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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
DOE OSTI2024

Digital Assurance for High Consequence Systems: 2024 Mission Campaign White Paper

This Sandia National Laboratories Mission Campaign (MC) seeks to create the technical basis that allows national leaders to efficiently assess and manage the digital assurance of high consequence systems. We will call for transformative research that enables efficient (1) development of provably secure systems and secure integration of untrusted products, (2) intelligent threat mitigation, and (3) digital risk-informed engineering trade-offs. Ultimately, this MC will impact multiple national security missions; it will develop an informed Digital Assurance for High Consequence Systems (DAHCS) community and expand Sandia partnerships to build this national capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dispatch Manager for NEML2 Constitutive Model Calculations Embedded in MOOSE

This report describes the extended capabilities of the NEML2 constitutive modeling library, including a flexible and efficient work dispatching system designed to leverage both CPU and GPU resources. This enhancement addresses one of the primary computational challenges in large-scale simulations: the ability to distribute and execute batches of material model evaluations across heterogeneous computing devices. The new dispatch system introduces a modular set of dispatcher and scheduler classes that coordinate the flow of data and execution between devices. The dispatcher is responsible for efficiently packaging work, managing device-specific memory operations, and synchronizing results. This modularity allows for extensibility, making it straightforward to integrate additional computing backends in the future. From an implementation standpoint, the dispatcher system interfaces seamlessly with NEML2's existing models. They handle device-aware tensor operations, optimize memory transfers, and support asynchronous execution when applicable. This design ensures that batches of material points can be evaluated concurrently, substantially improving throughput compared to previous single-device or serial implementations. These improvements not only enhance the raw performance of NEML2 but also improve its usability in multiscale and high-fidelity simulations, where the simultaneous evaluation of large material point batches is critical. Benchmarks included in the report demonstrate the system’s scalability, highlighting its effectiveness when leveraging modern GPU architectures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Energy Efficiency Analysis and Grid Service Results for Commercial Buildings using the VOLTRON IoT Platform

In fiscal year 2021 (FY21), the Commercial Buildings Integration Program (CBI) within the Building Technologies Office (BTO), funded PNNL to conduct a field evaluation of the AIRCx and ILC using an Internet-of-Things (IoT) platform, VOLTTRON. The primary goal of the project is to show that software solutions deployed and delivered through an IoT-platform can identify energy efficiency opportunities and manage peak load (beyond the traditional demand response) in commercial buildings. There are also two secondary goals: 1) show that the energy efficiency solutions will result in identification of significant savings opportunities (10% to 30%) as well as energy cost reductions (10% to 15%) by managing the peak load in commercial buildings, and 2) show that an IoT-based software solution is more cost-effective compared to a manual process in meeting the Re-tuning/retro commissioning (RCx) mandates and there is a pathway for broader adoption of this approach. This report provides AIRCx and ILC results for two building located in District of Columbia managed by the energy service provider Intellimation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Best Practices for Resilience in Smart Grid-Interactive Efficient Buildings

The Federal Energy Management Program (FEMP) supports federal agencies' energy decisions with information and guidance on design, funding, and operations to ensure federal buildings are efficient and resilient. The modernization of building infrastructure and the evolution of buildings to support decarbonization involves complex implementation of multiple components across several systems. This includes energy-efficient equipment, on-site energy generation and storage systems, and control systems. These systems have operational modes that can operate more efficiently if they are able to behave responsively to the conditions of the electrical grid. These grid-interactive efficient buildings (GEB) allow facilities to manage power demand according to operational constraints and market signals issued by grid operators. With proper design and planning, these same capabilities have the potential to enable building and facility resilience - coordinating with microgrids, maintaining power on critical circuits to sustain essential operations, and monitoring building health and safety status during an outage. By managing the load of buildings, GEBs can also reduce the cost of backup generation and make better use of renewable power sources on site. This document outlines some of the processes and considerations to guide the design and operation of GEBs in ways that promote facility resilience.

building decarbonization↗
DOE OSTI2025

Dual-Functional Thermocapacitive Heat Pump with Electrochemical Supercapacitors for Building Thermal Management and Energy Storage

Efficient heating and cooling technologies can help reduce the energy consumption and carbon emissions of buildings. This work explores the use of supercapacitive cells in a multifunctional, liquid-regenerated thermocapacitive heat pump that can provide electrical energy storage in addition to heating and cooling. A proof-of-concept prototype based on eight commercial supercapacitors and using deionized water as a liquid regenerator demonstrated cooling and energy storage capabilities. A peak cooling coefficient of performance (COPc) of 0.27 was achieved at a temperature drop of 0.24 K. The highest measured electrical energy storage density of the cells was 5.93 J cm-3, and the highest cooling power delivered relative to the volume of the cells was 0.58 mW cm-3. This work demonstrates the use of electrochemical energy storage devices in multifunctional equipment for thermal management in buildings.

25 ENERGY STORAGE↗
DOE OSTI2024

Carbonate Management to Enable Energy- and Carbon-Efficient CO 2 Electrolysis (Final Technical Report)

The rapid growth and plummeting cost of solar energy have created great interest in using CO 2 electrolysis to produce chemical feedstocks and fuels such as carbon monoxide, ethylene, ethanol, and propanol. While research in CO 2 electrolysis has yielded substantial progress in both fundamental understanding of the requisite electrocatalytic reactions and design of prototype devices, the energy efficiency (electrical energy to-product) and carbon efficiency (CO 2 -to-product) of CO 2 electrolysis remain far too low for large-scale deployment. A preponderance of evidence indicates that the principal reason for these low efficiencies is the rapid and thermodynamically favorable reaction of CO 2 with hydroxide (OH–) to form carbonate , which forces CO 2 electrolysis cells to operate under conditions that result in large voltage and CO 2 losses. This “CO 3 2 – problem” presents a fundamental scientific barrier to creating a viable electrochemical option for converting solar energy into chemicals and fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗
DOE OSTI2025

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)

This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.

Energy efficiency↗
DOE OSTI2025

Thermal Analysis of a Solid Particle Light-Trapping Planar Cavity Receiver Using Computational Fluid Dynamics

Concentrated solar power (CSP) is one of the most effective ways of harnessing solar power to create efficient, durable, and resilient energy systems. This study entails thermal modeling and analysis of a novel central tower receiver configuration. This receiver uses solid particles as the heat transfer fluid (HTF), a promising option for third-generation CSP systems. The configuration considered here is the light-trapping planar cavity receiver (LTPCR) introduced by the National Renewable Energy Laboratory. While heat transfer studies of various LTPCR subsystems have been done, system-level thermal analysis of the LTPCR receiver has not been attempted. This study also presents important sensitivity analyses of the operating parameters of the CSP system, which can help guide the design of future central tower receivers. This study employs Ansys Fluent as a computational fluid dynamics (CFD) tool to model fluid dynamics and heat transfer in the receiver, intending to quantify its thermal performance. The model seamlessly integrates Monte Carlo ray tracing data, which generates absorbed solar flux profiles from the heliostat field design, with the heat transfer characteristics of the fluidized particle bed. This unified model is designed to accurately predict the thermal behavior of the LTPCR. Analysis of preliminary results reveals that the primary loss mechanisms are radiative and natural convective losses, in that order. Based on observations from a baseline case, several strategies are suggested and numerically tested. These solutions include selective cooling of high-temperature regions and manipulation of particle bed parameters. Selective cooling of high-temperature regions reduced the peak temperature by 151 degrees C and decreased thermal losses by 0.9%. Improving the particle-wall heat transfer coefficient (P-W HTC) of the particle bed decreased the thermal losses by 1.7% and decreased the peak temperatures by 57 degrees C. Decreasing the particle inlet temperature (PIT) also reduced thermal losses by 3.5% and decreased peak temperatures by 29 degrees C. Compounding these strategies improved the thermal losses of the receiver from 13.5% in the baseline case to 7.5%. Additionally, the study explores the variation in thermal performance across different locations of the receiver, where a variation of thermal losses from 12.9% to 17.3% is found. This allows a comprehensive evaluation of potential improvements in efficiency and temperature management.

computational fluid dynamics↗
DOE OSTI2024

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Regulators’ Financial Toolbox: Leveraging Software as a Service, Cloud Computing, and Artificial Intelligence in Electric Utilities

The rapid evolution of Software as a Service (SaaS), cloud computing, and artificial intelligence (AI) is transforming the electric utility industry, reshaping operations, customer engagement, and financial models. This webinar introduced how utilities can deploy advanced software solutions and AI-driven analytics to improve grid efficiency, optimize asset management, and accurately forecast demand.

Bartlett, Phillip↗
DOE OSTI2025

ARCADE Analysis Methods & Validation Pathway

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) provides an automated analysis system which supports risk-informed performance based (RIPB) evaluations of nuclear control systems. Every possible cyber threat which could lead to consequence is identified by simulating the unsafe control action sequences which transform digital harm into physical harm. Eliminating the simulation of complex digital cyber attack chains cuts out unnecessary computational overhead and focuses directly on the physics of cyber-physical attacks. This focus enables designers to make informed decisions which can entirely eliminate categories of cyber threats against advanced reactors through the physical nature of the plant design. This narrowing of cyber threat against nuclear power plants through the physics of the system is intended to make any remaining threat management and cost efficient. This is the goal of the Tiered Cyber Analysis (TCA) outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors. ARCADE has been custom developed to meet the demands of the rigorous analysis required in Tier 1 of the TCA, which forms the foundation of the TCA process. Currently, ARCADE is still under development, but has made significant leaps in capability. A pilot analysis on the opensource Asherah simulator was performed which demonstrated key functionality goals. The next stage of ARCADE development involves improvements to the applications which support the analysis system, and enabling the analysis system to utilize the full suite of unsafe control action simulations. Since the analysis method’s core functions are complete, validation of the analysis method will be started concurrent to the next development stages. The automated analysis ARCADE will provide can radically change the cybersecurity design process for advanced reactors, reducing the cost of security implementation while enhancing cyber resilience. The pathway for ARCADE’s development to this goal has become much clearer. The majority of technical hurdles have been cleared, and the remaining development needs have been solidified. ARCADE is now capable of assisting the advanced reactor design process and directly support advanced reactor industry RIPB practices.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

SCRES Energy Quest Workshop (Technical Report)

Energy Quest: Sitka’s Path to 2050 is a game based public engagement tool developed as part of the Sitka Community Renewable Energy Strategy and the U.S. Department of Energy’s Energy Transitions Initiative Partnership Project Cohort 3. Designed for Sitka’s isolated island microgrid, which is powered primarily by hydropower and depends on imported diesel for backup, the game translated complex long term energy planning questions into an interactive format. In facilitated workshops, residents built energy roadmaps to 2050, explored tradeoffs, and expressed priorities around four themes: affordability, reliability, self sufficiency, and innovation. Across sessions, participants showed strong support for increasing local self sufficiency and resilience, even when this required balancing near term affordability with long term investments. They emphasized reducing dependence on imported diesel, making better use of existing hydropower, and considering new renewable generation such as solar, wind, and additional hydropower. Survey and game responses also highlighted interest in using surplus hydropower to support new industries, expanding electrification of heating and transportation, and exploring emerging technologies and green fuels. The report organizes these findings into four scenario themes to guide Sitka’s 2050 energy planning. Affordability focuses on managing rate impacts, efficiency, and conservation. Reliability addresses diversified generation, backup power, and energy security. Self sufficiency emphasizes distributed generation and reduced reliance on imported fuels. Innovation explores new technologies, marine based energy solutions, and low carbon fuels. By grounding future planning in these community derived themes, Energy Quest shows how a game based approach can make technical energy planning more accessible and inclusive for remote communities. For questions about the game board and piece production email Dr. Sarah Troise (sarah.troise@pnnl.gov)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗