This package is the first to perform symbolic DA computations, enabling traceability of initial condition contributions and runtime reduction for repeated DA calculations, potentially expanding DA’s applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

External Fast Electrically-Controlled Ferroelectric Tuners for SRF Cavities

Euclid Techlabs LLC, in collaboration with JLAB (R. Rimmer, C. Hovater) has been awarded the Phase I 2021 DOE SBIR DE-SC0021510 project to develop the first Fast Ferroelectric reactive tuner (FRT) for a SRF accelerator that is currently in operation – CEBAF at JLAB, and is studying the possibility of developing a tuner designs for various new applications: fast frequency switching, transient detuning, and microphonics compensation for the SRF accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Development of Accelerated Kinetic Monte Carlo Code for Simulation of Helium Bubble Evolution

A mesoscale model to predict helium bubble evolution is needed for tritium applications. Such a model requires that the conventional kinetic Monte Carlo (kMC) simulations be significantly accelerated. The objective of this report is to (a) highlight the concepts and mathematical expressions of the accelerated method for defect implementation that have not been published, (b) show an example input file to run the kMC code, and (c) provide suggestions on future improvement following my retirement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Characterizing Density and Spatial Distribution of Trap States in Ta 3 N 5 Thin Films for Rational Defect Passivation

Tantalum nitride (Ta 3 N 5 ) has gained significant attention as a potential photoanode material, yet it has been challenged by material quality issues. Defect-induced trap states are detrimental to the performance of any semiconductor material. Beyond influencing the performance of Ta 3 N 5 films, defects can also accelerate the degradation in water during desired electrochemical applications. Defect passivation has provided an enormous boost to the development of many semiconductor materials but is currently in its infancy for Ta 3 N 5 . This is in part due to a lack of experimental understanding regarding the spatial and energetic distribution of trap states throughout Ta 3 N 5 thin films. Here, we employ drive-level capacitance profiling (DLCP) to experimentally resolve the spatial and energetic distribution of trap states throughout Ta 3 N 5 thin films. The density of deeper energetic traps is found to reach ∼2.5 to 6 × 10 22 cm –3 at the interfaces of neat Ta 3 N 5 thin films, over an order of magnitude greater than the bulk. In addition to the spatial profile of deep trap states, we report neat Ta 3 N 5 thin films to be highly n-type in nature, owning a free carrier density of ∼9.74 × 10 17 cm –3 . This information, coupled with the present understanding of native oxide layers on Ta 3 N 5 , has facilitated the rational design of a targeted passivation strategy that simultaneously provides a means for catalyst immobilization. Loading catalyst via silatrane moieties suppresses the density of defects at the surface of Ta 3 N 5 thin films by two orders of magnitude, while also reducing the free carrier density of films by over one order of magnitude, effectively dedoping the films to ∼2.40 × 10 16 cm –3 . The surface passivation of Ta 3 N 5 films translates to suppressed defect-induced trapping and recombination of photoexcited carriers, as determined through absorption, photoluminescence, and transient photovoltage. Here, this illustrates how developing a deeper understanding of the distribution and influence of defects in Ta 3 N 5 thin films has the potential to guide future works and ultimately accelerate the integration and development of high-performance Ta 3 N 5 thin film devices.

defect passivation↗
DOE OSTI2020

Blunt Impact Brain Injury using Cellular Injury Criterion

The Advanced Combat Helmet (\ACH") military specification (\mil-spec") requires a helmeted magnesium (\Mg") Department of Transportation (\DOT") headform be dropped vertically, with an impact speed of 3.1 m/s (10 ft/s), onto a steel hemispherical target. The pass/fail criteria are based on translational acceleration (150 G) alone, absent of any rotational component. Without a rotational component, the specification's injury risk application is limited to skull fracture and peripheral hematomas (subdural, subarachnoid), since this translational acceleration injury risk assessment is based on the Wayne State Tolerance Curve (\WSTC"). To provide a more comprehensive view of injury for the entire brain, an alternative approach is needed. To meet this need, we worked with a larger group called PANTHER, a collaboration between national laboratories, industry, and academia. Collaborations specific to research and results presented here come from efforts led by Mr. Ron Szalkowski and Mr. Sushant Malave, Ms. Alice Fawzi, and Dr. Christian Franck. We have developed a prototypical injury risk criterion based on the neuronal response to abrupt changes in general motion (translation, rotation, or both). The cellular-based mild traumatic brain injury (\cbmTBI") criterion utilizes both the strain and strain rate of brain tissue to account for the stretch and rate of stretch that occurs throughout the brain as a result of blunt impact to the head. We conducted physical experiments of an ACH-fitted magnesium headform, which produced repeatable headform peak accelerations. Then, we developed a simulation of the experiment, and validated the simulation output with the experimental data. We then substituted the magnesium headform with a human headform, consisting of skin, muscle, bone, gray matter, white matter, cerebral-spinal fluid, membranes, vasculature, intravertebral discs, airway and sinus. We quantified brain injury risk using the cbmTBI criterion, using the current mil-spec test and a modified test. The modified mil-spec test used an inclined anvil target that was located posterior to the crown of the helmet in the axial plane. While the current mil-spec test produced brain deformation from head translation alone, the modified test produced brain deformation from head translation and rotation, which is closer to most real world and combat theater impacts (e.g., such as occur in tertiary blast exposure). Compared to the current mil-spec test, the modified test produced elevated strains in the human digital twin. These data, mapped to the cbmTBI criterion, suggest increased injury risk for blunt impacts that cause rotation and translation, rather than just translation alone. Moreover, these data may lead to a rotational performance metric, which is rooted in the biology and pathology of the brain's response to impact and blast, and which should be used to improve next-generation helmet designs.

42 ENGINEERING↗
DOE OSTI2023

End-to-end AI framework for interpretable prediction of molecular and crystal properties

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

36 MATERIALS SCIENCE↗
DOE OSTI2024

MEMS RF accelerators for nuclear energy and advanced manufacturing

Energetic ions are widely used to develop radiation hard structural materials and fuels for advanced nuclear energy. Ion beams are also used to generate neutrons for nuclear materials testing, as well as in a series of high impact, high value adding manufacturing processes (ranging from doping of semiconductors to hardening of materials against wear and to increase bio-compatibility of materials). But to date, high power beams of high energy ions are simply too expensive because they are delivered from large single beam accelerators. We have recently demonstrated that MEMS technology enables massively parallel, low cost batch fabrication of ion beams. We propose to scale intense ion accelerators based on MEMS (micro-electro mechanical systems) to high beam power (>10 kW). With our approach, hundreds to thousands of ion beamlets will be densely packed on silicon wafers. Ions are injected and accelerated across gaps formed in stacks of wafers, leading to uniquely high current densities for intense ion accelerators with variable kinetic energy (0.1 to 10 MeV) and variable ion species. Our team brings together experts in high power ion accelerators from Lawrence Berkeley National Laboratory and leading experts in MEMS technology from Cornell University. We will deliver a disruptive technology of low cost, flexible and massively scalable ion accelerators that will enable the rapid development of radiation hard nuclear materials for advanced nuclear energy and enable new applications in manufacturing.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

OpenACC unified programming environment for GPU and FPGA multi-hybrid acceleration

Attached accelerators have been frequently used in recent High Per- formance Computing (HPC) systems because of their high performance/power ratio. In particular, the Graphics Processing Unit (GPU) is the most popu- lar accelerator owing to its high peak FLOPS performance and high memory bandwidth supported by HBM2, etc. However, the performance of GPU depends highly on a large degree of SIMD parallelism and has difficulty sustaining a high performance on programs with frequent branch operations or a partially low degree of parallelism.By contrast, a Field Programmable Gate Array (FPGA) has received attention as a different type of accelerator than GPU as a fully reconfigurable processor fitting the target applications. The high performance of FPGA is mainly provided by a pipelined operation and optimized circuit suitable for any operation even with frequent conditional branches. We have been focusing on the flexibility of FPGA to compensate for the weakness of GPU. We believe that the coupling of GPU with FPGA can result in one of the most powerful accelerating platforms available.However, the program coding of GPU and FPGA coupling can be quite difficult for application users. Traditionally, CUDA by NVIDIA has been the most popular programming language with the largest share of GPUs used in HPC, whereas a hardware description language such as Verilog HDL has been used in FPGA programming. OpenCL coding has recently become available even on high-end FPGAs. Moreover, several recent studies have also enabled the OpenACC coding for use in FPGA. In this study, we provide a unified programming system based on OpenACC for a platform equipped with both GPU and FPGA aiming at the next-generation accelerated supercomputer framework. Our programming environment is called Multi-Hybrid OpenACC Translator (MHOAT), and in this paper, we describe the basic concept and prototype system of MHOAT based on an evaluation on the amount of coding required and the performance of a hybrid multi-device accelerated system.

Tsunashima, Ryuta↗
DOE OSTI2023

Novel materials irradiation and pre-characterization plans for high-power targetry applications

The multi-megawatt proton beams that will be employed at future accelerator complexes introduce many new challenges for next-generation targetry systems, primarily due to the increased levels of beam-induced radiation damage combined with thermal shock effects during a beam pulse. Novel material classes, such as high-entropy alloys and electro-spun nanofibers are currently being investigated as materials that will be more tolerant of these effects in high beam power applications. Low-energy ion irradiation will be used for pre-screening and down selecting candidate novel materials prior to prototypic high-energy irradiation studies. Specific nano and micro-scale characterization techniques will be used to evaluate the radiation damage and thermal shock resistance of the new materials. Irradiation induced lattice spacing alteration and defect concentrations, as well as mechanical and thermal properties of the novel materials will be measured and compared to those of conventional targetry materials. This poster will detail plans for initial low-energy ion irradiation tests, and the post-irradiation examination techniques that will be used to develop and screen these novel target materials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Quench Detection and Protection for High-Temperature Superconductor Accelerator Magnets

High-temperature superconductors (HTS) are being increasingly used for magnet applications. One of the known challenges of practical conductors made with high-temperature superconductor materials is a slow normal zone propagation velocity resulting from a large superconducting temperature margin in combination with a higher heat capacity compared to conventional low-temperature superconductors (LTS). As a result, traditional voltage-based quench detection schemes may be ineffective for detecting normal zone formation in superconducting accelerator magnet windings. A developing hot spot may reach high temperatures and destroy the conductor before a practically measurable resistive voltage is detected. The present paper discusses various approaches to mitigating this problem, specifically focusing on recently developed non-voltage techniques for quench detection.

Marchevsky, Maxim (ORCID:0000000172839305)↗
DOE OSTI2023

A Materials Perspective on Direct Recycling of Lithium-Ion Batteries: Principles, Challenges and Opportunities

As the dominant means of energy storage technology today, the widespread deployment of lithium-ion batteries (LIBs) would inevitably generate countless spent batteries at their end of life. From the perspectives of environmental protection and resource sustainability, recycling is a necessary strategy to manage end-of-life LIBs. Compared with traditional hydrometallurgical and pyrometallurgical recycling methods, the emerging direct recycling technology, rejuvenating spent electrode materials via a non-destructive way, has attracted rising attention due to its energy efficient processes along with increased economic return and reduced CO 2 footprint. This review investigates the state-of-the-art direct recycling technologies based on effective relithiation through solid-state, aqueous, eutectic solution and ionic liquid mediums and thoroughly discusses the underlying regeneration mechanism of each method regarding different battery chemistries. It is concluded that direct regeneration can be a more energy-efficient, cost-effective, and sustainable way to recycle spent LIBs compared with traditional approaches. Additionally, it is also identified that the direct recycling technology is still in its infancy with several fundamental and technological hurdles such as efficient separation, binder removal and electrolyte recovery. Finally, in addressing these remaining challenges, this review proposes an outlook on potential technical avenues to accelerate the development of direct recycling toward industrial applications.

25 ENERGY STORAGE↗