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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data mining of plug-in electric vehicles charging behavior using supply-side data

This paper aims to better understand the charging patterns of plug-in electric vehicles (PEVs) and identify factors that may significantly impact PEVs’ charging behavior. We collected 189,864 supply-side charging session data over 13 months from 821 charging stations in Illinois from ChargePoint. Through descriptive and regression analyses, we characterize the distributions of key charging behavior indicators, including charging location, dwell time, and battery start state of charge (SOC), and quantify the impacts of closely related factors on these charging behaviors. In this work, we find that: (1) PEVs are more likely to charge in the morning at multifamily commercial locations with a lower start SOC compared with single family residential locations; (2) Weekday and morning sessions are more likely to utilize workplace charging and have shorter dwell time compared with weekend and afternoon sessions; (3) Single family residential area and locations with Levels 1/2 chargers have a higher start SOC and longer dwell time compared with other locations and DC fast chargers (DCFCs). These findings provide policy insights to identify potential time and locations to incentivize PEVs for grid services, as well as identify critical location categories for further charging infrastructure investment to better reduce range anxiety and promote PEV adoption.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Assessing electrification readiness in U.S. single-family homes based on a nationwide survey of electrical panel capacities

Electrification of residential buildings is a key strategy for increasing the use of renewable energy sources. Central to this transition is understanding the capacity of existing electrical infrastructure—specifically electrical panels—to safely and effectively manage increased electricity demands from electrification technologies. However, comprehensive nationwide data on electrical panel capacities in U.S. single-family homes is currently lacking. To address this gap, we conducted a nationwide survey of single-family homes, collecting detailed data on electrical panel capacities, breaker slot availability, major electric and gas appliances, electrical panel models, and home characteristics such as construction year and floor area. Photographic documentation was used to verify electrical panel data and appliance information. Results show that approximately 60% of surveyed homes have electrical panels rated at ≥200 amperes (A), indicating that a significant portion of the existing housing stock can accommodate additional electric loads. However, 31% of homes possess panels rated at ≤100 A, potentially restricting their ability to adopt new electric appliances without significant upgrades. Panel capacities positively correlate with both home size and construction year, with newer and larger homes generally better suited for electrification. Homes with higher-capacity panels tend to have fewer gas appliances, reflecting a gradual shift toward electric technologies. Conversely, homes with lower-capacity panels frequently rely on multiple gas appliances, highlighting substantial electrification challenges. Additionally, approximately 3% of surveyed homes had potentially hazardous electrical panel models, emphasizing important safety considerations in the residential electrification process. Our findings underscore the need for targeted policies, financial incentives, and infrastructure investments designed specifically to address infrastructural and safety barriers, particularly in older and smaller homes, to support equitable and efficient electrification across the U.S. residential sector.

Gul, Sadia↗
DOE OSTI2023

Extremely rapid, Lagrangian modeling of 2D flooding: A rivulet-based approach

Estimates of potential flood inundation areas and depths are critical to informing the preparedness, response, and investment decisions of many government agencies and private sector organizations, especially under a changing climate. The standard modeling approaches, however, are often either computationally intensive or constrained in their accuracy or applicability. A novel, rivulet-based, 2D model of pluvial flooding is described in this article that is 10,000 to 10 million times less computationally complex than the full solution of the shallow water equations, yet achieves inundation area hit rates of between 0.8 and 0.9 and relative absolute mean errors of 10%-20% across a wide range of flow depths. This combination of accuracy and efficiency will enable real-time depth estimates during flood events, detailed sensitivity analyses, and the generation of large ensembles to support broad uncertainty analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multidisciplinary Design, Analysis, and Optimization (MDO) for Co-Designed Transmission & Distribution Electric Grid Planning

This paper describes early experiences and example use cases applying multi-disciplinary design analysis and optimization (MDO) to the integrated design of power grids. Adapted from aerospace, MDO enables combining multiple existing tools into a coordinated optimization. Here we use MDO to simultaneously capture integrated transmission-distribution and investment-engineering trade-offs in an automated framework. Example use cases showcase prototype interactions among existing grid models using MDO and hint at the types of integrated analyses enabled by this approach. In addition, we share experiences and thoughts on grid-specific challenges and opportunities to help advance further work in this area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Inequality and the Future of Electric Mobility in 36 U.S. Cities: An Innovative Methodology and Comparative Assessment

Electric vehicles are seen as one of the technological solutions to transition our transportation systems away from carbon, and cities offer unique opportunities to electrify transportation. To be equitable, however, this transition will not merely require technological innovations. Acknowledging socio-spatial inequalities and creating strategies to address them are critical - yet relatively underexplored - dimensions of the transportation transition. This paper integrates relevant literature into a micro-urban social typology (MUST) approach that uses agglomerative clustering techniques to examine, first, the factors and attributes defining transportation inequities within 36 U.S. cities, and, second, the implications of these inequities for policies that foster a more equitable transportation transition. By combining socio-spatial and transportation data, we identified five MUSTs: Wealthy, Urban Disadvantaged, Urban Renters, Middle-Class Homeowners, and Rural/Exurban. Rather than being tied to any particular indicator (e.g., homeownership), these MUSTs contain intersecting factors and features of inequities. We compare transportation and health outcomes across MUSTs, and the results suggest that user-centric strategies and public investments are necessary to foster true transportation equity. These must go beyond the electrification of private vehicles and should be tailored to the specific characteristics of each MUST. These could include electric carpooling for the rural/exurban MUST and electrification of transit for the urban disadvantaged and renter MUSTs. Our typology offers a critical next step toward informing transportation transition policies to target critical sociodemographic, economic, and techno-infrastructural factors.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Shedding light on the economic costs of long-duration power outages: A review of resilience assessment methods and strategies

Here this paper provides a literature review of methods and modeling techniques to estimate the cost of power system outages, along with the value of outage mitigation or system resilience. Regulators, policymakers, and infrastructure owners have a growing need to understand the methods for estimating the benefits of resilience improvements of electric infrastructure against natural and man-made disasters. There is a broad literature that estimates the cost of short-duration outages and a small but developing literature on estimating the cost of long-duration outages. This article reviews the models used to estimate the cost of outages and discusses their relative strengths. Additionally, this paper identifies key questions from stakeholders regarding resilience investment and maps them to the relevant models that would help answer them. We include recommendations for future work to include recent advances in regional economic modeling that can estimate region and demographic-specific costs and the distributional consequences of potential resilience projects.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Five grand challenges of offshore wind financing in the United States

Offshore wind energy has the potential to play a critical role in fostering a renewable energy transformation in the United States. This owes to its massive technical potential, strategic location near densely populated coastlines, and - relative to onshore wind and solar - high capacity factors and consistent production. The Biden Administration's target to build 30 GW of offshore wind capacity by 2030 (from 0.04 GW today) requires the creation and swift development of a new industry that interlinks the wind and power industries with the maritime sector. Critical to its success is financing. While financial capital is abundant, deploying it for offshore wind faces major challenges. We identify and describe five grand challenges affecting offshore wind finance in the U.S. Failing to address these challenges may put deployment targets at risk. The challenges include (1) Early years financing: navigating the complexities, timing mismatches, and high costs of projects in the development phase; (2) Policy support for project financial solvency: addressing the uncertainty and systematic transfers of tax credits away from offshore wind, characteristic of the U.S. Investment Tax Credit; (3) Workforce development: building a skilled workforce for an emerging market; (4) Transmission and integration barriers: upgrading the power grid to reliably support large scale offshore wind integration; and (5) Floating wind development: financing the development and scale-up of floating offshore wind technologies. The second challenge has already been solved to a large extent by the Inflation Reduction Act.

17 WIND ENERGY↗
DOE OSTI2025

Critical minerals lists for low-carbon transitions: Reviewing their structure, objectives, and limitations

Critical minerals lists have flourished in the past decade, in particular linked to the importance of critical minerals for low-carbon transitions. We identified 27 critical minerals or materials lists across 15 countries and the European Union (EU). These lists are designed to attract public and private attention and investments to secure both domestic and foreign supplies. This review article fills a gap in the existing literature by analyzing the ways in which these lists are defined and utilized by countries engaged in a mineral rush. We focus our attention on three categories of minerals – battery minerals, platinum-group metals (PGMs), and rare earth elements (REEs) that are particularly important to energy transitions. We situate this research in the broader legal and administrative developments that have driven critical minerals policies in the past decade. We provide an in-depth analysis of the commonalities and variations in the raw materials included in these lists, and identify six core limitations of critical minerals lists: (1) unclear links between criticality assessments and mineral prioritization (2) failure to account for the full mineral value-chain; (3) limited strategic alignment between allied nations; (4) limited flexibility in dynamic environments (5) limited consideration for recycling and by-product sourcing; and (6) reliance on incomplete reserve and resource data.

Battery minerals↗
DOE OSTI2021

Investigating feasible light configurations for fish restoration: an ethological insight

Light environment significantly impacts the effectiveness of fish population restoration in developed watersheds, subserving fish passage reconstruction in hydraulic complex and habitat rehabilitation. To develop an interior connection between light-related fish response from the laboratory and field fish conservation practice, we employed a new indexing system, consisting of phototaxis rate, relative swimming speed (RSS), and optic evasion coefficient (OEC), to quantify the light-induced behaviors in Ptychobarbus kaznakovi, a representative rare cyprinid in Tibet, China, at four experimental illuminance levels (15 lx, 30 lx, 60 lx, 120 lx) and four wavelengths (red, yellow, green, blue). The fish showed negative phototaxis at all given illuminances and wavelengths. Under red light, the OEC increased significantly from 0.642 at 15 lx to 0.782 at 30 lx (P = 0.029), while the RSS decreased significantly from 3.752 to 2.383 (P < 0.001). Behaviorally, the fish shifted from sprint to wandering around, indicating the alteration of dominant physiological activity from behavioral stress response to negative phototaxis. Under the green treatment, P. kaznakovi swam around quickly and presented slight negative phototaxis, and OEC values were uniform among all illuminances, probably because of the similarity of green light to the ambient color of the habitat. Accordingly, ecologists and fisheries practitioners can utilize red light to exclude fish from dangerous waters and green light to guide them to ideal habitats, laying the necessary groundwork for the light-driven fish recovery effort. Furthermore, the appropriate light configuration can yield economic benefits through the tradeoff among fish protection, power generation, and investment costs.

Lin, Chenyu↗
DOE OSTI2022

Meeting sustainable aviation fuel policy targets through first generation corn biorefineries

This paper investigates options to produce low carbon and renewable aviation fuel through existing commercial first generation biorefineries, which could be strategic given the rapid growth of the electric vehicle market for personal mobility. We evaluate the environmental impact of retrofitting a first generation biorefinery to produce aviation fuel through chemical process simulation. The analysis investigates the potential for reducing greenhouse gas (GHG) emissions and displacing fossil fuel (petroleum-based jet kerosene) in the aviation fuel industry through using corn feedstocks in dry grind biorefineries to convert sugars to 1,4-dimethylcyclooctane (DMCO), which qualifies as renewable jet-A fuel blend and is infrastructure compatible. Further, the average life cycle GHG emissions for a baseline case and a scenario with carbon capture and storage (CCS) of fermentative CO 2 are 36 and 5g CO 2 e/MJ DMCO, respectively. Investment in CCS at the biorefinery and adoption of crop best management practices on farms are essential for mitigating the risks of induced land use change GHG emissions for existing corn biorefineries. DMCO as a low-carbon aviation fuel could be a strategic fuel product for retrofitting existing corn dry grind facilities. For aviation fuel supply, corn dry grind facilities could displace about 12% of jet fuel demand in the near term, which would meet sustainable aviation fuel policy targets. We conclude that corn-to-DMCO shows promise for near-term low-carbon fuel markets in the aviation industry and as a bridge to developing biomass-based jet fuel in the future.

09 BIOMASS FUELS↗
DOE OSTI2024

Approach to startup inventory for viable commercial fusion power plant

With the increasing efforts to commercialize fusion power, private and government organizations are investing heavily in the development of technology to support a viable fusion power plant. Deuterium-Tritium (DT) fueled reactors are more prevalent than other proposed designs, requiring tritium processing and handling technology for safe operations and self-sufficiency. Further, each fusion power plant will need a specific-to-design startup inventory of tritium to begin operations. This startup inventory is required prior to breeding and is the minimum tritium inventory required to fill each processing component in the fuel cycle, to offset radioactive decay losses, and to avoid a zero-fuel situation for continuous operation. We present an approach to calculate the startup tritium inventory for a 500 MW th reactor, with considerations for reserve inventory for maintenance and commissioning. A baseline startup inventory was calculated to be approximately 327 gs. This value was obtained using modest assumptions about the technology and operating parameters of a fusion power plant. The required operating reserve inventory or the inventory necessary to keep a fusion power plant operational using only direct internal recycling for 24 h for the same plant design is approximately 642 gs. The approach and findings of this paper will enable fusion energy stakeholders to better utilize the existing scarce global tritium supply.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.

Accelerated Data Analytics and↗
DOE OSTI2023

Economic and operational investigation of CO 2 sequestration through enhanced oil recovery in unconventional reservoirs in Colorado, USA

The ongoing CCUS commercial projects are highly relied on the support of government incentives due to massive capital investment. Here, this study analyzes the economics of carbon capture utilization and sequestration (CCUS) projects, shows a state-wide CCUS deployment exercise, followed by simulation results of enhanced oil recovery (EOR) based CO 2 storage in unconventional reservoirs. The comprehensive economic analysis of capture, transportation, sequestration costs, enhanced 45Q tax credits, and EOR revenue implies the practicality of CO 2 -EOR to offset the high CCUS costs. With the economics understanding, we study the top CO 2 sources, existing CO 2 pipelines, and sequestration sinks in the state of Colorado, USA. This paper next presents results from EOR simulation in one section of the unconventional Denver-Julesburg (DJ) Basin Niobrara and Codell reservoirs. The simulation model is based on a geological static model, incorporated with hydraulic fracture stimulation, history matched to production, and calibrated to the microseismic and time-lapse surface seismic data. The CO 2 -EOR simulation results show that oil production can be increased and more CO 2 stored with: a longer primary production period; the presence of a shut-in period; higher injection rates; and multi-well injectors. The modeling results show that about 7–10 Mscf of CO 2 will be stored when recovering 1 stb of EOR oil. By adding the enhanced oil revenue and the carbon credits together, it is estimated that the most economic case can generate $\$13$ MM when oil price is assumed to be $\$80$/stb, and the EOR oil revenue is 3.4 times greater than that generated from 45Q incentives. It corresponds to the scenario that a five-year primary production is followed by CO 2 injection into four wells with the sequence of injection (4 MMscf/day for 6 months), shut-in (6 months) and production (12 months). The best practices in this study will provide valuable insights for similar CCUS projects in other unconventional fields. Furthermore, this study defines a term named “Carbon Neutrality Index (CNI)” by comparing the amount of CO 2 stored with that burned by EOR oil. The CNI value of 0 indicates the enhanced oil is carbon neutral; a negative CNI value implies there is a net reduction in carbon emission. The 4-year huff-n-puff (HnP) simulation leads to a positive CNI value, indicating that the EOR oil generated in this process is not carbon neutral yet.

03 NATURAL GAS↗
DOE OSTI2021

Common risk segment mapping: Streamlining exploration for carbon storage sites, with application to coastal Texas and Louisiana

Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. We adapt the CRS process to screening for CO 2 storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The results highlight sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. Furthermore, the method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO 2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evolution of storage monitoring – update in response to commercial and regulatory drivers

Carbon Capture and Storage (CCS) is in transition from first-of-a kind projects and research-orientated pilots to commercially-motivated applications. Monitoring results from many newly developed and planned large scale commercial projects are limited; however, it is worthwhile to assess their evolution and consider new strategies as part of an effort to assess and document best practices. Commercial monitoring is targeted to activities that comply with regulatory drivers and de-risk investments. Commercial monitoring also supports accounting that storage has occurred and is tied to project financing. It deals with long time frames and large volumes injected into multiple wells and multiple projects in favorable areas. We see developing trends toward reproducible workflows that systematically reduce risks and clarify expectations for oversight and long-term surveillance. Monitoring techniques showing increasing trends include injection zone pressure as a history-matching and compliance tool. To reduce cost and environmental impact of time-lapse seismic data collection, deploying new approaches and tools, such as use of fibre and installed sources are increasingly applied. Concern over the risk of induced seismicity by regulatory bodies and the general public has increased, which has also resulted in increased monitoring. Some techniques used in the early research phases have been sidelined or used only in restricted applications. For example, geochemical analyses in the injection zone as well as the environment are now being deployed less than it was in research-oriented programs, except in the US where it is required by the permitting process. Expectations of frequent area-wide near surface monitoring have also decreased.

25 ENERGY STORAGE↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗