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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

How Distributed Energy Resources Can Support Resilience in Utility Distribution Networks

The goal of this webinar is to engage with electric utilities in the Midwest, particularly small public utilities, to understand the industry's needs for science tools to plan for winter resilience in the future, designing tools that will benefit electric power resilience in all communities. Michigan Tech leads this project with partners from multiple academic, government, and industry groups and asked NLR to present on DERs and laboratory tools and resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evolution of parton distribution functions in the short-distance factorization scheme

Lattice QCD offers the possibility of computing parton distributions from first principles, although not in the usual $\overline{MS}$ factorization scheme. Calculations are therefore matched to $\overline{MS}$ using a perturbative procedure which is the source of significant uncertainty within the currently accessible kinematics. We present the possibility of computing the z 2 evolution of non-singlet pseudo-parton distribution functions within the short factorization scheme in a numerically improvable way. The goal is to have tools to evolve a calculation to a scale where perturbative uncertainties are less pronounced. We compare a numerical extraction of the evolution operator from lattice data to the computation of z 2 dependence in perturbation theory. Finally, we discuss how this numerical work may be extended to address the two-scale problem that arises when the Ioffe time range must be made large to extend the reach of the calculation of the pseudo-PDF to smaller values of the momentum fraction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Development of low temperature fuel cell holders for Operando x-ray micro and nano computed tomography to visualize water distribution

Abstract Synchrotron x-ray imaging techniques, like x-ray computed tomography (CT) and radiography have proven instrumental in expanding the communities knowledge of complex transport and reaction kinetics in electrochemical devices such as fuel cells and electrolyzers. This work presents the development of novel x-ray CT imaging techniques for operando visualization of water within low temperature fuel cells at spatial resolutions spanning the micro and nano scales. The design of operando sample holders, for both micro x-ray CT and nano CT experiments is described in depth, and prototypes of these sample holders were evaluated across a set of requirements, the most important of which are x-ray transmissibility, electrical conductivity and mechanical stability. Water segmentation from micro x-ray CT data was enabled by an image subtraction method, where the image without water is subtracted from the one with water. Through iterative experimentations, the operando nano CT cell was developed to optimize mechanical compression, electric conductivity and gas flow. While three-dimensional fuel cell reconstructions were shown possible, there remain challenges to overcome at typical lower energies (8 keV) due to beam damage, whereas it is not as significant for higher energies (>17.5 keV).

Kulkarni, Devashish↗
DOE OSTI2025

Unorthodox parallelization for Bayesian quantum state estimation

Quantum state tomography (QST) allows for the reconstruction of quantum states through measurements and some inference technique under the assumption of repeated state preparations. Bayesian inference provides a promising platform to achieve both efficient QST and accurate uncertainty quantification, yet is generally plagued by the computational limitations associated with long Markov chains. In this work, we present a novel Bayesian QST approach that leverages modern distributed parallel computer architectures to efficiently sample a D-dimensional Hilbert space. Using a parallelized preconditioned Crank–Nicholson Metropolis–Hastings algorithm, we demonstrate our approach on simulated data and experimental results from IBM Quantum systems up to four qubits, showing significant speedups through parallelization. Although highly unorthodox in pooling independent Markov chains, our method proves remarkably practical, with validation ex post facto via diagnostics like the intrachain autocorrelation time. We conclude by discussing scalability to higher-dimensional systems, offering a path toward efficient and accurate Bayesian characterization of large quantum systems.

Bayesian inference↗
DOE OSTI2024

Systematic Uncertainties from Gribov Copies in Lattice Calculation of Parton Distributions in the Coulomb Gauge

Recently, a new method has been proposed to compute parton distributions using boosted correlators fixed in the Coulomb gauge (CG) within the framework of large-momentum effective theory. This approach, which does not involve Wilson lines, could greatly improve the efficiency and precision of lattice quantum chromodynamics calculations. However, concerns remain regarding whether systematic uncertainties from Gribov copies, which correspond to ambiguities in lattice gauge-fixing, are adequately controlled. This work assesses the effects of Gribov copies on Coulomb-gauge-fixed quark correlators. We utilize different strategies for Coulomb-gauge fixing, selecting two different groups of Gribov copies based on lattice gauge configurations. We examine the differences in the resulting spatial quark correlators in both vacuum and pion states. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise, demonstrating that the effect of the Gribov copies can be neglected in practical lattice calculations of quark parton distributions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Systems and methods for distributed power system model calibration

A computing device for distributed power system model calibration is provided. The computing device is programmed to receive event data and model response data associated with a model to simulate, wherein the model includes a plurality of parameters, divide the event data into a plurality of sets, wherein each set includes associated parameters, and transmit the plurality of sets of event data to a plurality of client nodes. Each client node of the plurality of client nodes is programmed to analyze a corresponding set of event data to determine updated parameters for the model. The computing device is further programmed to receive a plurality of updated parameters for the model from the plurality of client nodes and analyze the received plurality of updated parameters to determine at least one adjusted parameter.

Wang, Honggang↗
DOE OSTI2025

Cosmic Reionization on Computers: Statistical Properties of the Distributions of Mean Opacities

Quasar absorption lines provide a unique window to the relationship between galaxies and the intergalactic medium during the Epoch of Reionization. In particular, high redshift quasars enable measurements of the neutral hydrogen content of the universe. However, the limited sample size of observed quasar spectra, particularly at the highest redshifts, hampers our ability to fully characterize the intergalactic medium during this epoch from observations alone. In this work, we characterize the distributions of mean opacities of the intergalactic medium in simulations from the Cosmic Reionization on Computers (CROC) project. We find that the distribution of mean opacities along sightlines follows a non-trivial distribution that cannot be easily approximated by a known distribution. When comparing the cumulative distribution function of mean opacities measurements in subsamples of sample sizes similar to observational measurements from the literature, we find consistency between CROC and observations at redshifts $z\lesssim 5.7$. However, at higher redshifts ($z\gtrsim5.7$), the cumulative distribution function of mean opacities from CROC is notably narrower than those from observed quasar sightlines implying that observations probe a systematically more opaque intergalactic medium at higher redshifts than the intergalactic medium in CROC boxes at these same redshifts. This is consistent with previous analyses that indicate that the universe is reionized too early in CROC simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Design and implementation of I/O performance prediction scheme on HPC systems through large-scale log analysis

Abstract Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units used by hundreds to thousands of users simultaneously. Applications from large numbers of users have diverse characteristics, such as varying computation, communication, memory, and I/O intensity. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, I/O performance is becoming increasingly important as data sizes rapidly increase and large-scale applications, such as simulation and model training, are widely adopted. However, predicting I/O performance is difficult because I/O systems are shared among all users and involve many layers of software and hardware stack, including the application, network interconnect, operating system, file system, and storage devices. Furthermore, updates to these layers and changes in system management policy can significantly alter the I/O behavior of applications and the entire system. To improve the prediction of the I/O performance on HPC systems, we propose integrating information from several different system logs and developing a regression-based approach to predict the I/O performance. Our proposed scheme can dynamically select the most relevant features from the log entries using various feature selection algorithms and scoring functions, and can automatically select the regression algorithm with the best accuracy for the prediction task. The evaluation results show that our proposed scheme can predict the write performance with up to 90% prediction accuracy and the read performance with up to 99% prediction accuracy using the real logs from the Cori supercomputer system at NERSC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2024

Modifying the Asynchronous Jacobi Method for Data Corruption Resilience

Moving scientific computation from high-performance computing (HPC) and cloud computing (CC) environments to devices on the edge, i.e., physically near instruments of interest, has received tremendous interest in recent years. Such edge computing environments can operate on data in situ, offering enticing benefits over data aggregation to HPC and CC facilities that include avoiding costs of transmission, increased data privacy, and real-time data analysis. Because of the inherent unreliability of edge computing environments, new fault-tolerant approaches must be developed before the benefits of edge computing can be realized. Motivated by algorithm-based fault tolerance, a variant of the asynchronous Jacobi (ASJ) method is developed that achieves resilience to data corruption by rejecting solution approximations from neighbor devices according to a bound derived from convergence theory. Numerical results on a two-dimensional Poisson problem show that the new rejection criterion, along with a novel approximation to the shortest path length on which the criterion depends, restores convergence for the ASJ variant in the presence of certain types data corruption. Numerical results are obtained for when the singular values in the analytic bound are approximated. Additional linear systems are also explored, one with a more dense sparsity pattern and one that includes advection. All results indicate that successful resilience to data corruption depends on whether the bound tightens fast enough to reject corrupted data before the iteration evolution deviates significantly from that predicted by the convergence theory defining the bound. This observation generalizes to future work on algorithm-based fault tolerance for other asynchronous algorithms, including upcoming approaches that leverage Krylov subspaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment (FY2021--Part 1)

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Support for Advanced Non-Light Water Reactors.” The tasks completed for this report are as follows: First, Task 1d: The net radiation transfer method was implemented into MOOSE for modeling reactor cavity cooling system geometries. RCCS models for two experiments were created: (1) Natural Convection Shutdown Heat Removal Test Facility (NSTF) experiment R022, and (2) HTTR VCS mockup. For validation, computed temperature distributions were compared to measured temperatures. Next, Task 4c: An algorithm for computing the pebble bed reactor equilibrium core isotopic com-position was developed and an initial version is implemented into the reactor multi-physics code Griffin. Initial results for a simplified axisymmetric pebble bed reactor are presented. Finally, Task 7: generation of a reference plant model for molten salt cooled pebble bed reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Cybersecurity Considerations for Grid-Connected Batteries with Hardware Demonstrations

The share of renewable and distributed energy resources (DERs), like wind turbines, solar photovoltaics and grid-connected batteries, interconnected to the electric grid is rapidly increasing due to reduced costs, rising efficiency, and regulatory requirements aimed at incentivizing a lower-carbon electricity system. These distributed energy resources differ from traditional generation in many ways including the use of many smaller devices connected primarily (but not exclusively) to the distribution network, rather than few larger devices connected to the transmission network. DERs being installed today often include modern communication hardware like cellular modems and WiFi connectivity and, in addition, the inverters used to connect these resources to the grid are gaining increasingly complex capabilities, like providing voltage and frequency support or supporting microgrids. To perform these new functions safely, communications to the device and more complex controls are required. The distributed nature of DER devices combined with their network connectivity and complex controls interfaces present a larger potential attack surface for adversaries looking to create instability in power systems. To address this area of concern, the steps of a cyberattack on DERs have been studied, including the security of industrial protocols, the misuse of the DER interface, and the physical impacts. These different steps have not previously been tied together in practice and not specifically studied for grid-connected storage devices. In this work, we focus on grid-connected batteries. We explore the potential impacts of a cyberattack on a battery to power system stability, to the battery hardware, and on economics for various stakeholders. We then use real hardware to demonstrate end-to-end attack paths exist when security features are disabled or misconfigured. Our experimental focus is on control interface security and protocol security, with the initial assumption that an adversary has gained access to the network to which the device is connected. We provide real examples of the effectiveness of certain defenses. This work can be used to help utilities and other grid-connected battery owners and operators evaluate the severity of different threats and the effectiveness of defense strategies so they can effectively deploy and protect grid-connected storage devices.

25 ENERGY STORAGE↗
DOE OSTI2025

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation

The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

FOS: Computer and information sciences↗
DOE OSTI2022

Userspace Squash Filesystem for Launching Linux Containers on HPC Systems [Thesis]

The demand for user defined software stacks (UDSS) has been increasing in the high-performance computing (HPC) community. Container technology has become popular due to the flexibility and isolation it provides to HPC users. Container images must be available to all nodes involved for use in HPC and can be distributed to compute nodes in a variety of ways. A common method for container image distribution is to simply copy the container image to memory on each compute node, which can be time consuming at scale, and uses valuable memory on each node. The kernel mounted squash filesystem (squashfs) has proven fast and efficient for this task but requires root-level access. This paper will show a user space mounted squashfs is an efficient and secure solution for container image distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

97 MATHEMATICS AND COMPUTING↗