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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Upstream Ultra-Low Frequency Waves Observed by MESSENGER's Magnetometer: Implications for Particle Acceleration at Mercury's Bow Shock

We perform the first statistical analysis of the main properties of waves observed in the 0.05–0.41 Hz frequency range in the Hermean foreshock by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Magnetometer. Although we find similar polarization properties to the “30 second” waves observed at the Earth's foreshock, the normalized wave amplitude (δ B /|B0| ∼0.2) and occurrence rate (∼0.5%) are much smaller. This could be associated with relatively lower backstreaming proton fluxes, the smaller foreshock size and/or less stable solar wind (SW) conditions around Mercury. Furthermore, we estimate that the speed of resonant backstreaming protons in the SW reference frame (likely source for these waves) ranges between 0.95 and 2.6 times the SW speed. The closeness between this range and what is observed at other planetary foreshocks suggests that similar acceleration processes are responsible for this energetic population and might be present in the shocks of exoplanets.

N. Romanelli↗
NASA NTRS1993

Numerical simulations of the superdetonative ram accelerator combusting flow field

The effects of projectile canting and fins on the ram accelerator combusting flowfield and the possible cause of the ram accelerator unstart are investigated by performing axisymmetric, two-dimensional, and three-dimensional calculations. Calculations are performed using the INCA code for solving Navier-Stokes equations and a guasi-global combustion model of Westbrook and Dryer (1981, 1984), which includes N2 and nine reacting species (CH4, CO, CO2, H2, H, O2, O, OH, and H2O), which are allowed to undergo a 12-step reaction. It is found that, without canting, interactions between the fins, boundary layers, and combustion fronts are insufficient to unstart the projectile at superdetonative velocities. With canting, the projectile will unstart at flow conditions where it appears to accelerate without canting. Unstart occurs at some critical canting angle. It is also found that three-dimensionality plays an important role in the overall combustion process.

Soetrisno, Moeljo↗
DOE OSTI2020

Impact of Laboratory-Accelerated Aging Methods to Study Alkali–Silica Reaction and Reinforcement Corrosion on the Properties of Concrete

This study focuses on two separate investigations of the main aging mechanisms: alkali–silica reactivity (ASR) and the corrosion of reinforcing steel (rebar) concrete, both of which may result in a premature failure to meet the serviceability or strength requirements of a concrete structure. However, these processes occur very slowly, spanning decades. The impact of direct chemical additives to fresh concrete to accelerate ASR and the corrosion of reinforcing steel on the fresh and hardened properties of the ensuing material are investigated to inform the potential use of chemicals in large-scale studies. The deterioration of reinforced concrete (RC) is determined by means of expansion, cracking, bulk diffusivity and surface resistivity measurements, and compressive, split tensile and flexural strength tests. The results indicate that the addition of sodium hydroxide and calcium chloride can effectively accelerate the crack formation and propagation in concrete due to ASR and the corrosion of rebar, respectively. The ASR-induced cracks maintained a constant crack width from 0.05 mm to 0.1 mm over the measurement period regardless of the intensity of aging acceleration. Adding 4% chloride by weight of cement for accelerating rebar corrosion resulted in an average crack that was 82% larger than in the case of ASR accelerated with the addition of sodium hydroxide. The addition of alkali resulted in an increase in early-age (7-day) strength. At a total alkali loading of 2.98 kg/m3, 3.84 kg/m3 and 5.57 kg/m3, the 28-day compressive strength of concrete decreased by 3%, 10% and 24%, respectively. Similarly, a higher early-age strength and a lower later-age strength was observed for the concrete in the presence of corrosive calcium chloride. The results from this research are expected to inform future studies on the long-term performance of RC structures under accelerated ASR and corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
NASA NTRS2011

Timescale Correlation between Marine Atmospheric Exposure and Accelerated Corrosion Testing

Evaluation of metal-based structures has long relied on atmospheric exposure test sites to determine corrosion resistance in marine environments. Traditional accelerated corrosion testing relies on mimicking the exposure conditions, often incorporating salt spray and ultraviolet (UV) radiation, and exposing the metal to continuous or cyclic conditions of the corrosive environment. Their success for correlation to atmospheric exposure is often a concern when determining the timescale to which the accelerated tests can be related. Accelerated laboratory testing, which often focuses on the electrochemical reactions that occur during corrosion conditions, has yet to be universally accepted as a useful tool in predicting the long term service life of a metal despite its ability to rapidly induce corrosion. Although visual and mass loss methods of evaluating corrosion are the standard and their use is imperative, a method that correlates timescales from atmospheric exposure to accelerated testing would be very valuable. This work uses surface chemistry to interpret the chemical changes occurring on low carbon steel during atmospheric and accelerated corrosion conditions with the objective of finding a correlation between its accelerated and long-term corrosion performance. The current results of correlating data from marine atmospheric exposure conditions at the Kennedy Space Center beachside corrosion test site, alternating seawater spray, and immersion in typical electrochemical laboratory conditions, will be presented. Key words: atmospheric exposure, accelerated corrosion testing, alternating seawater spray, marine, correlation, seawater, carbon steel, long-term corrosion performance prediction, X-ray photoelectron spectroscopy.

Montgomery, Eliza L.↗
NASA NTRS2011

NEXT Propellant Management System Integration With Multiple Ion Thrusters

As a critical part of the NEXT test validation process, a multiple-string integration test was performed on the NEXT propellant management system and ion thrusters. The objectives of this test were to verify that the PMS is capable of providing stable flow control to multiple thrusters operating over the NEXT system throttling range and to demonstrate to potential users that the NEXT PMS is ready for transition to flight. A test plan was developed for the sub-system integration test for verification of PMS and thruster system performance and functionality requirements. Propellant management system calibrations were checked during the single and multi-thruster testing. The low pressure assembly total flow rates to the thruster(s) were within 1.4 percent of the calibrated support equipment flow rates. The inlet pressures to the main, cathode, and neutralizer ports of Thruster PM1R were measured as the PMS operated in 1-thruster, 2-thruster, and 3-thruster configurations. It was found that the inlet pressures to Thruster PM1R for 2-thruster and 3-thruster operation as well as single thruster operation with the PMS compare very favorably indicating that flow rates to Thruster PM1R were similar in all cases. Characterizations of discharge losses, accelerator grid current, and neutralizer performance were performed as more operating thrusters were added to the PMS. There were no variations in these parameters as thrusters were throttled and single and multiple thruster operations were conducted. The propellant management system power consumption was at a fixed voltage to the DCIU and a fixed thermal throttle temperature of 75 C. The total power consumed by the PMS was 10.0, 17.9, and 25.2 W, respectively, for single, 2-thruster, and 3-thruster operation with the PMS. These sub-system integration tests of the PMS, the DCIU Simulator, and multiple thrusters addressed, in part, the NEXT PMS and propulsion system performance and functionality requirements.

Sovey, James S.↗
DOE OSTI2025

SYCL for Performance Portability: Application Experience with Coupled Cluster Formalism in Quantum Chemistry on Exascale Systems

The exascale computing has brought unprecedented heterogeneity in node architectures, with systems such as Frontier and Aurora featuring diverse GPU accelerators, network connectivity among others. Ensuring performance portability across these platforms is a key challenge. To address this, we employ the SYCL programming model to develop portable, high-performance quantum chemistry workloads. As a representative application, we focus on the non-iterative Triples component of the coupled-cluster CCSD(T) method, a key driver in quantum chemistry. In this work, we report on our experience deploying SYCL-based implementations using both DPC++ and AdaptiveCPP across two flagship exascale platforms: OLCF Frontier with AMD MI250X GPUs and ALCF Aurora with Intel GPUs. Our results demonstrate that SYCL enables efficient, single-source implementations that scale to thousands of nodes, delivering performance on par with vendor-optimized HIP solutions. We highlight key insights into runtime behavior, kernel portability, and scaling characteristics, showing that SYCL offers a viable path for performance-portable computing.

Bagusetty, Abhishek [Argonne National Laboratory (↗
NASA NTRS2014

Modeling Materials: Design for Planetary Entry, Electric Aircraft, and Beyond

NASA missions push the limits of what is possible. The development of high-performance materials must keep pace with the agency's demanding, cutting-edge applications. Researchers at NASA's Ames Research Center are performing multiscale computational modeling to accelerate development times and further the design of next-generation aerospace materials. Multiscale modeling combines several computationally intensive techniques ranging from the atomic level to the macroscale, passing output from one level as input to the next level. These methods are applicable to a wide variety of materials systems. For example: (a) Ultra-high-temperature ceramics for hypersonic aircraft-we utilized the full range of multiscale modeling to characterize thermal protection materials for faster, safer air- and spacecraft, (b) Planetary entry heat shields for space vehicles-we computed thermal and mechanical properties of ablative composites by combining several methods, from atomistic simulations to macroscale computations, (c) Advanced batteries for electric aircraft-we performed large-scale molecular dynamics simulations of advanced electrolytes for ultra-high-energy capacity batteries to enable long-distance electric aircraft service; and (d) Shape-memory alloys for high-efficiency aircraft-we used high-fidelity electronic structure calculations to determine phase diagrams in shape-memory transformations. Advances in high-performance computing have been critical to the development of multiscale materials modeling. We used nearly one million processor hours on NASA's Pleiades supercomputer to characterize electrolytes with a fidelity that would be otherwise impossible. For this and other projects, Pleiades enables us to push the physics and accuracy of our calculations to new levels.

Supercomputing↗
DOE OSTI2025

Record acceleration of the two-dimensional Ising model using a high-performance wafer-scale engine

The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on Cerebras Wafer-Scale Engine (WSE) – a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representation storing 16 spins on each int16 word, and efficiently distributed the spins over the processing units enabling seamless weak scaling and limiting communications to only immediate neighboring units. Our implementation can handle up to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flip attempts per second for Ising models with up to 200 million spins. This represents a gain of up to 148 times over previously reported single-devices with a highly optimized implementation on NVIDIA V100 and up to 88 times in productivity compared to NVIDIA H100. Our findings highlight the significant potential of the WSE in scientific computing, particularly in the field of materials modeling.

Ising model↗
DOE OSTI2024

A critical review of irradiation-induced changes in reactor pressure vessel steels

A large body of work has been conducted to investigate the embrittlement and degradation of reactor pressure vessel (RPV) steels. This includes experiments on alloys with different compositions, performed in research and test reactors and ion accelerators that span various temperatures, fluxes, and fluences. In this paper, we perform a critical review of the published experimental data and compile experimentally reported values for dislocation loop size/density, precipitate size/density and yield stress into an easily downloadable format that can be used by both experimentalists and modelers. This thorough experimental review is complemented by a brief review of simulation efforts at atomistic and mesoscopic length scales. Finally, this paper highlights key aspects of the behavior of RPV steels under irradiation, identifies gaps or discrepancies in current understanding, and identifies priority future research directions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Superbeams and neutrino factories — Two paths to intense accelerator-based neutrino beams

High-energy and high-beam power accelerators are extensively used for the neutrino physics research. At present, the leading operational facilities are the J-PARC facility in Japan, which recently approached 0.5 MW of the 30 GeV proton beam power, and the Fermilab Main Injector complex that delivers over 0.75 MW of 120 GeV protons on the neutrino target. Besides such type of neutrino superbeams, the concept of neutrino factories offers great promise for particle physics research. In this brief review we present the status and planned upgrades of the J-PARC and Fermilab accelerators and leading proposals for the next generation accelerator-based facilities of both types, their challenges and required and ongoing accelerator R&D programs aimed to address corresponding performance and cost risks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Nek5000/RS Performance on Advanced GPU Architectures

We demonstrate NekRS performance results on various advanced GPU architectures. NekRS is a GPU-accelerated version of Nek5000 that is targeting high performance on forthcoming exascale platforms. It is being developed in DOE’s Center of Efficient Exascale Discretizations (CEED), which is one of the co-design centers under the Exascale Computing Project (ECP). In this report, we consider Frontier, Crusher, Spock, Polaris, Perlmutter, ThetaGPU and Summit. Simulations are performed using ExaSMR’s 17x17 rod-bundle geometries with different problem sizes. The report focuses on strong scaling performance and analysis. Many of the results shown in this report are the outcome from participation in the ALCF GPU Hackathon on Polaris, which was held on 7/19/22, and 7/26–7/28/22. Members of the Nek5000/RS team included Misun Min, Yu-Hsiang Lan, Paul Fischer and Thilina Rathnayake. Mentors were Kris Rowe (ALCF) and Peng Wang (NVIDIA). Also presented in this report are results on Frontier, which were obtained in collaboration with John Holmen at OLCF.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Maximum von Mises Stress in the Loading Environment of Mass Acceleration Curve

Method for calculating stress due to acceleration loading: 1) Part has been designed by FEA and hand calculation in one critical loading direction judged by the analyst; 2) Maximum stress can be due to loading in another direction; 3) Analysis procedure to be presented determines: a) The maximum Mises stress at any point; and b) The direction of maximum loading associated with the "stress". Concept of Mass Acceleration Curves (MAC): 1) Developed by JPL to perform preliminary structural sizing (i.e. Mariners, Voyager, Galileo, Pathfinder, MER,...MSL); 2) Acceleration of physical masses are bounded by a curve; 3) G-levels of vibro-acoustic and transient environments; 4) Convergent process before the couple loads cycle; and 5) Semi-empirical method to effectively bound the loads, not a simulation of the actual response.

von Mises stress↗
NASA NTRS2011

Performance Effects of Adding a Parallel Capacitor to a Pulse Inductive Plasma Accelerator Powertrain

Pulsed inductive plasma accelerators are electrodeless space propulsion devices where a capacitor is charged to an initial voltage and then discharged through a coil as a high-current pulse that inductively couples energy into the propellant. The field produced by this pulse ionizes the propellant, producing a plasma near the face of the coil. Once a plasma is formed if can be accelerated and expelled at a high exhaust velocity by the Lorentz force arising from the interaction of an induced plasma current and the magnetic field. While there are many coil geometries that can be employed to inductively accelerate a plasma, in this paper the discussion is limit to planar geometries where the coil take the shape of a flat spiral. A recent review of the developmental history of planar-geometry pulsed inductive thrusters can be found in Ref. [1]. Two concepts that have employed this geometry are the Pulsed Inductive Thruster (PIT) and the Faraday Accelerator with Radio-frequency Assisted Discharge (FARAD).

Polzin, Kurt A.↗
DOE OSTI2021

Co-design of Advanced Architectures for Graph Analytics using Machine Learning

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.

Kurte, Kuldeep↗
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗