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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Energy Technology Innovation Partnership Project Strategic Energy Plan for Washington County, Maine

This Strategic Energy Plan (SEP) for Washington County, Maine was developed through the U.S. Department of Energy's Energy Technology Innovation Partnership Project (ETIPP) to address local challenges related to electricity reliability, affordability, and safety. Grounded in a "relational infrastructure" approach, the plan recognizes that improved energy outcomes depend not only on physical grid investments but also on strengthened coordination and trust between utilities and communities. Using utility-provided reliability data, public datasets, and community input, the plan identifies key risks, including vegetation-related outages, high energy burden, and vulnerability among elderly and medically dependent populations. The SEP establishes three priority areas: hazard mitigation for energy infrastructure, outage safety and emergency readiness, and energy assistance and thermal safety. It outlines community-driven actions designed to complement utility-led system improvements, including targeted outreach, municipal coordination, and expanded access to energy programs. The plan also situates local conditions within broader state policy, market dynamics, and funding opportunities. Intended as a living document, this SEP provides a scalable framework for collaborative action to improve resilience, safety, and affordability in rural energy systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Risk-Informed Applications and Science – Past, Present, & Future

This is a presentation to be made at the University of Tokyo. The Idaho National Laboratory is creating the next-generation reliability- and risk-assessment methods and tools that support risk-informed decision-making by combining physics-based models with probabilistic quantification approaches. Integrating these two worlds of physics and probability using a simulation framework leads us to predictions based upon an approach called “computational risk assessment” which will serve as the technical basis for the future of reliability and risk approaches. The driving factors for this new approach includes: temporal (timing issues), spatial (location issues), mechanistic (physics issues), and topological (complexity issues). By combining phenomenology directly with stochastic quantification, we are able to perform advanced uncertainty analysis directly on both parameters and models. In addition, new tools allow for the creation of reduced order models that mimic high-fidelity engineering models while still permitting the realization of thousands of simulation iterations in short run-times on current workstation-class computers. While these advanced methods and tools can provide increased realism in our engineering safety and risk approaches, their greater benefit is to provide a risk-informed engineering framework for design and operation. This talk will briefly review the history of risk assessment and risk-informed applications, describe some of the current research and development found at the Idaho National Laboratory, and discuss potential future applications and approaches for advanced methods and tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Emergency Diesel Generator Backup Power Systems for Military Bases

Backup power systems on military installations must provide reliable power during a grid outage. The risks of blackouts and loss of electric power are not new. Outages of just a few hours are well known, but longer duration outages are becoming more frequent. The U.S. Army, Navy, and Air Force now require backup power from one to two weeks. For multiday outages, the reliability of emergency diesel generators will have a significant impact on the installation’s backup power system’s ability to provide power for critical missions.

backup power↗
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Thermal and Mechanical Design of a High-Voltage Power Electronics Package: Preprint

Thermal and mechanical design aspects of a power electronic package with 5-kV GaN devices are presented. Through finite element analyses, the impact of different cooling configurations and device location on the thermal performance and reliability of the package was investigated. It was found that placing the devices closer to the direct-bond-copper substrate as opposed to a centered approach in the proposed double-sided-cooling configuration resulted in improved heat dissipation. This approach also reduced the total number of attachment layers, thereby likely improving the reliability of the package. Furthermore, simulations revealed that the device location had a negligible impact on the thermomechanical behavior of the attachment layers, as they are more prone to the local coefficient of thermal expansion mismatch.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Evaluation of Low-Pressure-Sintered Multi-Layer Substrates for Medium-Voltage SiC Power Modules: Preprint

Direct-bonded aluminum (DBA) multi-layer substrates have been fabricated using low-pressure silver sintering. These multi-layer substrates can be used to reduce the peak electric field strength inside power modules. This benefit is particularly important for medium-voltage silicon carbide (SiC) MOSFETs due to their higher operating voltages. The voiding content and defect density of the bond used to create the multi-layer substrate stackup is critical to the thermal performance and reliability of the power module. This work two low-pressure-assisted sintering techniques; one using nano-silver preform and the other using nano-silver paste to bond 23 mm by 49 mm DBA substrates. Scanning acoustic microscopy (C-SAM) is used to evaluate the bond quality after sintering. To evaluate the reliability of the sintered multi-layer substrates, passive thermal cycling from –40 °C to 200 °C was performed. Cross-sections were cut at pre-determined intervals and imaged with Scanning Electron Microscopy (SEM). After 1,000 thermal cycles, minor cracking was observed, but no failures have occurred.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2021

PV Evolution in the Light of Circular Economy: Preprint

An ideal circular economy has no waste, end-of-life resources become new goods, and all production loops are closed. For the photovoltaic industry’s growing waste problem, quantifying the value of lifetime, reliability, repair, reuse, recycling and other options to increase circularity can help identify and prioritize research needs. In this work we capture the evolution of PV module technology, such as improved reliability and increasing market share of glass/glass modules. We then study the material and energy flows for PV production and use, and identify the circular processes and pathways yielding the most impact. We discuss material circularity and energy return on investment as quantitative metrics to define and evaluate circularity.

bifacial↗
DOE OSTI2023

Wind Plant Operations and Maintenance Challenges and Research Opportunities

Global wind industry has experienced tremendous growth during the past two decades and the trend does not appear changing in near future. However, the industry is still challenged by premature component failures and high operations & maintenance (O&M) costs, which can account for up to 35% of levelized cost of energy. It is imperative for the industry to improve performance, reliability and reduce O&M costs through advanced technologies, enabled by research in related disciplines, to be competitive. This talk will first briefly discuss the challenges with wind plant O&M, then give an overview of related NREL research in the areas of performance, reliability, and O&M cost modeling, finally touch on future R&D opportunities in related areas. The authors hope some of these challenges are of interested to and can be addressed by the INFORMS community in future.

costs↗
DOE OSTI2022

It's Not Just About the Megawatts - ARC Industry Forum, The Future of Power Generation

As the world transitions to less carbon intensive energy portfolios, much of the attention understandably centers on generating capacity – replacing megawatts of fossil fuel with megawatts of solar, wind, nuclear, hydroelectric, hydrogen, and more. The actual operation of a reliable grid needs more than just megawatts though: adequate quantities of a portfolio of essential reliability services; flexible and capable power delivery systems; governance to operate in 5-10 minute intervals, forecast hourly, and plan and build on a generational time horizon; and dependable and secure communications. We can choose our fuel mix for climate reasons, but the grid itself and the society that relies upon it impose constraints on those choices that we ignore at our peril.

13 HYDRO ENERGY↗
DOE OSTI2023

Hydrogen Equipment Enclosure Risk Reduction through Earlier Detection of Component Failures

Hydrogen component reliability and the hazard associated with failure rates is a critical area of research for the successful implementation and growth of hydrogen technology across the globe. The research team has partnered to quantify system risk reduction through earlier detection of hydrogen component failures. A model of hydrogen dispersion in a hydrogen equipment enclosure has been developed utilizing experimentally quantified hydrogen component leak rates as inputs. This model provides insight into the impact of hydrogen safety sensors and ventilation on the flammable mass within a hydrogen equipment enclosure. This model also demonstrates the change in safety sensor response time due to detector placement under various leak scenarios. The team looks to improve overall hydrogen system safety through an improved understanding of hydrogen component reliability and risk mitigation methods. This collaboration fits under the work program of IEA Hydrogen Task 43 Subtask E Hydrogen System Safety.

CFD↗
DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A Knowledge Graph Approach to Analyze Systems and Assets Health

Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rancor-HUNTER: Using a Simulator Engine for Realistic Human Performance Modeling of Nuclear Power Operations

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a software system to simulate human performance in support of human reliability analysis (HRA) in nuclear power plants. This paper summarizes recent work to integrate HUNTER with a plant simulator, namely the Rancor Microworld Simulator. Rancor is an offshoot of earlier work at Idaho National Laboratory (INL) to support plant modernization. The graphical software tools used to mimic digital human-system interface upgrades at INL’s Human Systems Simulation Laboratory were linked to the Rancor Microworld Simulator, an INL-developed simplified plant model. HUNTER becomes a “virtual operator” coupled to the Rancor simulator, thereby allowing a tight coupling between a digital human twin and a digital twin of the plant. Rancor-HUNTER may be run through Monte Carlo iterations across a dynamic range of performance shaping factors, thereby producing distributions of human performance in terms of procedure paths, errors instantiations, and task durations. This paper overviews the various unique features of Rancor-HUNTER and presents an example run of Rancor-HUNTER for a startup scenario.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization

The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Chapter 6: Thermoelectric Energy Harvesters and Applications

With recent progress in the field of wearable and bio-integrated devices, which are widely used for various applications, such as real-time health monitoring, point-of-care diagnostics, and biological actuators, the need for a continuous and reliable power source is increasing. As solid-state devices with no moving parts, thermoelectric energy generators provide a reliable solution for harnessing body heat and converting it into electricity on demand. However, traditional thermoelectric generators, comprising inorganic materials, are rigid and bulky, limiting their wide deployment. Nonconventional thermoelectric generators, composed of organic and hybrid thermoelectric materials, are flexible and lightweight. In this chapter, we will discuss the working principle of thermoelectric energy harvesting, including materials, devices, and applications. While we briefly touch upon the inorganic materials, we will focus primarily on the organic and hybrid materials, which are nontoxic, flexible, easy to manufacture, easy to scale, and readily available at low cost, providing a promising solution for lightweight and conformal thermoelectric power generation.

body heat harvesting↗
DOE OSTI2020

Low Current and Analog Memristor with Ru as Mobile Species

The switching parameters and device performance of memristors are predominately determined by their mobile species and matrix materials. Devices with oxygen or oxygen vacancies as the mobile species usually exhibit a great retention but also need a relatively high switching current (e.g., >30 mu A), while devices with Ag or Cu as cation mobile species do not require a high switching current but usually show a poor retention. Here, Ru is studied as a new type of mobile species for memristors to achieve low switching current, fast speed, good reliability, scalability, and analog switching property simultaneously. An electrochemical metallization-like memristor with a stack of Pt/Ta2O5/Ru is developed. Migration of Ru ions is revealed by energy-dispersive X-ray spectroscopy mapping and in situ transmission electron microscopy within a sub-10 nm active device area before and after switching. The results open up a new avenue to engineer memristors for desired properties.

analog switching↗
DOE OSTI2023

Fully Inkjet‐Printed, 2D Materials‐Based Field‐Effect Transistor for Water Sensing

Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Interatomic Potential Model Development: Finite-Temperature Dynamics Machine Learning

Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. Here, for developing an unbiased potential, a finite-temperature dynamics machine learning (FTD-ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD-ML exhibits three distinguished features: 1) FTD-ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD-ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first-principles data; 3) FTD-ML is much more computationally cost effective than first-principles simulations, especially when the system size increases over 10 3 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD-ML approach exhibits good performance for general simulation purposes. Thus, the FTD-ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental-level accuracy.

42 ENGINEERING↗