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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 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2020

GEMINI Case Study

Sandia's GEMINI-Scout Mine Rescue Robot is an unmanned ground vehicle designed to enter potentially hazardous environments to explore, assess, and evaluate dangerous situations first responders may face when conducting a rescue mission. GEMINI is approximately four feet long and two feet tall, which enables the robot to maneuver through small locations on rough terrains caused by earthquakes, fires, or radiological incidents. GEMINI uses track propulsion to climb stairs, travel through gravel and sand pits, pivot in place, and traverse 45-degree climbs with few problems. Furthermore, the vehicle's dual tracked-chassis design allows it to operate in hostile, dark, muddy, high-temperature, and explosive debris-strewn environments, while maintaining efficient ground mobility. The mobility and modularity of the vehicle allow for easy integration of sensors to conduct gas and temperature sensing and offers pan/tilt, zoom color, and thermal camera video streaming capabilities. The vehicle is also able to carry a payload of about 50 pounds of batteries and can handle an additional 200 pounds of payload, whether for additional diagnostics, supplies, or clothing for those trapped in an effected area. GEMINI is remotely operated through a wireless connection and an onboard computer running a customized embedded control application, which directly communicates to all onboard components except for the audio and video systems. When line of sight is not possible, operators use a shockresistant fiber optic cable to ensure continuous functionality of the vehicle. This allows for direct local control of the vehicle, which streams collected data back to the operator for enhanced situational awareness. In addition, the vehicle incorporates safety features such as explosion proof housing to ensure safe electronic operations in hazardous gas or flooded environments; a four-channel video link and two-way audio to ensure located survivors can communicate with operators; and an MSHA-approved multi-gas sensor to monitor air quality.

42 ENGINEERING↗

DOE OSTI2023

Statistical Treatise on Critical Biodiesel (B100) Quality Properties in the United States from 2004-2022

The quality of neat biodiesel (B100) is critical for ensuring biodiesel blends used in diesel-powered vehicles do not adversely impact engine performance. In the United States, B100 is required to meet ASTM International's purity and fuel property requirements in D6751, "Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels." Here we review the development of this standard for the different grades of B100. The BQ-9000 program, which currently covers over 90% of U.S. and Canadian production volumes, is also described. Engine and original equipment manufacturers have expressed a desire for credible, third-party data on values of various ASTM B100 properties in the commercial market to inform their efforts to address future emissions and durability requirements. To address this need, the National Renewable Energy Laboratory published data from analysis and testing of monthly samples from BQ-9000 producers from calendar years 2017-2022, resulting in 300-500 data points (i.e., individual fuel samples) per year. These data and a statistical evaluation of properties including the average, mean, standard deviation, and 95th or 5th percentile are presented. The results show very consistent and high-quality B100 produced in this time frame. Additionally, we review the results of earlier quality surveys conducted in the United States between 2004 and 2011, revealing how quality has changed over time in response to ASTM standard revisions, as well as the significant overall improvement in quality for modern biodiesel production.

ASTM standard↗

DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles

Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

GridPIQ Reference Data

GridPIQ uses dozens of publicly available datasets to provide context for a user's grid project, as well as defaults for users to choose from. Users can choose to import their own data to better customize their analysis or use GridPIQ-supplied defaults. This allows users to get up and running with an analysis very quickly without having to spend significant time pulling together input data. To run an electric vehicle (EV) smart charging project, a user will need to provide or select from prepopulated values for the regional load profile shape and peak load, region of interest and closest weather station, EV charging profile, number of EVs to add for the analysis, maximum EV charging power, location of chargers relative to grid infrastructure, and allowable charging times (for coordinated charging mode). The outputs of the analysis are changes in air quality, EV energy consumption, EV peak demand, and EV hourly consumption profile—before and after project implementation." For a detailed description of the tool methodology, including all the publicly available datasets used by the tool, see the [GridPIQ documentation](https://gridpiq.pnnl.gov/v2-beta/doc/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Sensitivity of back‐projection algorithm (BPA) synthetic aperture radar (SAR) image formation to initial position, velocity, and attitude navigation errors

Abstract The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Lindstrom, Colton↗

DOE OSTI2026

2001 Atlanta Household Travel Survey

The 2001 Atlanta Household Travel Survey collected demographic, socioeconomic, and travel information on work and non-work travel behavior for a 48-hour travel period. The study was conducted on behalf of the Atlanta Regional Commission, and it is an essential element in the transportation planning and modeling efforts for the 13-county Atlanta region. The main objective of the study was to produce data that could be used to develop and calibrate travel demand models for use in travel forecasting, land use planning, and air quality planning to improve the transportation system. The second component in the survey was the deployment of an electronic travel diary with person-based GPS and an accelerometer to collect health and activity information. Travel data includes trip generation, trip distribution, and modal choice. The survey recruited a total of 12,184 households to participate in the study. Of these, 8,069 households (66%) completed travel. The 8,069 households, when weighted, represent 21,323 persons, 14,449 vehicles, and 126,127 places visited from April 2001 through April 2002. This dataset is part of the Metropolitan Travel Survey Archive, which includes travel surveys from numerous public agencies across the United States and is archived by the Transportation Secure Data Center to ensure their continued public availability.

1Hz data↗

DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗

DOE OSTI2022

Better bang for your buck? Comparing savings realization from ESPCs and direct-funded projects

Energy savings performance contracts (ESPCs) offer an opportunity to tremendously scale decarbonization projects, given their paid-from-savings premise. However, prospective customers still question whether ESPC is worth the effort and expense. This study evaluates the savings realization rates of ESPCs compared to direct-funded projects using ENERGY STAR Portfolio Manager (ESPM) benchmarking data. It compares normalized EUIs from ESPM before and after energy conservation projects implemented via ESPC or direct funding in roughly 450 federal buildings. This documented change, ideally a savings, can then be compared to the estimated savings from the energy conservation initiative in order to generate a rough realization rate. Preliminary results indicate a notably greater savings realization rate for the ESPC buildings (median = 105% of estimated savings) than those that underwent direct-funded projects (median = 46%). Because of a shortage of good quality data and the wide range in results, the difference is only significant at the p < 0.20 level. However, the higher savings realization of ESPCs corroborates the authors’ 2014 findings using a different comparison method. With continued soft government funding and regulatory impediments for climate change mitigation in the U.S., financed, paid-from-savings project models (e.g., ESPC, PACE, EaaS and others) certainly merit more attention. This study’s results are particularly compelling (and encouraging) given the country’s likely reliance on these vehicles to address existing building retrofits.

Earni, Shankar↗

DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part II - Autonomous Navigation

Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Enabling Seamless Integration of EV Charging Infrastructure with Weak Electric Grids

Widespread adoption of Electric Vehicles (EVs) for light, medium, and heavy-duty applications has gained significant interest. Based on ownership and user preferences, light duty and local delivery medium-duty EVs are typically connected to grid distribution networks whereas larger medium duty and heavy-duty EVs are typically connected to distribution or even sub-transmission networks. Challenges with at-scale EVs and charging infrastructure supporting them include interoperability, distribution network upgrades, surge in demand charges, power quality issues, voltage stability, etc. Based on real-world data, steady-state and dynamic assessments of light-duty EV charging with a focus on DC Fast Charging (DCFC) for weak distribution grids is presented. Three main areas of contributions in this paper include - steady state and dynamic power quality measurements and assessments using real world data, transient assessments of light duty EV integration with weak distribution networks (IEEE 13 node feeder system) under grid-connected and microgrid modes, and formulation of an intelligent charging decision algorithm to enable an impact-minimal charging.

actionable intelligence↗

DOE OSTI2026

2008 East Tennessee Household Travel Survey

The 2008 East Tennessee Household Travel Survey, conducted by NuStats, was a comprehensive study of travel behavior in Knox, Blount, Anderson, Jefferson, Loudon, Roane, Sevier, and Union counties. The purpose of the survey was to collect weekday travel characteristics of households in the eight-county region during a 24-hour timeframe. The data will be used by the Knoxville Regional Transportation Planning Organization and local agencies to update transportation and air quality models and to identify transportation needs in the region. The survey collected demographic and travel data from a sample of 1,400 households between February and May 2008. In total, 12,012 trips were reported by 3,301 persons.

1Hz data↗

DOE OSTI2020

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

17 WIND ENERGY↗

DOE OSTI2020

Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗