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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Glassy Word Problems: Ultraslow Relaxation, Hilbert Space Jamming, and Computational Complexity

We introduce a family of local models of dynamics based on “word problems” from computer science and group theory, for which we can place rigorous lower bounds on relaxation timescales. These models can be regarded either as random circuit or local Hamiltonian dynamics and include many familiar examples of constrained dynamics as special cases. The configuration space of these models splits into dynamically disconnected sectors, and for initial states to relax, they must “work out” the other states in the sector to which they belong. When this problem has a high time complexity, relaxation is slow. In some of the cases we study, this problem also has high space complexity. When the space complexity is larger than the system size, an unconventional type of jamming transition can occur, whereby a system of a fixed size is not ergodic but can be made ergodic by appending a large reservoir of sites in a trivial product state. This finding manifests itself in a new type of Hilbert space fragmentation that we call fragile fragmentation. We present explicit examples where slow relaxation and jamming strongly modify the hydrodynamics of conserved densities. In one example, density modulations of wave vector q exhibit almost no relaxation until times O ( exp ( 1 / q ) ) , at which point they abruptly collapse. We also comment on extensions of our results to higher dimensions. Published by the American Physical Society 2024

Physics↗
DOE OSTI2025

Real-World Cyber Security Demonstration for Networked Electric Drives

In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed's capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed's security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed's usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2025

Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor placement for parameter identification. The state-of-the-art algorithmic approaches following this simple formulation generally overlook misspecification of the elements of the inverse problem, such as the prior or the measurement uncertainties. This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian linear inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem. Specifically, we consider a worst-case scenario approach for the uncertain or misspecified parameters, formulate robust objectives, and propose an algorithmic approach for optimizing such objectives. Furthermore, both relaxation and stochastic solution approaches are discussed with detailed analysis and insight into the interpretation of the problem and the proposed algorithmic approach. Extensive numerical experiments to validate and analyze the proposed approach are carried out for sensor placement in a parameter identification problem.

Bayesian inverse problems↗
DOE OSTI2025

Deploying and Tracking Software with NCCS Software Provisioning

The National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory has a long history of deploying ground-breaking leadership-class supercomputers for the U.S. Department of Energy. The latest in this line of supercomputers is Frontier, the first supercomputer to break the exascale barrier (1018 floating-point operations per second) on the TOP500 list. Frontier serves a wide array of scientific domains, from traditional simulation-based workloads to newer AI and Machine Learning workloads. To best serve the NCCS user community, NCCS uses Spack to deploy a comprehensive software stack of scientific software packages, providing straightforward access to these packages through Lmod Environment Modules. Maintaining a large software stack while also including multiple new compiler releases each year is a very time-consuming task. Additionally, it is not straightforward to provide a software stack alongside existing vendor-provided software such as the HPE/Cray Programming Environment (CPE), and existing CPE, Spack, and Lmod integration does not allow for multiple versions of GPU libraries such as AMD’s ROCm to be used. To address these challenges and shortcomings, NCCS has developed the NCCS Software Provisioning tool (NSP)1, a tool for deploying and monitoring software stacks on HPC systems. NSP allows NCCS to quickly and effectively provision software stacks from the ground up using template-driven recipes and configuration files. NSP is successfully deployed on Frontier and several other NCCS clusters, enabling the NCCS software team to quickly deploy software stacks for newly-released compilers, expand current software offerings, better support GPU-based software, and monitor Lmod module usage to identify unused software packages that can be removed from the software stack. In this work, we discuss the shortcomings of the previous CPE, Spack, and Lmod usage at NCCS, provide further details on the implementation and structure of NSP, then discuss the benefits that NSP provides.

Rentschler, Asa [ORNL] (ORCID:0009000597694743)↗
DOE OSTI2026

Spatially Accelerated Winding Numbers for Curved Geometry

The generalized winding number (GWN) is a scalar field that supports robust containment queries on curved geometry, including non-watertight, overlapping, and nested boundary representations. While queries can be easily parallelized over samples, direct evaluation on parametric curves and surfaces remains costly for large and complex models. Fast, state-of-the-art GWN approaches leverage a spatial index to approximate the GWN, typically coupled with a Taylor expansion which approximates the GWN contribution for far clusters of geometric primitives. However, such methods operate only on discrete inputs such as triangle meshes and point clouds, and would introduce containment errors near boundaries if applied to curved input. We extend support for fast GWN evaluation over arbitrary collections of NURBS curves in 2D and trimmed NURBS patches in 3D via a Bounding Volume Hierarchy that stores efficiently precomputed moment data in the hierarchy nodes. When querying the hierarchy, approximations for far clusters are used alongside direct evaluation for nearby NURBS primitives, achieving sub-linear complexity while preserving the geometric features in the vicinity of the query point. Central to our performance improvements is an adaptive subdivision strategy for NURBS primitives during a preprocessing phase, creating better spatial partitions while retaining the same accuracy for containment decisions as a direct evaluation. We demonstrate the performance and accuracy of our approach across a large collection of 2D and 3D datasets.

Computer science↗
DOE OSTI2024

UOWDetection

The code provides machine learning, data collection, and computer science tools used to detect undocumented orphaned wells in United States from aerial imagery.

Kim, Anastasiia↗
DOE OSTI2025

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Accelerating Control Systems with GitOps: A Path to Automation and Reliability

GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN and Diamond Light Source among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab’s control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.

Gonzalez, M. [Fermilab]↗
DOE OSTI2025

Fiats: Functional inference and training for surrogates

Fiats provides a platform for research on the training and deployment of neural-network surrogate models for computational science. Fiats also supports exploring, advancing, and combining functional, object-oriented, and parallel programming patterns in Fortran 2023. As such, the Fiats name has dual expansions: “Functional Inference And Training for Surrogates” or “Fortran Inference And Training for Science.” Fiats inference and training procedures are pure and therefore satisfy a language constraint imposed on procedure invocations inside Fortran’s parallel loop construct: do concurrent. Furthermore, the Fiats training procedures are built around a do concurrent parallel reduction. Several compilers can automatically parallelize do concurrent on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Fiats thus aims to achieve performance portability through standard language mechanisms.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Sandia Wind Energy Program - FY23 Accomplishments

This report summarizes Fiscal Year 2023 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), the Sandia Laboratory Directed Research and Development (LDRD) program, and private industry. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

17 WIND ENERGY↗
DOE OSTI2024

FAIR Framework for Physics-Inspired AI in High Energy Physics (Final Technical Report)

The main deliverable of this proposal was to publish data from high energy physics experiments in a FAIR format so that non-specialists could develop machine learning technologies using our data. The Minnesota team of Profs. Cushman, Furmanski and Rusack, from the high energy experiments CDMS, Micro-Boone and CMS, respectively, and Prof J. Sun from Computer Science worked to organize the data, to provide code to access the data, and where relevant provide documentation describing the data. The FAIR4HEP collaboration was formed with groups from UC San Diego, MIT, and the University of Illinois, with the principal investigator was Dr. Huerta. Collectively we collaborated on the publication of datasets from the LHC experiments. Members of the Minnesota group contributed to the common papers published by the collaboration

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Towards Exascale Astrophysics of Mergers and Supernovae (TEAMS)

The TEAMS project brought together cutting-edge simulations, theoretical insights, and collaborative efforts to deepen our understanding of some of the universe’s most extreme phenomena—supernovae, neutron star mergers, and the powerful signals they emit. Using one of the largest suites of 3D supernova simulations ever conducted, researchers uncovered new insights into how massive stars explode, how those explosions vary by stellar mass, and what conditions lead to the birth of neutron stars or black holes. They also studied the radiation and gravitational wave signals emitted during these events, revealing how future observations can be used to uncover what happens deep inside collapsing stars. The team developed improved tools for modeling how light and neutrinos behave in such explosive environments, enabling more accurate predictions of what astronomers might observe. Work also explored how the chemical composition and geometry of kilonovae—the visible explosions that follow neutron star mergers—influence their signals and can reveal the origins of heavy elements like gold. These efforts not only advanced scientific knowledge, but also trained a new generation of researchers at the intersection of astrophysics, computational science, and nuclear theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Developing and Distributing HEP Software Stacks with Spack

The Computational Science and AI Directorate at Fermilab is using Spack to support the development efforts of a large number of scientific programmers, in many independent projects and experiments. While independent, these projects share many dependencies. They are typically under continuous and fairly rapid development. They have to support deployment on diverse hardware. This is a different context than is typical for the management of HPC software, where Spack was born. To support our community, we have created a model that enables users to develop code with greater efficiency than is possible with Spack’s current development facilities. In this talk we will present: - a brief introduction to the science we support (particle physics) - how the code we work with is naturally organized into several layers of packages - how we are using Spack to manage those layers - how we leverage the layering to provide efficient support for developers, using our Spack extension “MPD”. - some suggestions for changes or additions to Spack to make such work easier.

Knoepfel, Kyle J. [Fermilab]↗
DOE OSTI2026

Cooperative Education

Los Alamos National Laboratory (LANL) is a multidisciplinary national laboratory that conducts research and development in national security, engineering, materials science, computational modeling, and advanced manufacturing. The laboratory develops innovative technologies to address complex scientific and engineering challenges. This project focuses on the development and evaluation of high-performance absorbing structures through computational design, simulation, and engineering analysis. Absorbing structures are used in applications where damage mitigation, structural protection, and material efficiency are critical performance requirements. The increasing demand for lightweight, high-strength, and highly efficient structural systems has created a need for improved design methodologies capable of maximizing absorption while minimizing weight and material usage. The project utilizes advanced engineering software, including 3D CAD software and FEA, to generate and optimize structural concepts. Computational simulations are performed to evaluate structural behavior under loading conditions, while mathematical analyses are conducted using Python-based tools as well as established analytical equations from material and structural mechanics. The project benefits LANL by supporting the development of advanced design methodologies and improving the understanding of material and structural performance. During the internship term, a significant portion of the design development, simulation, and data analysis activities will be completed. Success of the project depends on collaboration among engineering mentors and technical staff members. Work will be conducted at Los Alamos National Laboratory using laboratory computing resources and engineering software.

42 ENGINEERING↗
DOE OSTI2025

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗