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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013 Metropolitan Area Planning Agency External Travel Survey

# 2013 Metropolitan Area Planning Agency External Travel Survey The 2013 Metropolitan Area Planning Agency (MAPA) External Travel Survey was conducted to measure and identify travel patterns into, within, and out of the greater Omaha/Council Bluffs metropolitan area in Nebraska. MAPA sponsored the survey in conjunction with the Federal Highway Administration, the Nebraska Department of Roads, and the Iowa Department of Transportation. ## Data Collection Agency MAPA conducted the survey. ## Methodology The purpose of the survey was to collect information and data needed as input for MAPA’s travel-demand model. The survey employed a combination of nine survey methods and data-collection activities, including Bluetooth technology, intercept surveys, postcard handouts, travel-time studies, vehicle classification counts, and a web-based survey. ## Survey Records Survey records include a total of 729 participants. ## Transportation Data This study provides Bluetooth records and supplementary data for 17,434 passenger trips and 3,123 commercial trips, accounting for 714,218 vehicle miles traveled. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

ambrosia: An R package for calculating and analyzing food demand that is responsive to changing incomes and prices

The ambrosia R package was developed to calculate food demand for staples and non-staple commodities that is responsive to changing levels of incomes and prices. ambrosia implements the framework to quantify food demand as established by Edmonds et al. (Edmonds et al. 2017) and allows the user to explore and estimate different variables related to the food demand system. Currently ambrosia provides 3 main functions: (1) calculation of food demand for any given set of parameters including income levels and prices (2) estimation of parameters within a given a dataset. Note: ambrosia is used to calculate parameters for the food demand model implemented in the Global Change Analysis Model(GCAM) (3) exploration and preparation of raw data before starting a parameter estimation

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Changes in When and Where People are Spending Time in Response to COVID-19

The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

SPS market analysis

A market analysis task included personal interviews by GE personnel and supplemental mail surveys to acquire statistical data and to identify and measure attitudes, reactions and intentions of prospective small solar thermal power systems (SPS) users. Over 500 firms were contacted, including three ownership classes of electric utilities, industrial firms in the top SIC codes for energy consumption, and design engineering firms. A market demand model was developed which utilizes the data base developed by personal interviews and surveys, and projected energy price and consumption data to perform sensitivity analyses and estimate potential markets for SPS.

Goff, H. C.↗
NASA NTRS1981

Effects off system factors on the economics of and demand for small solar thermal power systems

Market penetration as a function time, SPS performance factors, and market/economic considerations was estimated, and commercialization strategies were formulated. A market analysis task included personal interviews and supplemental mail surveys to acquire statistical data and to identify and measure attitudes, reactions and intentions of prospective SPS users. Interviews encompassed three ownership classes of electric utilities and industrial firms in the SIC codes for energy consumption. A market demand model was developed which utilized the data base developed, and projected energy price and consumption data to perform sensitivity analyses and estimate potential market for SPS.

Source record↗
NASA NTRS1986

Towards effective interactive three-dimensional colour postprocessing

Recommendations for the development of effective three-dimensional, graphical color postprocessing are made. First, the evaluation of large, complex numerical models demands that a postprocessor be highly interactive. A menu of available functions should be provided and these operations should be performed quickly so that a sense of continuity and spontaneity exists during the post-processing session. Second, an agenda for three-dimensional color postprocessing is proposed. A postprocessor must be versatile with respect to application and basic algorithms must be designed so that they are flexible. A complete selection of tools is necessary to allow arbitrary specification of views, extraction of qualitative information, and access to detailed quantitative and problem information. Finally, full use of advanced display hardware is necessary if interactivity is to be maximized and effective postprocessing of today's numerical simulations is to be achieved.

Bailey, B. C.↗
NASA NTRS1990

The California corridor transportation system: A design summary

A design group was assembled to find and research criteria relevent to the design of a California Corridor Transportation System. The efforts of this group included defining the problem, conducting a market analysis, formulation of a demand model, identification and evaluation of design drivers, and the systematic development of a solution. The problems of the current system were analyzed and used to determine design drivers, which were divided into the broad categories of cost, convenience, feasibility, environment, safety, and social impact. The relative importance of individual problems was addressed, resulting in a hierarchy of design drivers. Where possible, methods of evaluating the relative merit of proposed systems with respect to each driver were developed. Short takeoff vertical landing aircraft concepts are also discussed for supersonic fighters.

Source record↗
NASA NTRS1997

Large-Eddy Simulation of a High Reynolds Number Flow Around a Cylinder Including Aeroacoustic Predictions

The dynamic subgrid-scale model is employed in large-eddy simulations of flow over a cylinder at a Reynolds number, based on the diameter of the cylinder, of 90,000. The Centric SPECTRUM(trademark) finite element solver is used for the analysis. The far field sound pressure is calculated from Lighthill-Curle's equation using the computed fluctuating pressure at the surface of the cylinder. The sound pressure level at a location 35 diameters away from the cylinder and at an angle of 90 deg with respect to the wake's downstream axis was found to have a peak value of approximately 110 db. Slightly smaller peak values were predicted at the 60 deg and 120 deg locations. A grid refinement study suggests that the dynamic model demands mesh refinement beyond that used here.

Spyropoulos, Evangelos T.↗
NASA NTRS2003

The Conference Proceedings of the 2003 Air Transport Research Society (ATRS) World Conference, Volume 5

The UNO Aviation Institute Monograph Series began in 1994 as a key component of the education outreach and information transfer missions of the Aviation Institute and the NASA Nebraska Space Grant & EPSCoR Programs. The series is an outlet for aviation materials to be indexed and disseminated through an efficient medium. Publications are welcome in all aspects of aviation. Publication formats may include, but are not limited to, conference proceedings, bibliographies, research reports, manuals, technical reports, and other documents that should be archived and indexed for future reference by the aviation and world wide communities. The Conference proceedings of the 2003 Air Transport Research Society (ATRS) world conference, volume 5 is presented. The topics include: 1) The Temporal Configuration of Airline Networks in Europe; 2) Determination and Applications of Environmental Costs at Different Sized Airports-Aircraft Noise and Engine Emissions; 3) Cost Effective Measures to Reduce CO2 Emissions in the Air Freight Sector; 4) An Assessment of the Sustainability of Air Transport System: Quantification of Indicators; 5) Regulation, Competition and Network Evolution in Aviation; 6) Regulation in the Air: Price and Frequency Cap; 7) Industry Consolidation and Future Airline Network Structures in Europe; 8) Application of Core Theory to the U.S. Airline Industry; 9) Air Freight Transshipment Route Choice Analysis; 10) A Fuzzy Approach of the Competition on Air Transport Market; and 11) Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair.

Bowen, Brent↗
NASA NTRS2009

NASA Subsonic Rotary Wing Project

This slide presentation will outline the rationale for, and the initial results of, a contractor study being performed by a SAIC-led team of Bell Helicopter Textron, Sensis, and Optimal Synthesis. Together, this team represents an extensive body of subject matter expertise as related to rotorcraft technologies and design, airspace demand modeling simulation, and terminal area operations and flight path planning. The initial conceptual design results of a fleet of civil tiltrotor aircraft ranging in size from 10 to 120 passengers is a key highlight of the work to be presented. The intent of this presentation is to begin to provide the general community of rotorcraft researchers, manufacturers, and end-users an appreciation of the criticality of interjecting rotorcraft-specific technology and concepts of operation issues into Next Gen airspace requirements

Young, Larry A.↗
DOE OSTI2025

Dynamic Low-Rank Training with Spectral Regularization: Achieving Robustness in Compressed Representations

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94\% compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2025

Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging

Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughput environments, such as the CERN LHC. The quadratic complexity of transformer models demands substantial resources and increases latency during inference. In order to address these issues, we introduce the Spatially Aware Linear Transformer (SAL-T), a physics-inspired enhancement of the linformer architecture that maintains linear attention. Our method incorporates spatially aware partitioning of particles based on kinematic features, thereby computing attention between regions of physical significance. Additionally, we employ convolutional layers to capture local correlations, informed by insights from jet physics. In addition to outperforming the standard linformer in jet classification tasks, SAL-T also achieves classification results comparable to full-attention transformers, while using considerably fewer resources with lower latency during inference. Experiments on a generic point cloud classification dataset (ModelNet10) further confirm this trend. Our code is available at https://github.com/aaronw5/SAL-T4HEP.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗