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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Tactical Surface Metering Procedures for Charlotte Douglas International Airport

NASA has been collaborating with the Federal Aviation Administration (FAA) and aviation industry partners to develop and demonstrate new concepts and technologies for the Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. The primary goal of the ATD-2 project is to improve the predictability and the operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure and surface prediction, scheduling, and management systems. In the Phase 1 Baseline IADS Demonstration, the tactical surface scheduling capability and the user interfaces for ramp controllers and ramp traffic managers were implemented for ramp operations. The purpose of the tactical surface scheduling capability is to provide the airline ramp controller with aircraft pushback advisories that prevent surface congestion and to respond to surface and airspace constraints that become known over relatively short time horizons. For this purpose, the tactical surface metering tool first estimates the capacity of current and near-future runway resources from flight schedule and surveillance data. With demand forecasts and predicted taxi trajectories, this tool computes an efficient runway schedule of aircraft in the planning horizon based on their readiness, Earliest Off-Block Times (EOBTs), and a ration by schedule (RBS) rule. Details on the implementation of the Tactical Surface Metering tool will be provided in the full paper. Both pushback and recommended hold times advisories provided by this surface metering tool are shown on the user interfaces for the ramp controller and the ramp traffic manager, called Ramp Traffic Console (RTC) and Ramp Manager Traffic Console (RMTC), respectively. There is excess queue time in the system due to demand capacity imbalance, this time can be taken as a hold on the runway queue or at the gate and was referred to as the Metering Value. This metering value can be adjusted by the Ramp Manager in collaboration with Air Traffic Controller-Tower Traffic Management Coordinator (TMC). They selected a set of metering values as default values for the tool during human-in-the-loop simulation. As the metering value increases, there is a decrease in the gate hold and increase in the queue time at the runway. Procedures and Information needs related to managing the surface metering procedures were researched in the simulated environment. These procedures will be compared to the procedures adopted at Charlotte Douglas International Airport when the tools were deployed and adopted in November 2017 for one departure push bank per day. Feedback regarding initial issues, information needs such as the need to see EOBTs on the flight data tags and how they compare to scheduled times will also be discussed in the full paper. Initial results will be provided regarding the choice of the metering value and how it was adjusted on a daily basis and what procedures evolved will also be presented in the paper.

surface metering↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Work from Home Patterns across COVID-19 Waves: Implications for Future Transportation

The unprecedented rise in work from home (WFH) during COVID-19 poses challenges for the transportation engineers and planners with the travel demand forecasting. If WFH persists post-pandemic, it could influence traffic patterns, reducing peak-hour congestion. However, the evolution of WFH decisions across pandemic phases and varying socio-economic contexts remains unclear. This study examines factors influencing WFH choices using data from the US Census Bureau’s Household Pulse Survey. Findings reveal a decline in WFH participation from 60% during the first wave to 38% by the third wave of the COVID-19 pandemic. A Geographically Weighted Regression model highlights the influence of socio-economic, household, and COVID-19-related variables, with notable spatial variability. Results show that younger individuals, females, and households with children are consistently more likely to WFH, while non-white and higher income individuals have an increasing likelihood for WFH as the pandemic progresses. These insights inform future transportation planning, emphasizing equity and decentralization strategies for post-pandemic commuting.

Patwary, Latif [ORNL] (ORCID:0000000189174928)↗
DOE OSTI2026

2011 Atlanta, Georgia, Regional Travel Survey

The 2011 Regional Travel Survey collected trip data from households across 20 counties in and around Atlanta, Georgia, to improve regional travel demand forecasts. The survey was conducted by PTV NuStats, GeoStats, and PG Americas Inc. on behalf of the Atlanta Regional Commission. The goal was to collect trip data from a minimum of 10,000 households with a subsample of 1,000 households providing global positioning system (GPS) data. The GPS add-on consisted of two sample groups. The first sample group was provided with GPS devices to install in their personal vehicles (797 vehicles) to collect the speed and location of a vehicle during an assigned study period. The second sample group (1,653 participants) was provided with a wearable GPS device to collect the speed and location of an individual during an assigned study period. People participating in the wearable add-on part of the study were chosen because their travel diaries reported use of public transit in day-to-day travel. Each GPS participant was grouped into either the spring phase (March to May 2011) or the fall phase (July to September 2011), and data were recorded for a maximum of seven days for vehicle units and three days for wearable units.

1Hz data↗
DOE OSTI2026

2007 Chicago Regional Household Travel Inventory

To provide data for developing and refining travel demand forecast models, the 2007 Regional Household Travel Inventory studied the demographic and travel behavior characteristics of residents in the greater Chicago area. It included households in eight counties in Illinois and three in Indiana. The Chicago Metropolitan Agency for Planning conducted the study in coordination with the Illinois Department of Transportation, the Northwestern Indiana Regional Planning Commission, and the Indiana Department of Transportation. Travel and activity information for all household members (regardless of age) was collected during a randomly assigned 24- or 48-hour period. It relied on the willingness of households to provide detailed information about their members and vehicles, and to record all travel and associated activity during the collection period. This study also featured a subsample of households collecting data via in-vehicle and wearable global positioning system devices.

1Hz data↗
DOE OSTI2026

2010-2012 Minneapolis - St. Paul Travel Behavior Inventory

The 2010-2012 Travel Behavior Inventory (TBI) provided Minnesota policymakers and researchers with data about travel in the Minneapolis - St. Paul region. It also updated the region's travel demand forecasting, including transit ridership for major transportation projects. The Metropolitan Council in the Minneapolis-St. Paul area conducted the survey. The TBI consists of a paper-based survey and a wearable global positioning system survey. The data collection process for these two surveys was independent, and the results are not intended to function together.

1Hz data↗
DOE OSTI2026

2008 NIRPC Household Travel Inventory

The Northwestern Indiana Regional Planning Commission (NIRPC) Household Travel Inventory, also sponsored by the Indiana Department of Transportation, is a comprehensive study of the demographic and travel of residents of Lake, Porter, and La Porte counties in northwest Indiana. The primary objective of the study was to provide data for the continued development and refinement of the Indiana regional travel demand forecast models. 3,838 households from northwest Indiana participated in the survey, which entailed the collection of activity and travel information for all household members regardless of age during a randomly assigned 24- or 48-hour period. NIRPC travel survey data provided the information required on base-year daily trips made by study area residents to the Chicago CBD. This travel market is treated separately because of the importance of transit for its travelers.

1Hz data↗
DOE OSTI2026

1995 OMPO Model Development Project

The 1995 Oahu Metropolitan Planning Organization (OMPO) Model Development Project for Oahu Island was intended to document how Oahu residents use the streets, highways, and transit services in the region. These data were used to develop the Travel Demand Forecasting Model, which is used to forecast the traffic impacts of various land use changes. The survey collected complete travel information of 4,060 Oahu households for a 24-hour period, including demographic and socioeconomic data.

1Hz data↗
DOE OSTI2026

1988/1989 Maricopa Household Travel Study

This study was commissioned by the Maricopa Association of Governments (MAG) Transportation and Planning Office. The primary objectives of this study were to update the trip generation rates used in the MAG travel demand forecasting process and to provide data to validate the MAG trip distribution model. Demographic, socioeconomic, and travel data was collected for 2,992 households residing within the MAG Urban Planning Area. Respondents were asked to record their travel and activities for a 24-hour period. A total of 26,733 trips were recorded by 6,463 people.

1Hz data↗
DOE OSTI2026

1999 Puget Sound Household Travel Survey

The 1999 Puget Sound Household Travel Survey was conducted between July and November. NuStats Research and Consulting conducted the survey on behalf of the Puget Sound Regional Council. The purpose of the study was to provide data to continue developing and refining the Regional Travel Demand Forecasting Model, as well as to provide a better understanding of travel behavior in the Puget Sound region. The study area consists of King, Kitsap, Pierce, and Snohomish counties. The resultant dataset will be used to fulfill the model's functions of estimating trip generation and distribution, mode choice, and assignments. The study had household members 16 years old or older keep track of travel for a 48-hour period. A total of 9,028 households were recruited to participate in the study. Of these, 6,000 households (66.5%) completed travel diaries, and the information was retrieved from all household members regardless of age. An “attitude” survey about transportation and land use issues was also mailed to household members 16 years old or older.

1Hz data↗
DOE OSTI2026

1994 Household Travel Survey

The Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board (COG/TPB) periodically conducts regional household travel surveys to monitor changes in daily travel and to gather information on the demographic, socioeconomic, and trip-making characteristics of Washington, D.C.-area residents. Information collected in the 1994 Household Travel Survey was an important component in the development of regional travel demand forecasting models used to predict changes in daily travel in response to current development trends and changes in regional transportation policies and programs. In two waves during the spring and fall of 1994, consultants for COG/TPB conducted a survey of daily travel by persons living in area households. The survey file contains travel information associated with 4,863 households residing in 13 jurisdictions comprising the greater Washington, D.C., region (approximately a 1-in-300 sample). The survey file contains 39,800 internal trip records with respect to the expanded cordon.

1Hz data↗
DOE OSTI2026

1998/99 Thurston County Household Travel Study

The survey was conducted under the auspices of the Thurston Regional Planning Council, and it was funded through a state grant awarded to Intercity Transit of Olympia, Washington. Data collection was from September 1998 through March 1999. The purpose of the study was to provide data for the continuing development and refinement of the Regional Travel Demand Forecasting Model, as well as to provide a better understanding of travel behavior in the southern Puget Sound region of Washington. The resultant data set will be used to fulfill the model's functions of estimating trip generation and distribution, mode choice, and assignments. Participating households were assigned specific “travel days” to record their travel over a 48-hour period. A total of 2,465 households were recruited to participate in the study. Of these, 1,537 households completed travel diaries, and the information was retrieved from 3,653 household members regardless of age. Households member made 25,278 total trips during their 48-hour diary period.

1Hz data↗
DOE OSTI2026

1968 Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board Home Interview Survey

The Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board periodically conducts regional household travel surveys to monitor changes in daily travel and to gather information on the demographic, socioeconomic, and trip-making characteristics of Washington, D.C.-area residents. Information collected in the 1968 Home Interview Survey was an important component in the development of regional travel demand forecasting models used to predict changes in daily travel in response to current development trends and changes in regional transportation policies and programs. The survey sampled 26,000 households residing in six jurisdictions comprising the greater Washington, D.C. region (an approximately 1-in-20 sample).

1Hz data↗
DOE OSTI2026

1987/88 Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board Home Interview Survey

The Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board periodically conducts regional household travel surveys to monitor changes in daily travel and to gather information on the demographic, socioeconomic, and trip-making characteristics of Washington, D.C.-area residents. Information collected in the 1987/88 Home Interview Survey was an important component in the development of regional travel demand forecasting models used to predict changes in daily travel in response to current development trends and changes in regional transportation policies and programs. The survey sampled 8,000 households residing in eight jurisdictions comprising the greater Washington, D.C. region (an approximately 1-in-166 sample).

1Hz data↗
DOE OSTI2025

Hourly Load Profile Dataset for Electric Transit Bus Depots in the United States

Transit buses operate primarily in dense urban areas, where nearby populations face increased exposure to fine particulates, nitrogen oxides, and other harmful pollutants. Electrifying transit buses presents a clear opportunity to reduce greenhouse gas emissions and improve urban air quality. However, widespread adoption may pose significant energy and infrastructure challenges, which can be mitigated through proactive planning and investment. This report presents a robust modeling framework and an initial estimation of the hourly electricity demand at transit bus depots across the United States. The resulting depot-level dataset, available at data.nrel.gov/submissions/282, provides valuable insights for infrastructure planning and electricity demand forecasting, supporting the scalable electrification of transit bus fleets nationwide.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Regulators’ Financial Toolbox: Leveraging Software as a Service, Cloud Computing, and Artificial Intelligence in Electric Utilities

The rapid evolution of Software as a Service (SaaS), cloud computing, and artificial intelligence (AI) is transforming the electric utility industry, reshaping operations, customer engagement, and financial models. This webinar introduced how utilities can deploy advanced software solutions and AI-driven analytics to improve grid efficiency, optimize asset management, and accurately forecast demand.

Bartlett, Phillip↗