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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effect of coherent edge-localized mode on transition to high-performance hybrid scenarios in KSTAR

This paper deals with one of the origins and trigger mechanisms responsible for the observed performance enhancements in the hybrid scenario experiments conducted in Korea Superconducting Tokamak Advanced Research (KSTAR). The major contribution to the performance improvement comes from a broader and higher pedestal formation. The increase of fast ion pressure due to a plasma density decrease also contributes substantially to the global beta. Although the reduced core plasma volume resulting from the pedestal expansion has a negative effect on the core thermal energy, a considerable confinement improvement observed in the inner core region limits the degradation. The one significant characteristic of high-performance discharges is the presence of Coherent Edge-localized Mode (CEM) activity. CEM is triggered during the pedestal recovery phase between typical ELM crashes and has been found to be related to the increase of particle and heat transport. It appears to underlie two commonly observed phenomena in high-performance hybrid scenario discharges in KSTAR; pedestal broadening and continuous density decrease. Despite the associated transport increase, CEM activities can induce performance enhancement. With the pedestal broadening, ELM crashes become delayed and weakened, which, in turn, allows for a higher pedestal. Moreover, the density decrease directly increases fast ion pressure by extending the beam-slowing-down time. The linear gyrokinetic analysis reveals that the increase of fast ions could initiate positive feedback loops, leading to the stabilization of Ion Temperature Gradient mode in the inner core region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Long-pulse high-performance H-mode plasmas achieved on EAST

A record duration of a 310 s H-mode plasma (H 98y2 ~ 1.3, n e /n GW ~ 0.7, f BS > 50%) has been recently achieved on experimental advanced superconducting tokamak (EAST) with metal walls, exploiting the device's improved long-pulse capabilities. The experiment demonstrates good control of tungsten concentration, core/edge MHD stability, and particle and heat exhaust with an ITER-like tungsten divertor and zero injected torque, establishing a milestone on the path to steady-state long-pulse high-performance scenarios in support of ITER and CFETR. Important synergistic effects are leveraged toward this result, which relies purely on radio frequency (RF) powers for heating and current drive (H&CD). On-axis electron cyclotron heating enhances the H&CD efficiency from lower hybrid wave injection, increasing confinement quality and enabling fully non-inductive operation at high density (n e /n GW ~ 70%) and high poloidal beta (β P ~ 2.5). A small-amplitude grassy edge localized mode regime facilitates the RF power coupling to the H-mode edge and reduces divertor sputtering/erosion. The high energy confinement quality (H 98y2 ~ 1.3) is achieved with the experimental and simulated results pointing to the strong effect of Shafranov shift on turbulence. Transport analysis suggests that trapped electron modes dominate in the core region during the record discharge. The detailed physics processes (RF synergy, core-edge integration, confinement properties, etc.) of the steady-state operation will be illustrated in the content. In the future, EAST will aim at accessing more relevant dimensionless parameters to develop long-pulse high-performance plasma toward ITER and CFETR steady-state advanced operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Chain entanglements enable regeneration of high-performance thermosets

Thermoset plastics underpin structural materials, electronics and transportation, yet the permanent covalent networks that prevent flow and provide dimensional stability also make them difficult to recycle without sacrificing performance. In this work we show that high-performance thermosets can be built around dense chain entanglements, the physical interlacing of long polymer strands, rather than dense permanent crosslinks, with only a small number of selectively cleavable junctions preserving connectivity. Long, rigid, entangled polyolefin backbones generated by frontal polymerization form glassy polymers with high stiffness, high toughness and excellent creep suppression yet can be fully deconstructed into soluble, linear oligomers. Varying oligomer length and end-group chemistry enables their reuse as re-entangling building blocks that regenerate thermosets with thermal and mechanical properties that remain unchanged across generations. The strategy further extends to high-temperature fibre-reinforced composite matrices and additively manufactured structures, establishing chain entanglement as a design principle for durable, regenerable thermosets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hydrogenated TiO 2 Carbon Support for PtRu Anode Catalyst in High‐Performance Anion‐Exchange Membrane Fuel Cells

Abstract The availability of durable, high‐performance electrocatalysts for the hydrogen oxidation reaction (HOR) is currently a constraint for anion‐exchange membrane fuel cells (AEMFCs). Herein, a rapid microwave‐assisted synthesis method is used to develop a core–shell catalyst support based on a hydrogenated TiO 2 /carbon for PtRu nanoparticles (NPs). The hydrogenated TiO 2 provides a strong metal‐support interaction with the PtRu NPs, which improves the catalyst's oxophilicity and HOR activity compared to commercial PtRu/C and enables greater size control of the catalyst NPs. The as‐synthesized PtRu/TiO 2 /C‐400 electrocatalyst exhibits respectable performance in an AEMFC operated at 80 °C, yielding the highest current density (up to 3× higher) within the catalytic region (compared at 0.80–0.90 V) and voltage efficiency (68%@ 0.5 A cm −2 ) values in the compared literature. In addition, the cell demonstrates promising short‐term voltage stability with a minor voltage decay of 1.5 mV h −1 . This “first‐of‐its‐kind in alkaline” work may open further research avenues to develop rapid synthesis methods to prepare advanced core–shell metal‐oxide/carbon supports for electrocatalysts for use in the next‐generation of AEMFCs with potential applicability to the broader electrochemical systems research community.

08 HYDROGEN↗
DOE OSTI2022

The COVID-19 High-Performance Computing Consortium

In March of 2020, recognizing the potential of High Performance Computing (HPC) to accelerate understanding and the pace of scientific discovery in the fight to stop COVID-19, the HPC community assembled the largest collection of worldwide HPC resources to enable COVID-19 researchers worldwide to advance their critical efforts. Amazingly, the COVID-19 HPC Consortium was formed within one week through the joint effort of the Office of Science and Technology Policy (OSTP), the U.S. Department of Energy (DOE), the National Science Foundation (NSF), and IBM to create a unique public–private partnership between government, industry, and academic leaders. This article is the Consortium's story–how the Consortium was created, its founding members, what it provides, how it works, and its accomplishments. We will reflect on the lessons learned from the creation and operation of the Consortium and describe how the features of the Consortium could be sustained as a National Strategic Computing Reserve to ensure the nation is prepared for future crises.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2020

High Performance Computing Application I/O Traces

The dataset comprises trace files from high performance computing (HPC) simulations. The trace files contain records of every I/O operation executed by a simulation application run, including I/O operations from HDF5, MPI-IO, and POSIX and all of the parameters supplied to those operations, e.g. file name, offset, and flags. The traces are generated by executing a simulation application that is linked with the Recorder tracing tool (https://github.com/uiuc-hpc/Recorder). The Recorder trace tool intercepts the I/O calls made by the application, records the I/O trace record, and then calls the intended I/O call so that the operation executes.

Wang, C.↗
DOE OSTI2024

High Performance Computing Management: A Sustainable System Software Approach

The demand for high performance computing (HPC) resources continues to grow, driven by the increasing complexity of modeling and simulation, artificial intelligence (AI), and machine learning (ML) workloads [Porter]. The growing energy consumption demand of these HPC systems is a significant concern, both in terms of operational costs and environmental impact. AI hardware accelerators are expected to reach 1.5% of the world’s power consumption by 2029 [Shah].

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗
DOE OSTI2025

Simulation of Physics-Based 0-10Hz Strong Motion Using High Performance Computing Supporting Refinements to Regional Ground Motion Models for the Central Eastern US

In collaboration with the U.S. Nuclear Regulatory Commission (NRC) the LLNL has developed a computationally efficient simulation platform designed to perform physics-based ground motion simulations for crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using high-performance computing. The main objective of the earthquake simulations was to use synthetic ground motion to provide constrains to refinements of existing ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-10Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model (1994). In simulations performed during the first stage of this project the Saikia’s velocity model demonstrated better performance in modelling high frequency regional wave propagation for the CEUS region recorded during the Mw5.0 November 7, 2016, Cushing Oklahoma (Taylor et al., 2017), and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes. The proposed regional 3D model includes random perturbations to the 1D background model using the stochastic scheme of Pitarka and Mellors (2021). In addition, validation analysis of the rupture generator and regional wave propagation models, using comparisons with different GMMs for Mw6.5 and Mw7.0 scenario earthquakes in the CEUS region resulted in a very good match between the simulated and empirical ground motion models. For the purposes of seismic hazard assessment at the existing and planned nuclear power plants, NRC is interested in studies aimed at improving the current ground motion models (GMM) for both Stable Continental Regions (SCR) in the Central and Eastern US and Active Crustal Regions (ACR) in the Western US. Due to lack of recorded data, these improvements require synthetic data for short fault distances and large magnitude earthquakes for which the existing recorded data is not enough to uniquely constrain the GMMs. The need for simulations and strong motion data is especially critical for the CEUS region where we do not have recorded data from potentially large damaging earthquakes with moment magnitudes 6.0 and higher. In this the project, we focused on 10Hz simulations of Mw7.0 scenario earthquakes with strike slip and thrust faulting mechanisms. We used more than 50 Mw7.0 earthquake rupture scenarios to investigate the ground motion uncertainty due to unknown earthquake rupture parameters, in particular, the slip distribution, rupture velocity, and faulting mechanism, and their implication on ground motion amplification due to forward rupture directivity effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Monofluorinated acetal electrolyte for high-performance lithium metal batteries

High degree of fluorination for ether electrolytes has resulted in improved cycling stability of lithium metal batteries due to stable solid electrolyte interphase (SEI) formation and good oxidative stability. However, the sluggish ion transport and environmental concerns of high fluorination degree drive the need to develop less fluorinated structures. Here, we depart from the traditional ether backbone and introduce bis(2-fluoroethoxy)methane (F2DEM), featuring monofluorination of the acetal backbone. High coulombic efficiency and stable long-term cycling in Li||Cu half cells can be achieved with F2DEM even under fast Li metal plating conditions. The performance of F2DEM is further compared with diethoxymethane (DEM) and 2-[2-(2,2-difluoroethoxy)ethoxy]-1,1,1-trifluoroethane (F5DEE). A significantly lower overpotential is observed with F2DEM, which improves energy efficiency and enables its application in high-rate conditions. Comparative studies of F2DEM with DEM and F5DEE in anode-free lithium iron phosphate (LiFePO4) LFP pouch cells and high-loading LFP coin cells further show improved capacity retention of F2DEM electrolyte, demonstrating its practical applicability. More importantly, we also extensively investigate the underlying mechanism for the superior performance of F2DEM through various techniques, including X-ray photoelectron spectroscopy, scanning electron microscopy, cryogenic electron microscopy, focused ion beam, electrochemical impedance spectroscopy, and titration gas chromatography. Overall, F2DEM facilitates improved Li deposition morphology with reduced amount of dead Li. This enables F2DEM to show superior performance, especially under higher charging and slower discharging rate conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

Microgrid Integration with High Performance Computing Systems for Microreactor Operation

Multiple nuclear microreactor concepts are currently being developed across several sizes and fuel types with high performance computing (HPC) systems anticipated to be end-users of the power. Nuclear microreactors are small in size, portable, produce less than 10 MW electric, operate autonomously, and have a refueling interval of as many as 10 years. However, their load-follow is also generally limited to 10%/minute or worse whereas the power variance in HPC systems easily exceeds this constraint under normal operations. This study explores an approach that requires no load-follow from the microreactor but integrates the HPC system with a microgrid built from commercial-off-the-shelf components. Three typical HPC architectures are explored in the context of microgrid operation in this study. Components of power quality and transient response are empirically measured for five different HPC load-follow response levels using a self-contained mobile datacenter connected to the microgrid capable of integration with a nuclear microreactor.

microgrids↗
DOE OSTI2023

Ground Motion Models (GMMs) Improvements Using Earthquake Simulations on High Performance Computers

A computationally efficient simulation platform was developed that can provide representative synthetic ground motions from crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using 3D modeling and high-performance computing. The main objective was to use synthetic ground motion to provide constrains to refinements of exiting ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-5Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. As part of a strategy for selecting a reginal velocity model and validation of developed rupture modeling technique, ground motions from the moment magnitude Mw5.0 November 7, 2016, Cushing Oklahoma, and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes were simulated. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model. Saikia’s model demonstrated better performance in modelling high frequency regional wave propagation for CEUS region. The proposed 3D model includes lateral variations added to the 1D background model using the stochastic scheme of Pitarka and Mellors. Comparisons of the simulations with recordings of both earthquakes demonstrated the reliability of our deterministic simulation approach while emphasizing the importance of including small-scale variability in the regional velocity model needed to reproduce the observed high-frequency wave scattering effects. As part of validation analysis, comparisons with different GMMs for a Mw6.5 earthquake in the CESUS region resulted in a very good match between the simulated and empirical ground motion models. Initial investigations of within-event and between-event ground motion variabilities for Mw6.5 scenario earthquakes on a strike-slip fault, suggest that they are strongly related to spatial slip and slip rate variations, average rupture velocity, rupture area and rupture initiation location. For certain scenarios we found that the ground motion variability observed at near-fault distances (< 5 km) also persists at longer distances. Regardless of the rupture scenario, the simulated ground motion tends to fully saturate at short distances and for all periods. The near-fault saturation has to do with the attenuation of waves propagating along the fault and local rupture radiation pattern that also contribute to stronger ground motion variation at such distances. Analysis of effects of rupture initiation location suggest that the peak ground motion (PGV) and spectral acceleration (SA) can be quite variable due to rupture directivity effects. Such effects are stronger at periods longer than 1s. The effect of the 1D velocity models and surface topography on simulated ground motion were investigated by comparing three component synthetic seismograms computed at selected sites. Effect of surface topography was considered using the ratio between spectral accelerations simulated for two 1D models with flat surface topography and realistic model with surface topography. Overall, the topography slightly amplifies (by ~30%) the ground motion amplitude in the frequency range 1-3Hz. The effect of topography is more visible in the surface and coda waves portion of the seismograms.

58 GEOSCIENCES↗
DOE OSTI2024

Zero-strain high entropy spinel oxide (FeNiCuCrMn) 3 O 4 @rGO as high-performance anode for sodium ion battery

High entropy spinel oxides (HEOs), as a new type of anode material with at least five transition metal elements and high theoretical specific capacity, have enormous promise in sodium ion batteries (SIBs). However, the influence of different transition metal elements on the structure and electrochemical performances of HEOs is not clear. Here, in this study, the addition of Mn to HEO can increase the surface oxygen vacancy (O v ) concentration, shorten the band gap, and enhance the electrochemical performance of (FeNiCuCrMn) 3 O 4 @rGO. XPS results show (FeNiCuCrMn)3O4@rGO has the highest surface O v concentration. Ab initio calculations demonstrate that the indirect band gap of (FeNiCuCrMn)3O4@rGO is 0.068 eV. In situ X-ray diffraction and synchrotron X-ray absorption spectroscopy reveal that (FeNiCuCrMn) 3 O 4 @rGO exhibits a zero-strain characteristic with an inconsequential unit cell volume change of 0.2 %. Specifically, (FeNiCuCrMn) 3 O 4 @rGO as anode materials for SIBs show good cyclic stability (with a capacity retention of 84 % at 500 mA g −1 after 3000 cycles). Overall, this work provides a new direction for optimizing transition metal elements in HEOs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

PtychoShelves , a versatile high-level framework for high-performance analysis of ptychographic data

Over the past decade, ptychography has been proven to be a robust tool for non-destructive high-resolution quantitative electron, X-ray and optical microscopy. It allows for quantitative reconstruction of the specimen's transmissivity, as well as recovery of the illuminating wavefront. Additionally, various algorithms have been developed to account for systematic errors and improved convergence. With fast ptychographic microscopes and more advanced algorithms, both the complexity of the reconstruction task and the data volume increase significantly. PtychoShelves is a software package which combines high-level modularity for easy and fast changes to the data-processing pipeline, and high-performance computing on CPUs and GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

N-doped honeycomb-like porous carbon towards high-performance supercapacitor

Biomass-derived porous carbon with developed pore structure is critical to achieving high performance electrode materials. In this work, we report a grape-based honeycomb-like porous carbon (GHPC) prepared by KOH activation and carbonization, followed by N-doping (NGHPC). The obtained NGHPC exhibits a unique honeycomb-like structure with hierarchically interconnected micro/mesopores, and high specific surface area of 1268 m2/g. As a supercapacitor electrode, the NGPHC electrode exhibits a remarkable specific capacitance of 275 F/g at 0.5 A/g in a three-electrode cell. Moreover, the NGHPC//NGHPC symmetric supercapacitor displays a high energy density of 12.6 Wh/kg, and excellent cycling stability of approximately 95.2% capacitance retention after 5000 cycles at 5 A/g. The excellent electrochemical performance of NGHPC is ascribed to its high specific surface area, honeycomb-like structure and high-content of pyrodinic-N (36.29%). It is believed that grape-based carbon materials show great potential as advanced electrode materials for supercapacitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems

Coupled-cluster theory (CC) is a highly accurate and versatile method for simulating complex interactions within quantum systems. The extension of CC theory to model mixed electron-photon processes with quantum electrodynamics (QED) has improved our capability to predict cavity-modified chemistry, a field where photons are used as cost-effective and eco-friendly alternatives to catalyze/inhibit chemical reactions. However, calculations with CC methods, even without incorporating QED effects, are often prohibitively expensive. Simulations of larger systems require scalable infrastructures that exist for traditional CC methods but not for QED-CC methods. As such, we present a GPU-enabled, high-performance, open-source implementation of the quantum electrodynamics coupled-cluster method with single and double excitations (QED-CCSD) within the ExaChem quantum chemistry software package. ExaChem relies on the Tensor Algebra for Many-body Methods (TAMM) infrastructure: a parallel heterogeneous tensor library designed to achieve scalable performance on modern heterogeneous supercomputing platforms. Furthermore, we discuss theoretical foundations, algorithmic details, and numerical benchmarks to showcase the larger systems that ExaChem can simulate and how the integration of photonic degrees-of-freedom alters their ground-state properties.

Basis sets↗