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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. This presentation will highlight several of our findings. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.

community energy planning↗
DOE OSTI2025

An analytically tractable marked power spectrum

The increasing precision of cosmology data in the modern era is calling for methods to allow the extraction of non-Gaussian information using tools beyond two-point statistics. The marked power spectrum has the potential to extract beyond two-point information in a computationally efficient way while using much of the infrastructure already available for the power spectrum. In this work we explore the marked power spectrum from an analytical perspective. In particular, we explore a low-order polynomial for the mark that allows us to better control the theoretical uncertainties and we show that with minimal new degrees of freedom the analytical results match measurements from N-body simulations for both the matter field and biased tracers in redshift space. Finally, we show that even within the limited forms of mark that we consider, there are degeneracies that can be broken by inclusion of the marked auto-spectrum or the cross-spectrum with the unmarked field. I n conclusion, we discuss future theoretical developments that would enable us to apply this approach to survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Distribution System Planning for Growth in Residential Electric Vehicle Adoption

Anticipated growth in Electric Vehicles (EV) adoption could stress distribution system circuits beyond their original design limits. Uncertainties related to when households will begin buying EVs in large numbers challenges existing distribution system planning approaches and complicates efforts to assess grid impacts. Anticipated location, timing, and demand for EV charging is critical information for distribution system planners as it guides investment strategies for infrastructure upgrades to ensure continued reliability. This research makes two significant contributions: an EV adoption model that uses socioeconomic data to forecast location and year-specific adoption patterns through a bottoms-up approach, and an EV hosting capacity assessment methodology that offers improvements to current utility planning and asset management practices for infrastructure investments. Both contributions are applied to a Southern California Edison feeder in 7 adoption years from 2025 to 2050, with the results indicating that they are likely valuable additions to distribution systems planning capabilities.

Sridhar, Siddharth↗
DOE OSTI2020

Collaboration with USACyS for Signals Manipulation

Savannah River National Laboratory has been investing into cybersecurity capabilities since 2019. Cybersecurity has been a rapidly growing area for the CSRA in general and has included the consolidation of Cyber and Electronic Warfare units at Fort Gordon. This consolidation has been driven by the convergence of cyberwarfare and electronic warfare as the technologies underpinning Army Cyber Command’s vision of Information Warfare. This convergence, investment by SRNL into cybersecurity for critical infrastructure, and the desire for collaboration between the Army and SRNL has resulted in an opportunity for SRNL to develop R&D capabilities in electronic warfare that will complement SRNL’s cybersecurity efforts. Discussions between SRNL and the U.S. Army Cyber School (USACyS) has identified a general gap in ICS training lab capability in which both entities could contribute to solve. It is proposed to design and architecture that meets this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Potential Availability of Alternative Fuel to Supply Maritime Activities in Pacific Northwest Ports

The international shipping sector represented 3% of global greenhouse gas emissions in 2023 (Office of Energy Efficiency & Renewable Energy 2024). International shipping has been classified as a difficult-to-decarbonize industry (IRENA 2024). In an effort to drive decarbonization, the U.S. Department of Energy has partnered with Mission Innovation to co-lead the Zero-Emission Shipping Mission, which launched in 2021(Office of Energy Efficiency & Renewable Energy 2021). In addition, the U.S. Department of State partnered with Norway to launch the Green Shipping Challenge in 2022 (Office of the Spokesperson 2022b). As part of the ZESM and Green Shipping Challenge, the United States is collaborating with the Republic of Korea (ROK) to develop a green shipping corridor (U.S. Mission Korea 2023). The United States and ROK have conducted a pre-feasibility study as the first step in developing a green shipping corridor between the countries. The ports included in the study are Seattle, Tacoma, and Everette in the U.S. Pacific Northwest (PNW) and Busan, Ulsan, and Masan in ROK. The National Renewable Energy Laboratory's role in the study was to analyze the availability and technical potential of alternative marine fuels in proximity to U.S. PNW ports. The findings show most of the existing alternative fuel capacity within the region is from renewable diesel, biodiesel, and sustainable aviation fuel facilities. The largest growth in fuel capacity in the region by 2030 is projected to be in renewable diesel and hydrogen. The overall technical readiness of non-drop-in alternative fuel production and conversion technologies is more developed than alternative-fueled ships and associated fueling infrastructure. However, much of the fuel capacity in the region is comprised of drop-in fuels, making it technically possible to use existing infrastructure for transporting and bunkering to the existing fleet. Data to inform regional alternative fuel quantity estimations were collected from an extensive review of databases, reports, announcements, and other publicly available resources. A maturity index and sector competition factor were applied to announced fuel projects to determine the quantity of alternative fuel available to the marine sector in the region by 2030. Demand data were collected from fuel bunkering logs covering the PNW seaports (State of Washington 2021). Both supply and demand data were converted to very-low sulfur fuel oil gallon equivalents (VLSFO-GE) for better comparison. Qualitative data were gathered through interviews with stakeholders, project developers, and industry experts. Total alternative fuel capacity available to the marine sector in the region is estimated to be 824 million VLSFO-GE per year by 2030. This is sufficient to cover the requirements of a green shipping corridor between the United States and ROK. The findings from this report are being used to inform detailed feasibility studies for several U.S. PNW -ROK green shipping corridors. Updates from the U.S. PNW - ROK feasibility studies will continue to be published on Mission Innovation's green corridor tracking website (Zero Emission Shipping Mission, n.d.). In addition, this report has helped to inform further work on shipping decarbonization in the U.S. PNW, including the Pacific Northwest to Alaska Green Corridor focused on cruise vessels.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Timeouts Best Practices

This study investigates common timeouts encountered in the electric vehicle (EV) charging communications process. Many different timeouts are defined within the EV charging communications protocols. These timeouts can either be a fixed value or a defined range of values. In both cases, the timeout defines the duration of time for which one or both parties in the communications session are expected to wait for some action or process to complete before terminating the charge attempt. These timeout-based terminations are intended to prevent the charging process from becoming stuck indefinitely in any particular step. These terminations also enable a retry of the terminated charging session to begin. However, misaligned timeout values can have a significant negative impact on the user experience. Premature termination of charging sessions due to inappropriate timeout settings can lead to charging failures, causing inconvenience, wasted time, and frustration for users. These disruptions can degrade the overall user experience, making it essential to carefully manage and align timeout values with the relevant actions and processes to ensure reliable and satisfactory EV charging sessions. The core objective of this study is to boost reliability and enhance user experience by conducting a thorough review of timeout-based issues in EV charging and delivering a set of recommendations to modify these existing timeouts. These recommendations are informed by feedback gathered from multiple EV charging partners. This document is intended to inform electric vehicle supply equipment (EVSE) and EV manufacturers, EV charging infrastructure developers, and policymakers responsible for designing and implementing EV charging protocols and systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Data Format and Descriptions for the Alabama Carbon Storage: Data Sharing and Engagement Project

The Alabama Carbon Storage: Data Sharing and Engagement (ACS-DSE) project seeks to develop publicly accessible geologic carbon storage models and data across the southern Gulf Coastal Plain of Alabama. The public online platform developed for this project will include geologic, geophysical, infrastructure, and other relevant datasets and geologic models of the study area. Datasets, model surfaces (e.g. structural contour maps, isolith maps, porosity maps), and infrastructure data (e.g. offshore pipelines, field boundaries) will be downloadable in commonly used file formats. The anticipated primary geologic datasets are well headers, formation tops, average reservoir properties, and core analyses; these will be available as commaseparated values (CSV) text files and MS Excel workbooks. Geophysical logs will be available in Log ASCII Standard (LAS) file format. Modeled surfaces, such as structure contour maps, will be available in ArcGIS formats and text files. Infrastructure data will be available as ArcGIS shapefiles. This document provides information on the data sources and attributes of the datasets.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment

The Coupled Model Intercomparison Project (CMIP) coordinates community-based efforts to answer key and timely climate science questions, facilitate delivery of relevant multi-model simulations through shared infrastructure, and support national and international climate assessments. Generations of CMIP have evolved through extensive community engagement from punctuated phasing into more continuous support for the design of experimental protocols, infrastructure for data publication and access, and public delivery of climate information. We identify four fundamental research questions motivating a seventh phase of coupled model intercomparison relating to patterns of sea surface temperature change, changing weather, the water–carbon–climate nexus, and tipping points. Key CMIP7 advances include an expansion of baseline experiments, a focus on CO 2 -emissions-driven experiments, sustained support for community MIPs, periodic updating of historical forcings and diagnostics requests, and a collection of prioritized experiments, or the “Assessment Fast Track”, drawn from community MIPs to support climate research, assessment, and service goals across prediction and projection, characterization, attribution, and process understanding.

Environmental sciences↗
DOE OSTI2021

Initialized Earth system prediction from subseasonal to decadal timescales

Initialized Earth system predictions are made by starting a numerical prediction model in a state as consistent as possible to observations, and running it forward in time for up to ten years. Skillful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, from agriculture to water resource management, and human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skillful predictions can be made: at S2S timescales, there has been some skill in predicting the Madden-Julian Oscillation and North Atlantic Oscillation; at S2I in predicting the El Niño-Southern Oscillation; and at S2D, in predicting variability in North Atlantic sea surface temperatures. However, challenges remain, and future work must prioritise reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could increase predictive skill and, in turn, confidence. As numerical models progress towards Earth system models, initialized predictions are expanding to include prediction of sea-ice, air pollution, terrestrial and ocean biochemistry which can bring clear benefit to society and various stakeholders.

climate prediction↗
DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

Traditional hazards analysis (HA) methods, originally developed to address physical and operational risks, often fall short when it comes to identifying and mitigating cyber threats. These cyber threats pose unique and evolving risks to critical infrastructure and industrial control systems (ICS). This report explores the integration of Cyber-Informed Engineering (CIE) principles into existing HA methods to enhance their ability to address cyber-induced risks. CIE provides organizations with a practical, cost-effective approach to closing the gap between traditional HA methods and the need for cyber risk mitigation. By leveraging existing safety processes and controls, CIE allows users to examine and mitigate cyber vulnerabilities without overhauling existing HA methods. This report identifies areas where HA and CIE naturally align and where their approaches diverge. It emphasizes how CIE principles can be used to adapt HA methods, broadening their scope to include cyber risks and enabling the mitigation of cyber- induced impacts alongside traditional hazards and failure scenarios. This report examines how CIE can be applied across various HA methods—such as Hazard and Operability Studies (HAZOP), Probabilistic Risk Assessment (PRA), Failure Modes and Effects Analysis (FMEA), Systems-Theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), and Layers of Protection Analysis (LOPA). It provides strategies for integrating CIE to strengthen the identification, assessment, and mitigation of cyber-induced risks. The findings offer a structured entry point for organizations to embed CIE concepts into hazards and safety analyses, as well as broader engineering processes, ultimately supporting the design and operation of a more resilient infrastructure.

42 ENGINEERING↗
DOE OSTI2026

Hardware Aware Mitigation of Timing Side-Channel Vulnerabilities in Critical Infrastructure Software

Program runtime/timing attacks exploit variations in a program’s execution times to extract sensitive information from the program (e.g. encryption keys, sensitive variable data, intellectual property). State-of-the-art solutions to runtime sidechannel attacks attempt to balance the execution time of the sensitive code for different control flow paths to eliminate the timing leakage. However, during the mitigation process, most techniques do not consider the underlying hardware/device on which the target program is supposed to run on. This can lead to over-fixing (unnecessary extra operations), under-fixing (not solving the imbalance properly), and even failures. We propose DISARM, a joint hardware-software methodology (unlike any existing solution) for mitigating runtime side-channel vulnerabilities that utilizes timing values from real embedded devices to generate targeted software fixes. We implement DISARM to support C/C++/Java source codes and validate it across 22 standard benchmarks. DISARM outperforms state-of-the-art solutions such as PENDULUM and DifFuzzAR in terms of execution time overhead (up to −46%), code size overhead (up to −10%), and correctness (no failures) on five different embedded/edge devices.

Suha, Tasneem [University of Maine]↗
DOE OSTI2025

Cyber-Informed Engineering Briefing for ABET

Cyber-Informed Engineering (CIE) is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system, energy or otherwise, to mitigate or even eliminate avenues for cyber-enabled attacks.?CIE concepts use design decisions and engineering controls to prioritize defense against the worst possible consequences of cyberattacks facing critical infrastructure systems and asset owners. These slides offer a deep dive into Cyber-Informed Engineering for engineering educators.

42 - ENGINEERING↗
DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

Traditional hazards analysis (HA) methods, originally developed to address physical and operational risks, often fall short when it comes to identifying and mitigating cyber threats. These cyber threats pose unique and evolving risks to critical infrastructure and industrial control systems (ICS). This report explores the integration of Cyber-Informed Engineering (CIE) principles into existing HA methods to enhance their ability to address cyber-induced risks. CIE provides organizations with a practical, cost-effective approach to closing the gap between traditional HA methods and the need for cyber risk mitigation. By leveraging existing safety processes and controls, CIE allows users to examine and mitigate cyber vulnerabilities without overhauling existing HA methods. This report identifies areas where HA and CIE naturally align and where their approaches diverge. It emphasizes how CIE principles can be used to adapt HA methods, broadening their scope to include cyber risks and enabling the mitigation of cyber- induced impacts alongside traditional hazards and failure scenarios. This report examines how CIE can be applied across various HA methods—such as Hazard and Operability Studies (HAZOP), Probabilistic Risk Assessment (PRA), Failure Modes and Effects Analysis (FMEA), Systems-Theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), and Layers of Protection Analysis (LOPA). It provides strategies for integrating CIE to strengthen the identification, assessment, and mitigation of cyber-induced risks. The findings offer a structured entry point for organizations to embed CIE concepts into hazards and safety analyses, as well as broader engineering processes, ultimately supporting the design and operation of a more resilient infrastructure.

42 - ENGINEERING↗
DOE OSTI2025

Resource Recovery for the Wastewater Industry

This information sheet discusses the technology pillar, Resource Recovery, as a pathway toward improving wastewater infrastructure sustainability and resiliency. To supplement existing literature on current technologies and policies for improving resiliency at wastewater (WW) treatment plants, this document aims to accomplish the following: • Summarize wastewater sludge recovery methods • Summarize biogas production and codigestion methods • Serve as a comprehensive (though not exhaustive) repository for resource recovery for wastewater utilities The Resource Recovery Technical Information Sheet should be viewed as a general guide to established best practices for the water and wastewater (W/WW) sector when considering implementing energy capture technologies. Additional details on associated energy capture avenues such as combined heat and power (CHP), renewable energy, and inline hydropower from tertiary effluent in W/WW facilities are presented in the Energy Capture Technology Information Sheet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗
DOE OSTI2020

2020 Status Report - Research Reactor Infrastructure Program

This presentation will discuss the purpose and scope of the Department of Energy - Research Reactor Infrastructure (RRI) Program. Personnel involved in the program will be introduced and contact information will be provided for team member. Information will be provided to conference attendees as to the status of the core activities of the program. These activities include fresh fuel element fabrication and spent nuclear fuel shipment returns to the DOE. Current and future issues pertinent to the RRI program will also be presented. The RRI program maintains fuels support contracts and provides nuclear reactor fuel at no or low cost to 24 U.S. universities operating a total of 25 reactor facilities. These facilities include: - Twelve TRIGA facilities - Eight plate fueled facilities - Three AGN facilities - One Pulstar fueled facility - One Critical facility The title for the fuel remains with the United States government and when the universities are finished with the fuel, the fuel is returned to the United States government for long-term storage. Mission of the Research Reactor Infrastructure Program: The Research Reactor Infrastructure Program is funded by the U.S. Department of Energy, Office of Nuclear Energy and is managed by the Idaho National Laboratory (INL) in Idaho Falls, Idaho. The program goals are: - Keep all U.S. operating university reactor programs supplied with nuclear fuel. - Provide assistance for movement of irradiated nuclear fuel from U.S. universities, after the DOE receipt facility authorizes the fuel receipt.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗