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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probabilistic Grid Reliability Analysis with Energy Storage Systems

SAND2025-12025O The Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS) software tool is an open-source tool for assessing the resource adequacy of the evolving electric power grid integrated with energy storage systems (ESS). This tool uses a simulation engine to create diverse scenarios that test the limits of the modern power grid consisting of a high-volume ESS and variable energy resources (VER). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Tu↗
DOE OSTI2016

The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for “intensity modulated radiation therapy (IMRT)” as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

NASA Year 2000 (Y2K) Program Plan

NASA initiated the Year 2000 (Y2K) program in August 1996 to address the challenges imposed on Agency software, hardware, and firmware systems by the new millennium. The Agency program is centrally managed by the NASA Chief Information Officer, with decentralized execution of program requirements at each of the nine NASA Centers, Headquarters and the Jet Propulsion Laboratory. The purpose of this Program Plan is to establish Program objectives and performance goals; identify Program requirements; describe the management structure; and detail Program resources, schedules, and controls. Project plans are established for each NASA Center, Headquarters, and the Jet Propulsion Laboratory.

Source record↗
DOE OSTI2016

Pursuing Cost Effective Tank Waste Characterization at the Savannah River Site - 16172

The U. S. Department of Energy Office of Environmental Management has tasked the Savannah River National Laboratory (SRNL) with developing strategies and technologies to understand, optimize, scale, and speed up tank waste characterization. This scope is part of a multi-year project with the end goal of implementing programmatic changes that accelerate tank waste processing and tank closure schedules, and at the same time reduce characterization costs, while maintaining data integrity. The project is currently in its second year. During the first year of the project, SRNL completed a series of activities focused on understanding the range of current characterization practices, needs, gaps, risks, and potential alternative approaches. Based on the results, conclusions were drawn regarding aspects of the programs that are most costly and time consuming; particular waste constituents that drive costs, schedule, and program risks; alternative characterization approaches (other than sampling and analysis) that are less expensive and more rapid; new laboratory methods holding the greatest promise for reducing characterization costs and schedule; and in whole, the primary areas where current characterization practices can be improved. SRNL's current scope is focusing on three primary activities: 1) development of a technical basis/strategy for improving the cost effectiveness and schedule of SRNL's tank closure characterization program; 2) design/assembly of hardware, plumbing, and software for automating select radiochemical separation and waste removal processes; and 3) development and testing of alternative radiochemical separation protocols most likely to improve high resource demand/time consuming analysis methods (such as Ra-226, Pa-231, Tc-99/I-129, and/or Y/trivalent actinide separations). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2001

Enabling Advanced Automation in Spacecraft Operations with the Spacecraft Emergency Response System

True autonomy is the Holy Grail of spacecraft mission operations. The goal of launching a satellite and letting it manage itself throughout its useful life is a worthy one. With true autonomy, the cost of mission operations would be reduced to a negligible amount. Under full autonomy, any problems (no matter the severity or type) that may arise with the spacecraft would be handled without any human intervention via some combination of smart sensors, on-board intelligence, and/or smart automated ground system. Until the day that complete autonomy is practical and affordable to deploy, incremental steps of deploying ever-increasing levels of automation (computerization of once manual tasks) on the ground and on the spacecraft are gradually decreasing the cost of mission operations. For example, NASA's Goddard Space Flight Center (NASA-GSFC) has been flying spacecraft with low cost operations for several years. NASA-GSFC's SMEX (Small Explorer) and MIDEX (Middle Explorer) missions have effectively deployed significant amounts of automation to enable the missions to fly predominately in 'light-out' mode. Under light-out operations the ground system is run without human intervention. Various tools perform many of the tasks previously performed by the human operators. One of the major issues in reducing human staff in favor of automation is the perceived increased in risk of losing data, or even losing a spacecraft, because of anomalous conditions that may occur when there is no one in the control center. When things go wrong, missions deploying advanced automation need to be sure that anomalous conditions are detected and that key personal are notified in a timely manner so that on-call team members can react to those conditions. To ensure the health and safety of its lights-out missions, NASA-GSFC's Advanced Automation and Autonomy branch (Code 588) developed the Spacecraft Emergency Response System (SERS). The SERS is a Web-based collaborative environment that enables secure distributed fault and resource management. The SERS incorporates the use of intelligent agents, threaded discussions, workflow, database connectivity, and links to a variety of communications devices (e.g., two-way paging, PDA's, and Internet phones) via commercial gateways. When the SERS detects a problem, it notifies on-call team members, who then can remotely take any necessary actions to resolve the anomalies.The SERS goes well beyond a simple '911' system that sends out an error code to everyone with a pager. Instead, SERS' software agents send detailed data (i.e., notifications) to the most appropriate team members based on the type and severity of the anomaly and the skills of the on-call team members. The SERS also allows the team members to respond to the notifications from their wireless devices. This unique capability ensures rapid response since the team members no longer have to go to a PC or the control center for every anomalous event. Most importantly, the SERS enables safe experimentation with various techniques for increasing levels of automation, leading to robust autonomy. For the MIDEX missions at NASA GSFC, the SERS is used to provide 'human-in-the-loop' automation. During lights-out operations, as greater control is given to the MIDEX automated systems, the SERS can be configured to page remote personnel and keep them informed regarding actions taking place in the control center. Remote off-duty operators can even be given the option of enabling or inhibiting a specific automated response in near real time via their two-way pagers. The SERS facilitates insertion of new technology to increase automation, while maintaining the safety and security of mission resources. This paper will focus on SERS' overall functionality and how SERS has been designed to handle the monitoring and emergency response for missions with varying levels of automation. The paper will also convey some of the key lessons learned from SERS' deployment across of variety of missions, highlighting this incremental approach to achieving 'robust autonomy'.

Breed, Julie↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗
DOE OSTI2018

Distribution Feeder Modeling for Time-Series Simulation of Voltage Management Strategies

This paper presents techniques to create baseline distribution models using a utility feeder from Hawai'ian Electric Company. It describes the software-to-software conversion, steady-state and time-series validations of a utility feeder model. It also presents a methodology to add secondary low-voltage circuit models to accurately capture the voltage at the customer meter level. This enables preparing models to perform studies that simulate how customer-sited resources integrate into legacy utility distribution system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Vibroacoustic payload environment prediction system (VAPEPS): Data base management center remote access guide

A Vibroacoustic Data Base Management Center has been established at the Jet Propulsion Laboratory (JPL). The center utilizes the Vibroacoustic Payload Environment Prediction System (VAPEPS) software package to manage a data base of shuttle and expendable launch vehicle flight and ground test data. Remote terminal access over telephone lines to a dedicated VAPEPS computer system has been established to provide the payload community a convenient means of querying the global VAPEPS data base. This guide describes the functions of the JPL Data Base Management Center and contains instructions for utilizing the resources of the center.

Thomas, V. C.↗
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
DOE OSTI2018

Distribution Feeder Modeling for Time-Series Simulation of Voltage Management Strategies: Preprint

This paper presents techniques to create baseline distribution models using a utility feeder from Hawai'ian Electric Company. It describes the software-to-software conversion, steady-state, and time-series validations of a utility feeder model. It also presents a methodology to add secondary low-voltage circuit models to accurately capture the voltage at the customer meter level. This enables preparing models to perform studies that simulate how customer-sited resources integrate into legacy utility distribution system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Benchmarking Near-Surface Winds in the HRRR Analyses Using Multisource Observations over Complex Terrain in the Southeastern United States

Wind energy plays a crucial role in sustainable power generation, yet its full potential in the southeast United States (SEUS) remains underexplored. This study advances wind resource assessment in the SEUS using existing regional modeling and multisource observations. We find that terrain complexity, influenced by orography and forest canopy, significantly impacts the accuracy of modeled wind speed. While the High-Resolution Rapid Refresh (HRRR) model effectively simulates wind profiles over flat, nonforested terrain, larger errors are produced above forest canopies, and particularly for those stands in hilly and mountainous terrain where there are no observations aloft that could be used by HRRR’s data assimilation software. Seasonal variations, e.g., changes in the leaf area index, further complicate the relationship between terrain complexity and simulation errors. Here, our findings emphasize the critical need for comprehensive wind profile measurements, extending from below the canopy to height above the canopy and through the lower planetary boundary layer, to fully quantify model performance over and near forests, and for improving wind resource assessment, planning, and management. This study provides valuable insights not only for further model development but also for future field campaign deployment with the goal of further improving our understanding of wind resources in the region.

Complex terrain↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Vibroacoustic Payload Environment Prediction System (VAPEPS): VAPEPS management center remote access guide

A Vibroacoustic Payload Environment Prediction System (VAPEPS) Management Center was established at the JPL. The center utilizes the VAPEPS software package to manage a data base of Space Shuttle and expendable launch vehicle payload flight and ground test data. Remote terminal access over telephone lines to the computer system, where the program resides, was established to provide the payload community a convenient means of querying the global VAPEPS data base. This guide describes the functions of the VAPEPS Management Center and contains instructions for utilizing the resources of the center.

Fernandez, J. P.↗
DOE OSTI2017

Offshore Storage Resource Assessment - Final Scientific/Technical Report

The DOE developed volumetric equation for estimating Prospective Resources (CO 2 storage) in oil and gas reservoirs was utilized on each depleted field in the Federal GOM. This required assessment of the in-situ hydrocarbon fluid volumes for the fields under evaluation in order to apply the DOE equation. This project utilized public data from the U.S. Department of the Interior, Bureau of Ocean Energy Management (BOEM) Reserves database and from a well reputed, large database (250,000+ wells) of GOM well and production data marketed by IHS, Inc. IHS interpreted structure map files were also accessed for a limited number of fields. The databases were used along with geological and petrophysical software to identify depleted oil and gas fields in the Federal GOM region. BOEM arranged for access by the project team to proprietary reservoir level maps under an NDA. Review of the BOEM’s Reserves database as of December 31, 2013 indicated that 675 fields in the region were depleted. NITEC identified and rank these 675 fields containing 3,514 individual reservoirs based on BOEM’s estimated OOIP or OGIP values available in the Reserves database. The estimated BOEM OOIP or OGIP values for five fields were validated by an independent evaluation using available petrophysical, geologic and engineering data in the databases. Once this validation was successfully completed, the BOEM ranked list was used to calculate the estimated CO 2 storage volume for each field/reservoir using the DOE CO 2 Resource Estimate Equation. This calculation assumed a range for the CO 2 efficiency factor in the equation, as it was not known at that point in time. NITEC then utilize reservoir simulation to further enhance and refine the DOE equation estimated range of CO 2 storage volumes. NITEC used a purpose built, publically available, 4-component, compositional reservoir simulator developed under funding from DOE (DE-FE0006015) to assess CO 2 -EOR and CO 2 storage in 73 fields/461 reservoirs. This simulator was fast and easy to utilize and provided a valuable enhanced assessment and refinement of the estimated CO 2 storage volume for each reservoir simulated. The user interface was expanded to allow for calculation of a probability based assessment of the CO 2 storage volume based on typical uncertainties in operating conditions and reservoir properties during the CO 2 injection period. This modeling of the CO 2 storage estimates for the simulated reservoirs resulted in definition of correlations applicable to all reservoir types (a refined DOE equation) which can be used for predictive purposes using available public data. Application of the correlations to the 675 depleted fields yielded a total CO 2 storage capacity of 4,748 MM tons. The CO 2 storage assessments were supplemented with simulation modeling of eleven (11) oil reservoirs that quantified the change in the stored CO 2 storage volume with the addition of CO 2 -EOR (Enhanced Oil Recovery) production. Application of CO 2 -EOR to oil reservoirs resulted in higher volumes of CO 2 storage.

02 PETROLEUM↗
DOE OSTI2017

XPRESS: eXascale PRogramming Environment and System Software

The XPRESS Project is one of four major projects of the DOE Office of Science Advanced Scientific Computing Research X-stack Program initiated in September, 2012. The purpose of XPRESS is to devise an innovative system software stack to enable practical and useful exascale computing around the end of the decade with near-term contributions to efficient and scalable operation of trans-Petaflops performance systems in the next two to three years; both for DOE mission-critical applications. To this end, XPRESS directly addresses critical challenges in computing of efficiency, scalability, and programmability through introspective methods of dynamic adaptive resource management and task scheduling.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

ADP Analysis project for the Human Resources Management Division

The ADP (Automated Data Processing) Analysis Project was conducted for the Human Resources Management Division (HRMD) of NASA's Langley Research Center. The three major areas of work in the project were computer support, automated inventory analysis, and an ADP study for the Division. The goal of the computer support work was to determine automation needs of Division personnel and help them solve computing problems. The goal of automated inventory analysis was to find a way to analyze installed software and usage on a Macintosh. Finally, the ADP functional systems study for the Division was designed to assess future HRMD needs concerning ADP organization and activities.

Tureman, Robert L., Jr.↗
NASA NTRS1991

Landsat Legacy

The Landsat resources survey system spawned a number of companies engaged in commercial applications of remote sensing, among them International Imaging Systems (I2S). With initial NASA assistance, I2S has provided remote sensing hardware and software to several foreign countries, developed meteorological analysis systems, medical diagnostic software and scanning equipment for government and commercial use. Latest product is an advanced image-based photogrammetric system employing digital technology - not optical or mechanical systems - to generate terrain elevation data and other processing functions. Called PRI2SM, it compensates automatically for topographic relief displacement, is cheaper, faster, and easier to use and maintain. Company product line includes four major areas: image processing equipment for Earth Resources Management; meteorological analysis systems; satellite ground processing systems; and digital photogrammetric mapping systems.

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