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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Queue wait time prediction in high performance computing (HPC) systems

High Performance Computing (HPC) systems are critical enablers for groundbreaking scientific research across various domains. Efficient resource allocation, facilitated by job scheduling, is paramount for maximizing the utilization of HPC systems. However, the variability in wait times for queued jobs poses challenges for users, necessitating accurate job wait time estimation. This paper explores the influence of job characteristics, including job size (the number of nodes requested and walltime), the queue to which the job is submitted and other resource requirements, on job wait times in leadership-class HPC systems. Focusing on the Theta Cray XC40 and Polaris machines at Argonne National Laboratory, the study evaluates the performance of different supervised learning algorithms in predicting job wait times. It also evaluates the impact of data preprocessing, including outlier detection, Principal Component Analysis (PCA), and feature selection, on the performance of wait time prediction models. The findings reveal insights into the relationship between job characteristics and wait times, offering a foundation for optimizing resource allocation and enhancing user experience. The methodologies and tools developed in this study are adaptable to other leadership-class HPC systems, providing a valuable contribution to the broader HPC community aiming to improve job scheduling efficiency and user satisfaction.

Okafor, Nwamaka↗
DOE OSTI2026

Lessons learned from the development and implementation of a workforce training curriculum for advanced controls for high performance HVAC systems

Over the past decade, academic research on advanced controls has slowly transitioned into new software platforms, giving rise to various companies developing and deploying these innovative products, including solutions for light commercial HVAC systems. However, the current workforce remains widely unprepared to install, maintain and operate these systems, particularly complex software-based control platforms, as most workforce training programs still focus on traditional building automation for large commercial buildings. This paper presents the development and piloting of curriculum for three key types of professionals: ● Technicians (trade-level): installing and maintaining modern high-performance HVAC systems and controls ● Programmers (undergrad-level): developing and implementing advanced controls ● Engineers and energy professionals (undergrad/grad-level): managing and evaluating system performance We share details of the material developed including training videos, open-source software, instruction manuals. We also present the results of a pilot implementation of the training materials with real students.

Casillas, Armando↗
DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dopant for improving casting and electroplating performance

Systems, methods, components, and parts are provided for improving casting and electroplating performance of a plated cast part by doping a semiconductor material with an electrically active dopant before mixing the semiconductor material into a base material. The doped semiconductor material improves the castability of the base material and has an improved electrical conductivity which is closer to that of the base material such that a consistency of a subsequent plating on the part is improved.

Young, Barbara Diane↗
DOE OSTI2022

Energy Management Information Systems Cybersecurity Best Practices

Energy management information systems (EMIS) are a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance. Critical systems are often integrated with or operate on the same networks as EMIS scope systems, necessitating stable, continuous, and secure communication. When connecting EMIS to building automation and utility control systems, there are also many physical assets that could cause harm to the building and its occupants if a malicious act or human error were introduced. It is imperative to ensure all EMIS scope systems are connected securely to the EMIS and do not open vulnerable pathways to other facility networks and operations. The Federal Energy Management Program (FEMP) promotes best practices for impactful utilization of EMIS at federal facilities. This best practice document is part of a series of fact sheets created to help accelerate the market adoption and use of EMIS in the federal sector. It provides an overview of required EMIS cybersecurity standards for compliance and authority to operate along with additional recommendations.

cybersecurity↗
DOE OSTI2022

Energy Management Information Systems Cybersecurity Best Practices

Energy management information systems (EMIS) are a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance. Critical systems are often integrated with or operate on the same networks as EMIS scope systems, necessitating stable, continuous, and secure communication. When connecting EMIS to building automation and utility control systems, there are also many physical assets that could cause harm to the building and its occupants if a malicious act or human error were introduced. It is imperative to ensure all EMIS scope systems are connected securely to the EMIS and do not open vulnerable pathways to other facility networks and operations. The Federal Energy Management Program (FEMP) promotes best practices for impactful utilization of EMIS at federal facilities. This best practice document is part of a series of fact sheets created to help accelerate the market adoption and use of EMIS in the federal sector. It provides an overview of required EMIS cybersecurity standards for compliance and authority to operate along with additional recommendations.

Cybersecurity↗
DOE OSTI2025

Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint

Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.

adversarial threats↗
DOE OSTI2023

Unlocking the Value of Deep Energy Retrofits

This publication is based on an extensive study conducted on behalf of the New York State Energy Research and Development Authority (NYSERDA) and the U S Department of Energy (DOE) to explore financial and risk products to accelerate the implementation of deep energy retrofit (DER) solutions to reduce costs These financial products are intended to speed market development via three fundamental themes: reallocating risk from building owners and lenders to insurers, quantifying and monetizing previously unrecognized value associated with DERs, and providing building owners and lenders with confidence in the performance of building systems Findings are based on a comprehensive analysis and characterization of 100+ existing DER case studies, and more than 40 qualitative interviews with industry experts, including insurers, researchers, building owners, and policymakers In addition, a quantitative model of overall project economics with baseline, high, and low cases was developed This data informed the analysis that resulted in three recommended financial product solutions, which were evaluated for their potential to raise projected cash flows and finance DERs: 1) Building System Performance and Energy Savings Guarantee; 2) Trade Credit Insurance; 3) Ancillary Revenue Contracts. This report identifies many value streams associated with DERs and introduces these three potential financial products, which aim to reallocate risks and reduce barriers to the adoption of advanced envelope DERs.

ancillary renenue contracts↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2025

A Brief Survey on High Performance Computing Systems Power Management

This paper provides a survey of software-based power management techniques in High Performance Computing (HPC) systems. Seven existing power management and monitoring tools and frameworks are discussed. These are: Variorum, dynamic energy-performance optimizer (DEPO), Powersched, Bull Dynamic Power Optimizer (BDPO), Energy Aware Runtime (EAR), Global Extensible Open Power Manager (GEOPM), and PoLiMEr. Each of these tools is evaluated based on hardware abstraction, optimization methods, usability, and experimental validation. This survey highlights the diversity of approaches in managing energy efficiency, from vendor-neutral APIs to algorithm-driven power capping, and dynamic frequency adjustments. Given that energy requirements for large computational systems is increasing quickly, the importance of integrating these tools into existing HPC environments and the need for further research in this rapidly evolving field is also discussed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Best practices for photovoltaic performance loss rate calculations

Abstract The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.

14 SOLAR ENERGY↗
DOE OSTI2022

An economic analysis of the role of materials, system engineering, and performance in electrochemical carbon dioxide conversion to formate

The development of technologies that utilize carbon dioxide is important to mitigating climate change. The electrochemical reduction of carbon dioxide is one technology that can utilize greenhouse gasses that would be otherwise be emitted to the atmosphere by producing chemicals and fuels from carbon dioxide and electricity. Significant progress has been made in the experimental performance of carbon dioxide reduction systems with novel catalyst designs, new materials, and systems engineering; however, no work has linked such changes in stack design and materials to capital costs for the stack itself. In this study, we present an analysis that accounts for and analyzes the impacts of alternative materials and system architectures on manufactured costs of carbon dioxide reduction stacks, thus providing a framework to understand exactly how these advances impact the at-scale capital costs of these systems. Specifically, we consider the impact that the addition of a catholyte buffer layer has on an electrolyzer reducing carbon dioxide to formate, finding that the cost of manufacturing this part only increases stack costs by about $30/m 2 at high manufacturing rates, while previous work finds that this part improves system performance. This work shows that the links between system performance, materials, and costs are nonlinear, and that achieving low-cost scalability requires optimization of not just performance parameters but also the use of low-cost and highly scalable materials. These results bridge experimental and techno-economic analysis of processes for carbon dioxide reduction, informing researchers by providing a quantifiable estimate of the impact of advances in electrochemical carbon dioxide reduction technology on manufactured stack capital costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Quantitative Small-Signal Stability Analysis of Grid-Forming Inverters in Large-Scale Power Systems

In this paper, the challenges induced by the integration of inverter-based resources (IBRs) into existing power systems are discussed. Although the grid-forming (GFM) control has been used to enhance system performance in multiple sections of electric power systems (e.g., transmission and distribution systems, microgrids, among others), the coupling mechanism between GFM inverter and various types of other components in modern power systems should be clearly explored. Existing analyses mainly focus on qualitative investigations that verify the effectiveness of deploying GFM inverters for stability enhancement. A holistic system-level quantitative analysis is still missing to verify the detailed benefits of GFM inverters and their interactions with other generation units (e.g., synchronous generators [SGs], conventional grid-following [GFL] inverters, synchronous condensers, among others). To fill in the technical gaps, a holistic small-signal model of the entire system with both rotating generation units and IBRs is established along with the customized power flow analysis for varying steady-state operating points. Further, a quantitative method is proposed to identify the impacts of GFM inverters on system performance with an emphasis on a quantifiable stability margin and a complete modal analysis at the system level. Finally, a realistic test system is used to verify the proposed approach.

grid-following↗
DOE OSTI2022

Obfuscation for high-performance computing systems

An example method includes initializing, by an obfuscation computing system, communications with nodes in a distributed computing platform, the nodes including one or more compute nodes and a controller node, and performing at least one of: (a) code-level obfuscation for the distributed computing platform to obfuscate interactions between an external user computing system and the nodes, wherein performing the code-level obfuscation comprises obfuscating data associated with one or more commands provided by the user computing system and sending one or more obfuscated commands to at least one of the nodes in the distributed computing platform; or (b) system-level obfuscation for the distributed computing platform, wherein performing the system-level obfuscation comprises at least one of obfuscating system management tasks that are performed to manage the nodes or obfuscating network traffic data that is exchanged between the nodes.

Powers, Judson↗
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 OSTI2022

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

The electric grid is rapidly evolving as small-scale, demand-side resources play increasingly important roles in grid operations and decarbonization. Maximizing the potential of demand-side resources involves incentivizing electricity customers to use those resources in ways that benefit the broader electrical grid. These incentives depend largely on the electricity cost savings that customers can realize from demand-side resource adoption. Determining these potential cost savings is a complex task. Cost savings depend on numerous factors, including the characteristics of different technologies, the algorithms that control these devices, system performance, customer behavior, electricity rate structures, and climatic factors. Another challenge is that estimated cost savings are frequently based on modeled rather than observed system performance, particularly in the academic literature. In this study, we begin to fill the gap in empirical research of demand-side resources using data from a new construction residential community equipped with rooftop solar and storage (S+S) in Arizona. We use these data to analyze the factors that determine customer electricity cost savings and emissions impacts of S+S in the real world. We then compare these data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, we explore ways to improve such models and, conversely, use modeled results to suggest improvements to actual S+S deployment. The results of these analyses can be summarized in four key findings: 1) rate structures play a central role in the grid and customer value of demand-side resources; 2) certain customers can benefit more from demand-side resource adoption than others; 3) modeled battery dispatch and sizing reveals opportunities for additional cost savings; and 4) optimal dispatches can reduce grid emissions while maximizing bill savings.

14 SOLAR ENERGY↗
DOE OSTI2024

System design and analysis of thermal power dispatch systems for boiling water reactors

Nuclear power plants are crucial to meeting net zero emission goals and achieving energy sustainability. Integrating these plants with clean energy technologies such as high-temperature steam electrolysis (HTSE) may improve the efficiency and economic competitiveness of these plants. Here, the current study investigates the design and operation of a thermal power dispatch (TPD) system for coupling boiling water reactors (BWRs) to HTSE plants. The TPD system extracts a portion of the steam from the reactor’s main steam line and transfers its thermal energy to an HTSE plant through a power transport loop. A TPD system for 5 % steam extraction has been designed and the system performance during steady and transient operations has been analyzed. The TPD system dispatched a total of 197 MW thermal energy to the HTSE plant under nominal design conditions. Saturated steam at 7.17 MPa from the BWR plant was condensed and subcooled to a temperature of 168 °C, while a mass flow rate of 91.1 kg/s of superheated steam was dispatched to the HTSE plant. Furthermore, the system performance during transient operation showed a continuous transition from the initial hot standby mode to the nominal power dispatch level. The transient simulation results emphasized the importance of investigating component level performance for the TPD system design. The current results will guide future works on the development of integrated energy systems for hydrogen production or process heat applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Resource-aware compression

Systems, apparatuses, and methods for implementing a multi-tiered approach to cache compression are disclosed. A cache includes a cache controller, light compressor, and heavy compressor. The decision on which compressor to use for compressing cache lines is made based on certain resource availability such as cache capacity or memory bandwidth. This allows the cache to opportunistically use complex algorithms for compression while limiting the adverse effects of high decompression latency on system performance. To address the above issue, the proposed design takes advantage of the heavy compressors for effectively reducing memory bandwidth in high bandwidth memory (HBM) interfaces as long as they do not sacrifice system performance. Accordingly, the cache combines light and heavy compressors with a decision-making unit to achieve reduced off-chip memory traffic without sacrificing system performance.

97 MATHEMATICS AND COMPUTING↗