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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Design of a first-of-A-kind instrumented advanced test reactor irradiation Capsule experiment for In situ thermal conductivity measurements of metallic fuel

Metallic fuel undergoes dramatic microstructural changes early in life due to fission gas swelling until ~2–3 at% burnup which affects the conductivity of the material, however the evolution of metallic fuel thermal conductivity during this early phase burnup has never been successfully measured in situ. The Irradiated Material Properties Accelerated Characterization Test (IMPACT) experiment will be the first in a series of experiments to irradiate advanced nuclear metallic fuel specimens with novel embedded thermal conductivity probes in ATR. In the current work the IMPACT experiment final design and supporting analysis is reported in detail. Results are evaluated for various reactor operational conditions to meet the functional requirements of the experiment. Finally, the first iteration of this IMPACT experiment will provide data regarding thermal properties evolution in uranium-zirconium (U10Zr) fuel, but this experiment vehicle is envisioned for future advanced fuels and structural materials irradiations in ATR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A data-driven mobility–energy typology framework for New York State

Exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals. Concurrently, mapping key enablers, barriers, and risks for successfully meeting ambitious goals and targets (e.g. by geography, age, income, education, population density) offer important explanatory power as to context-specific challenges and opportunities. This paper explores how a highly geographically resolved understanding of social, economic, techno-infrastructural, environmental, and governance (SET EG) factors shape variations in technology adoption and associated mobility and energy outcomes in diverse communities of New York State, in terms of electric vehicle adoption rates, alternative commute mode choices, vehicles per household, and vehicle fuel economy. Results indicate the range of two to three times higher adoption rates for electric vehicles by more highly educated, wealthier, “Core Urban” populations relative to the other identified typologies, given the labels “Rural”, “Suburban”, and “Urban” populations. Additionally, commute mode choice is closely linked with population and employment density—more than 89% of Core Urbanites use transit or active modes, compared with just 26% and 18% of Suburban and Rural residents, respectively. Household vehicle ownership varies from approximately 1.9 vehicles per household in Rural areas to only 0.6 in Core Urban settings. Findings on differences among the four settlement types, which go beyond simply rural to urban contexts, suggest an important need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems statewide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Validation of Calibrated k–ε Model Parameters for Jet-in-Crossflow

Previous efforts determined a set of calibrated, optimal model parameter values for Reynolds-averaged Navier–Stokes (RANS) simulations of a compressible jet in crossflow (JIC) using a $k–ε$ turbulence model. These parameters were derived by comparing simulation results to particle image velocimetry (PIV) data of a complementary JIC experiment under a limited set of flow conditions. Here, a $k–ε$ model using both nominal and calibrated parameters is validated against PIV data acquired from a much wider variety of JIC cases, including a realistic flight vehicle. The results from the simulations using the calibrated model parameters showed considerable improvements over those using the nominal values, even for cases that were not used in the calibration procedure that defined the optimal parameters. This improvement is demonstrated using a number of quality metrics that test the spatial alignment of the jet core, the magnitudes of multiple flow variables, and the location and strengths of vortices in the counter-rotating vortex cores on the PIV planes. These results suggest that the calibrated parameters have applicability well outside the specific flow case used in defining them and that with the right model parameters, RANS solutions for the JIC can be improved significantly over those obtained from the nominal model.

42 ENGINEERING↗
DOE OSTI2020

Powertrain control system and method of operating the same

A system and method are provided for operating a powertrain control system. The method includes receiving data measured from a plurality of sensors, the measured data relating to distance dependent speed values, and receiving information from one or more vehicle modules, the vehicle module information relating to distance independent speed values. The method further includes building a speed trajectory profile for a horizon window that includes a plurality of speed change regions represented by at least some distance dependent speed values or at least some distance independent speed values, and creating a synthesized speed profile for the horizon window by processing the speed trajectory profile. The synthesized speed profile optimizes efficiency of the powertrain control system at each of the plurality of speed change regions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2021

Sensing Technology Survey for Obstacle Detection in Vegetation

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Hydrogen Fuel Cell Vehicles in Tunnels

There are numerous vehicles which utilize alternative fuels, or fuels that differ from typical hydrocarbons such as gasoline and diesel, throughout the world. Alternative vehicles include those running on the combustion of natural gas and propane as well as electrical drive vehicles utilizing batteries or hydrogen as energy storage. Because the number of alternative fuels vehicles is expected to increase significantly, it is important to analyze the hazards and risks involved with these new technologies with respect to the regulations related to specific transport infrastructure, such as bridges and tunnels. This report focuses on hazards presented by hydrogen fuel cell electric vehicles that are different from traditional fuels. There are numerous scientific research and analysis publications on hydrogen hazards in tunnel scenarios; however, compiling the data to make conclusions can be a difficult process for tunnel owners and authorities having jurisdiction over tunnels. This report provides a summary of the available literature characterizing hazards presented by hydrogen fuel cell electric vehicles, including light-duty, medium and heavy-duty, as well as buses. Research characterizing both worst-case and credible scenarios, as well as risk-based analysis, is summarized. Gaps in the research are identified to guide future research efforts to provide a complete analysis of the hazards and recommendations for the safe use of hydrogen fuel cell electric vehicles in tunnels.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2025

Electric Vehicle and Charging Infrastructure Assessment in Cold-Weather Climates: A Case Study of Fairbanks, Alaska

The purpose of this report is to determine the effects that extreme cold temperatures have on electric vehicles (EVs) and electric vehicle supply equipment (EVSE). The adoption of EVs in extreme cold weather presents challenges that require careful analysis of efficiency and charging infrastructure. This report, "Electric Vehicle and Charging Infrastructure Assessment in Cold-Weather Climates: A Case Study of Fairbanks, Alaska," explores how EVs and EVSE perform in temperatures as low as -40 degrees C (-40 degrees F), focusing on real-world data from Teslas in Alaskan winter conditions. The findings indicate that EVs can successfully function in extreme cold, though efficiency is significantly affected. Vehicles stored in heated environments outperformed those stored outdoors, with efficiency dropping by up to 69% for outdoor storage. Despite these challenges, none of the vehicles experienced failures that prevented travel. Storing EVs indoors led to benefits such as faster preconditioning and improved efficiency, while charging in extreme cold, though slower, remained functional. Ultimately, the report concludes that with proper precautions and best practices, EVs are viable transportation solutions in cold climates. Investing in enclosed, heated storage is recommended to maximize efficiency and minimize battery strain. As advancements in battery chemistry and thermal management progress, EV adoption in extreme climates is expected to become even more feasible.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗
DOE OSTI2021

FEDFLEET: FAST (FY2020 Federal Fleet Dataset Overview) [Slides]

This presentation provides an overview of the fiscal year (FY) 2020 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; provides a high-level look at what the collected information shows about the makeup and operation of the federal motor vehicle fleet during FY 2020 and how that compares with recent years; discusses the process used to review the agency submissions comprising the dataset and the impact of some of the agency-provided corrections to identified issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

City and County Vehicle Inventories

This light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. truck), fuel type, and model year showing the changes in adoption trends over time and average fuel economies. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Alternative Fuel↗
DOE OSTI2026

2005 Washoe County Travel Characteristics Study

The primary objective of the 2005 Washoe County Travel Characteristics Study was to document travel behavior data characteristics of regional households in Washoe County, Nevada, in order to update the regional transportation model. The household travel survey, which was conducted from October through November, was one of several surveys conducted in the fall of 2005 for the Regional Transportation Commission (RTC) of Washoe County, with the other surveys focusing on documenting transit usage, visitor travel behavior, and external travel through the region. The data collected were drawn from 1,174 households within the RTC planning area and contain information about 2,679 household members, 2,138 vehicles, and 11,077 unlinked trips. The household travel survey entailed the collection of activity and travel information for all household members regardless of age during a specific 24-hour period. The survey relied on the willingness of regional households to 1) provide demographic information about their household, its members, and its vehicles and 2) have all household members record all travel and activity for a specific 24-hour period, including address information for all locations visited, trip purpose, mode, and travel times.

1Hz data↗
DOE OSTI2022

Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

Electric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic dispatch model of 2035 generation. We find that peak net electricity demand increases by up to 25% with forecast adoption and by 50% in a stress test with full electrification. Locally optimized controls and high home charging can strain the grid. Shifting instead to uncontrolled, daytime charging can reduce storage requirements, excess non-fossil fuel generation, ramping and emissions. Our results urge policymakers to reflect generation-level impacts in utility rates and deploy charging infrastructure that promotes a shift from home to daytime charging.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

On-Line Prediction of Resistant Force During Soil–Tool Interaction

For off-road vehicles such as excavators and wheel loaders, a large portion of energy is consumed to overcome the soil resistant force in the digging process. For optimal control of the digging tool, a high-fidelity model of the soil–tool interaction force is important to reduce energy consumption. Here, in this paper, an on-line soil resistant force prediction method is proposed. In this method, a hybrid model, which combines a physical model and a data-driven model, is used for the force prediction. In addition, the parameters of the hybrid model can be updated on-line based on real-time data. Comparisons with experimental data demonstrate that the proposed prediction method has an average error of around 12.7%.

42 ENGINEERING↗
DOE OSTI2021

INTERFUEL: FAST - FY 2020 Federal Fleet Dataset [Slides]

This presentation provides an overview of the fiscal year (FY) 2020 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; a high-level look at what the information collected shows about the makeup and operation of the federal motor vehicle fleet during FY 2020 and how that compares with recent years; the process used to review the agency submissions comprising the dataset and the impact of some of the agency-provided corrections to identified issues; activities following the finalization of the dataset.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Real-time Object Bounding in LiDAR Data With Computer Vision

The Multimodal Measurement System is a roadside radiation measurement testbed used to detect radiation sources in passing vehicles. It works by combining sensor signals from various modalities to produce a thorough scan of the source. A LiDAR sensor is used to measure the dimensions of the vehicle and provide a velocity estimate. However, the current LiDAR setup uses propriety software for which the source code is unavailable and cannot be updated to improve performance. Therefore, it is imperative to the accuracy of the analysis to create a custom vehicle detection that can return the dimensions and velocity of passing vehicles in real time. This new custom detection is written in C++ using the PointCloud Library, which keeps it lightweight. It also utilizes Docker and the Robot Operating System, which allows the versatility of running both on a small computer or the Lawrence Livermore National Laboratory cluster while utilizing different models of LiDAR sensors. The custom detection outperforms the current detection model, which increases the accuracy of radiation source detection.

97 MATHEMATICS AND COMPUTING↗