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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Energy Resilient Recovery in Puerto Rico: Hurricane Maria Recovery Support

This report covers microgrids as a special application of distributed energy resources (DERs) in Puerto Rico. This is one of a series of reports describing the U.S. Department of Energy (DOE) multi-laboratory efforts undertaken. To ensure the sustainable, long-term recovery of Puerto Rico's electric power grid from hurricanes Maria and Irma and to build capacity to manage future potential natural disasters in the most secure and resilient way, DOE convened experts from multiple national laboratories to develop a comprehensive set of data, models, analytic tools, and studies, considering inputs from a wide variety of stakeholder groups, to support technically sound recommendations for Puerto Rico's energy investment decisions. In Phase 1 of the multi-laboratory effort to support Puerto Rico's recovery, the National Renewable Energy Laboratory (NREL) provided the utility company Puerto Rico Electric Power Authority (PREPA) recommendations for a new framework of interconnection standards to accelerate the integration of utility-scale, transmission-connected, renewable electrical generation and energy storage that ensure cross-technology compatibility and enable high deployment levels without compromising grid reliability, safety, or security (https://www.nrel.gov/docs/fy19osti/73848.pdf). In Phase 2, NREL published a report focused on the interconnection of distributed energy resources (DERs) to the electric distribution system in Puerto Rico (https://www.nrel.gov/docs/fy21osti/77127.pdf). This report familiarized the reader with Puerto Rico's distribution infrastructure and operational practices and procedures that are relevant to DER interconnection. The report also provided considerations for streamlining the interconnection process given the expected increase in deployments resulting from Puerto Rico's renewable portfolio standard goal of 100% renewables by 2050. Accordingly, the report identifies considerations and concerns associated with the increase in intermittent generation, strategies for DER interconnection best practices, and the potential use of the latest technological solutions identified in the latest revision of the Institute of Electrical and Electronics Engineers 1547-2018 interconnection standard. Additionally, the report identified ways to improve the physical resiliency of installed DERs. This task describes how distributed energy systems may participate in and contribute to improving the resiliency of Puerto Rico's energy infrastructure in the future. This report presents an analysis of the Puerto Rico grid to illustrate the possible different microgrid and minigrid scenarios. The capability to form sustained microgrids and minigrids has the potential to provide a high level of resiliency during disturbances on the grid. DERs, on the other hand, have the capability to aid in the microgrid operation by providing grid support as well as grid-forming functionality. Other tasks in Phase 3 include voltage regulation in distribution networks using DERs and transmission-and-distribution co-simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Analytic Framework for Optimal Sizing of Hydrogen Fueling Stations for Heavy Duty Vehicles at Ports (CRADA 512)

This CRADA presents the strategy that PNNL and Sandia National Laboratories (SNL) will take to support Seattle City Light (SCL) in properly sizing a hydrogen station for class 8 drayage trucks at the Port of Seattle (Port), as part of a coordinated plan of mixed uses for hydrogen. The proper sizing for the fueling station is unique from past experiences because it will be part of a coordinated plan for a multi-use scheme for the hydrogen, and not a stand-alone fueling station. The multiple use-streams, and their relative values, will impact all facets of station sizing and design, including fueling rates, storage technology and size, equipment selection, and safety considerations. The future deployment of this fueling station, and its proper sizing, is seen as a critical first step in the larger vision of utilizing hydrogen to address a range of issues for SCL and the Port. These include, but are not limited to, large scale fueling of medium duty (MD) and heavy duty (HD) vehicles to reduce emissions, support of adjacent light duty (LD) vehicles, support of critical port operation during extreme events (e.g. resiliency), mitigation of capital intensive electrical distribution system upgrades to support evolving port operations, a market resource that can be used by SCL to generate revenue, and support of planned future maritime operations that currently require heavy use of shipboard hydrogen. While the broader use of hydrogen by SCL and the Port is not the focus of this CRADA, how its use impacts the design of the initial vehicle fueling station, and the modular approach, are the central work. As such, this project will develop the framework and process for developing a modular station design that considers the broader range of coordinate uses of hydrogen as an energy resource.

08 HYDROGEN↗
DOE OSTI2024

A techno-economic survey of energy storage media for long-duration energy storage applications

Energy storage technologies that can economically store and provide electricity over multi-day and seasonal timescales are likely to be a critical component of a sustainable and resilient energy system. In this analysis, we perform a broad survey of energy storage technologies to find storage media (SM) that are promising for these long-duration energy storage (LDES) applications. The energy capital cost of the SM is identified as a key figure of merit for LDES. We develop a data collection framework to collect material price and physical property data, which are used to calculate the energy capital cost floor for 376 SM. These costs are then compared with cost targets for the LDES applications. The results show a multitude of thermal and chemical SM are economically promising, including some synthetic fuels, sensible thermal, latent thermal, thermochemical, and batteries based on low-cost materials. In contrast, currently deployed energy storage systems such as lithium-ion batteries and pumped hydro storage do not appear to be promising systems for LDES applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

We report a distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Study of microgrid resilience through co-simulation of power system dynamics and communication systems

The interdependence of power and communication systems in smart grid technologies is acknowledged, but difficult to quantify. Communication systems can be essential to maintaining stability in microgrids that are islanded due to extreme events. Though many power system studies assume the presence of communication networks, detailed modeling of power and communication systems for dynamic studies of microgrids is rare. The work presented in this paper develops a framework for power and communication system co-simulation to study the impact of communications system on microgrid stability. An operational use case is examined where a battery energy storage system operates to offset the loss of generation in an islanded microgrid. The framework is evaluated for different communication technologies, network structures, and communication media.

Thekkumparambath Mana, Priya↗
DOE OSTI2026

Exploring the fusion power plant design space: comparative analysis of positive and negative triangularity tokamaks through optimization

The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance metrics. This study presents a systematic comparison using constrained multi-objective optimization with the integrated FUsion Synthesis Engine (FUSE) framework. Over 200 000 integrated design evaluations were performed exploring the trade-offs between capital cost minimization and operational reliability (maximizing q 95 ) while satisfying engineering constraints including 250 ± 50 MW net electric power, tritium breeding ratio > 1.1, power exhaust limits and an hour flattop time. Both configurations achieve similar cost-performance Pareto fronts through contrasting design philosophies. PT, while demonstrating resilience to pedestal degradation (compensating for up to 40% reduction), are constrained to larger machines (R 0 > 6.5 m) by the narrow operational window between L–H threshold requirements and the research-established power exhaust limit (P sol /R < 15 MW m –1 ). This forces optimization through comparatively reduced magnetic field (∼8 T). NT configurations exploit their freedom from these constraints to access compact, high-field designs (R 0 ~ 5.5 m, B 0 >12 T), creating natural synergy with advancing HTS technology. Sensitivity analyses reveal that PT’s economic viability depends critically on uncertainties in L–H threshold scaling and power handling limits. Notably, a 50% variation in either could eliminate viable designs or enable access to the compact design space. These results suggest configuration selection should be risk-informed: PT offers the lowest-cost path when operational constraints can be confidently predicted, while NT is robust to large variations in constraints and physics uncertainties.

FUSE framework↗
DOE OSTI2023

Quantitative Systems Modeling for Critical Infrastructure Predictions in Climate Change: A National Defense Framework

Any national defense is dependent on the efficacy of the available physical infrastructure. Whatever degrades infrastructure, structurally, physically, architecturally, or esthetically, is of interest to the nation. Climate change is now a major significant factor of interest impinging on national critical infrastructure. The devastating effects of climate change have increasing pervasiveness throughout the world. The impact on critical infrastructure is of particular interest to researchers. In consonance with ASCE-ASME's special issue on critical infrastructure protection and resilience, this paper presents a systems-modeling approach for critical infrastructure and predictions in relation to climate change agreements in COP26. The paper addresses high-level critical systems-based assessment of the social, legal, economic, and technical nuances impinging on the viability of COP26 agreements. The paper was written using a multinational collaboration approach. In conclusion, the specific focus of national defense is used as the backdrop for the methodology of the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine learning-enabled model-data integration for predicting subsurface water storage

Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change.

Lu, Dan↗
DOE OSTI2023

Local-Scale Framework for Techno-Economic Analysis of Subsurface Hydrogen Storage

The energy sector is evolving toward increased reliance on renewable energy technologies to meet state, national, and organization decarbonization goals. This trend is creating challenges and opportunities with meeting current and future energy demand under the variable supply conditions that most renewables provide. Hydrogen (H 2 ) is a promising energy carrier that may meet the need for both on-demand and long-duration storage to maintain energy security and resilience. Underground hydrogen storage (UHS) is a method of storing H 2 in subsurface geological systems, such as depleted hydrocarbon reservoirs, salt caverns, saline aquifers, hard rock, and other engineered systems. UHS has the potential to store large quantities of H 2 over time, providing a reliable source of energy while minimizing surface footprints at a lower investment cost compared to surface storage. Earlier work estimated that, if converted and retrofitted, existing underground natural gas storage (UGS) facilities in the U.S. can store approximately 327 TWh, or 9.8 million metric tons, of pure H 2 . However, a shift to pure H 2 would decrease the collective working-gas energy of the UGS facilities by approximately 75% due to physical and chemical differences between natural gas and hydrogen. The same work also suggests that almost 75% of the existing UGS facilities in the U.S. could maintain current energy demand buffering using a blend of only 20% H 2 to 80% natural gas, by volume at surface conditions. If we can take advantage of the suite of mature technologies of existing UGS facilities and natural gas utility systems to accelerate the transition to a hydrogen economy in the U.S., a 20% H 2 blend could lead to a 6-7% reduction of greenhouse gas emissions for energy delivered through natural gas utility systems.

03 NATURAL GAS↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗
DOE OSTI2024

A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events

Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel policy-guided tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, in this study, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Plasma confinement state classification via FPP relevant microwave diagnostics

We present a parsimonious and robust machine learning approach for identifying plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement and high-confinement regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained set of diagnostics. To address this challenge, we demonstrate that a minimalist model, using only electron cyclotron emission (ECE) signals, can achieve accurate and reliable state classification. ECE provides electron temperature profiles without the engineering or survivability issues of in-vessel probes, making it a primary candidate for FPP-relevant diagnostics. Our framework employs ECE as input, extracts features using radial basis functions, and applies a gradient boosting classifier, achieving a test accuracy of 96% (correct predictions). Robustness analysis and feature importance analyzes confirm the approach’s reliability. These results demonstrate that state-of-the-art performance is attainable from a restricted diagnostic set, paving the way for minimalist yet resilient plasma control architectures for FPPs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A multi-algorithm approach for modeling coastal wetland eco-geomorphology

Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change makes it more difficult for these ecosystems to adapt to the fluctuation in sea levels and other environmental changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. However, their divergent estimates of the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetlands (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model–data comparison, we showed the importance of using a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also found that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites in diverse environments can help reduce the model uncertainty.

58 GEOSCIENCES↗
DOE OSTI2026

DEMO-FTES: Development, Monitoring, and Control of Fracture Thermal Energy Storage in Crystalline Rock Formations (CRADA Final Report)

The DEMO-FTES project investigated the feasibility of Fracture Thermal Energy Storage (FTES) as a seasonal energy storage solution in crystalline rock formations. FTES leverages hydraulically induced fractures to exchange heat between circulating fluids and the surrounding rock mass, enabling long-term thermal energy retention due to the high specific heat and low thermal conductivity of rock. This approach has the potential to reduce heating and cooling energy demands and enhance building energy resilience. The project combined dimensional analysis, numerical modeling, laboratory experiments, and meso-scale field tests to evaluate FTES performance and advance its technology readiness level from 3 to 5. Scaling analysis identified key dimensionless parameters governing heat transfer and fluid flow, ensuring laboratory and field tests were representative of larger-scale systems. Numerical simulations using TOUGH and iTOUGH2 frameworks supported experiment design and interpretation, modeling fracture geometry, thermal-hydraulic behavior, and thermo-mechanical coupling. Laboratory tests at EPFL involved creating single and multiple fractures in 25 cm cubic samples of Gabbro and Granite under true triaxial stress.

25 ENERGY STORAGE↗
DOE OSTI2023

Distributed Energy Resource Visual Emulator: Phase 1

To help federal energy managers assess, monitor, and manage cybersecurity while achieving decarbonization, the National Renewable Energy Laboratory's Distributed Energy Resource Cybersecurity Framework (DER-CF) offers a comprehensive, web-based assessment tool focusing on cyber governance or policies, technical management, and physical security. The DER-CF currently presents users with a series of pertinent cybersecurity questions, which are used to generate a site-specific report and recommendations. This paper outlines a technical approach to integrate the DER-CF with another key asset—NREL's Advanced Research on Integrated Energy Systems (ARIES) Cyber Range—to visualize cybersecurity resilience and compliance and to enhance the usability and accessibility of the DER-CF. The result is a new tool called the Distributed Energy Resource Visual Emulator (DER-VE). Its development will include regular conversations with stakeholders to assess the effectiveness of these efforts, refine the visualization capability, and ensure its value to our partners. Phase 0 of the integration project was concluded in 2021. Phase 1, completed in 2022, has two components: The first is developing a working visualization of system compliance using the DER-CF, and the second is planning the design of a server application that takes input data from the DER-CF and creates a personal emulated environment of the user's system or a selected reference architect. Major components that were addressed in this phase are the DER-CF output, compliance visualization, data model, and compliance server design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Energy Sector Position, Navigation, and Time Cybersecurity Profile

The Pacific Northwest National Laboratory has developed this positioning, navigation, and timing (PNT) profile for the Energy Sector at the request of the U.S. Department of Energy. The Department of Energy is the sector-specific agency for the Energy Sector and is responsible for providing guidance in the form of this PNT Profile, per Executive Order 13905, Strengthening National Resilience Through Responsible Use of Positioning, Navigation, and Timing Services (Executive Office of the President 2020) issued February 18, 2020. Executive Order 13905 directed the National Institute of Standards & Technology to develop a PNT Profile that is broadly applicable to all sectors and to serve as a foundation for sector-neutral guidance, NISTIR 8323 Foundational PNT Profile: Applying the Cybersecurity Framework for the Responsible Use of Positioning, Navigation, and Timing (PNT) Services. This document builds on NISTIR 8323 to identify and serve as the required Energy Sector specific guidance. For purposes of this PNT Profile, Energy Sector precise time is 0.5 µs (microseconds) to 1 µs accuracy. Currently, the electricity subsector has systems that use this level of precision time, but the oil and natural gas subsector currently does not. While the electricity subsector uses precision timing, it does not use positioning and/or navigation at this level of accuracy. For this reason, this PNT Profile will focus on only precise time for the electricity subsector.

24 POWER TRANSMISSION AND DISTRIBUTION↗