SEARCH · Search NASA

Results for “Consumer vehicle data”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Holistic Transportation and Energy Modeling

This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗

DOE OSTI2026

2019 California Vehicle Survey

The 2019 California Vehicle Survey of residential and commercial light-duty fleet owners in California assessed consumer preferences for vehicles and included a targeted sample of plug-in electric vehicle (PEV) owners. In addition to economic and demographic data, the survey integrated light-duty vehicle holding and use information with vehicle choice data, which was collected via a set of eight exercises on vehicle and fuel type choice. The PEV owner survey participants provided additional data on charging behavior, electricity rates, and their main motivations for purchasing PEVs.

1Hz data↗

NASA NTRSDate not supplied

Mapping the ISS with the Autonomous Free-Flying Astrobee Robots

Space intra-vehicular robots (IVR) can provide coverage survey and targeted close-up inspection imagery capabilities that improve ground operator situation awareness. We report on a series of activities using the autonomous Astrobee robotic free-flyers that mapped the interior of three ISS modules, generating a panoramic tour of the ISS interior without astronaut manual photography. For the ISS, these capabilities can complement or replace time-consuming crew inspection tasks, providing panoramic data products that are easier to navigate than the baseline crew safety videos. For future exploration vehicles with extended uncrewed mission phases, robotic inspection will play a more critical role of providing the primary “eyes in the sky” for vehicle operators.

robotics↗

DOE OSTI2021

A Deterministic Multivariate Clustering Method for Drive Cycle Generation from In-Use Vehicle Data

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data. This paper explores the development and initial validation of a method of generating representative drive cycles from large collections of real-world vehicle data using a deterministic multivariate clustering approach. Starting with theory and diving into the methodology behind representative cycle generation, the paper aims to also present graphical and tabular results of initial validation via vehicle simulation and chassis dynamometer testing. Additional topics for further research and areas for ongoing development will also be presented.

47 OTHER INSTRUMENTATION↗

NASA NTRS2009

Modeling and Simulation of Variable Mass, Flexible Structures

The advent of the new Ares I launch vehicle has highlighted the need for advanced dynamic analysis tools for variable mass, flexible structures. This system is composed of interconnected flexible stages or components undergoing rapid mass depletion through the consumption of solid or liquid propellant. In addition to large rigid body configuration changes, the system simultaneously experiences elastic deformations. In most applications, the elastic deformations are compatible with linear strain-displacement relationships and are typically modeled using the assumed modes technique. The deformation of the system is approximated through the linear combination of the products of spatial shape functions and generalized time coordinates. Spatial shape functions are traditionally composed of normal mode shapes of the system or even constraint modes and static deformations derived from finite element models of the system. Equations of motion for systems undergoing coupled large rigid body motion and elastic deformation have previously been derived through a number of techniques [1]. However, in these derivations, the mode shapes or spatial shape functions of the system components were considered constant. But with the Ares I vehicle, the structural characteristics of the system are changing with the mass of the system. Previous approaches to solving this problem involve periodic updates to the spatial shape functions or interpolation between shape functions based on system mass or elapsed mission time. These solutions often introduce misleading or even unstable numerical transients into the system. Plus, interpolation on a shape function is not intuitive. This paper presents an approach in which the shape functions are held constant and operate on the changing mass and stiffness matrices of the vehicle components. Each vehicle stage or component finite element model is broken into dry structure and propellant models. A library of propellant models is used to describe the distribution of mass in the fuel tank or Solid Rocket Booster (SRB) case for various propellant levels. Based on the mass consumed by the liquid engine or SRB, the appropriate propellant model is coupled with the dry structure model for the stage. Then using vehicle configuration data, the integrated vehicle model is assembled and operated on by the constant system shape functions. The system mode shapes and frequencies can then be computed from the resulting generalized mass and stiffness matrices for that mass configuration. The rigid body mass properties of the vehicle are derived from the integrated vehicle model. The coupling terms between the vehicle rigid body motion and elastic deformation are also updated from the constant system shape functions and the integrated vehicle model. This approach was first used to analyze variable mass spinning beams and then prototyped into a generic dynamics simulation engine. The resulting code was tested against Crew Launch Vehicle (CLV-)class problems worked in the TREETOPS simulation package and by Wilson [2]. The Ares I System Integration Laboratory (SIL) is currently being developed at the Marshall Space Flight Center (MSFC) to test vehicle avionics hardware and software in a hardware-in-the-loop (HWIL) environment and certify that the integrated system is prepared for flight. The Ares I SIL utilizes the Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) tool to simulate the launch vehicle and stimulate avionics hardware. Due to the presence of vehicle control system filters and the thrust oscillation suppression system, which are tuned to the structural characteristics of the vehicle, ARTEMIS must incorporate accurate structural models of the Ares I launch vehicle. The ARTEMIS core dynamics simulation models the highly coupled nature of the vehicle flexible body dynamics, propellant slosh, and vehicle nozzle inertia effects combined with mass and flexible body properties that vary significant with time during the flight. All forces that act on the vehicle during flight must be simulated, including deflected engine thrust force, spatially distributed aerodynamic forces, gravity, and reaction control jet thrust forces. These forces are used to excite an integrated flexible vehicle, slosh, and nozzle dynamics model for the vehicle stack that simulates large rigid body translations and rotations along with small elastic deformations. Highly effective matrix math operations on a distributed, threaded high-performance simulation node allow ARTEMIS to retain up to 30 modes of flex for real-time simulation. Stage elements that separate from the stack during flight are propagated as independent rigid six degrees of freedom (6DOF) bodies. This paper will present the formulation of the resulting equations of motion, solutions to example problems, and describe the resulting dynamics simulation engine within ARTEMIS.

Tobbe, Patrick A.↗

NASA NTRS1977

High pressure gas storage system consumable analyses for ALT flights free flight 1 and free flight 6

Consumables analyses are presented for the high pressure gas storage oxygen (O2), and hydrogen (H2) reactant systems for orbiter vehicle 101 approach and landing tests, for two flights, free flight 1 and free flight 6 are given. The consumables analyses are based on average power data. The required system and mission data updates were made in order to perform the analyses, notably the thermal environment profiles of the reactant storage cylinders and the power profile for the Electrical power subsystem. No mission-dependent environment profiles were provided; therefore, nominal free flight mission profile was used to generate environment profiles for free flights 1 and 6.

Hurst, J. E.↗

NASA NTRS1998

Vehicle and Mission Design Options for the Human Exploration of Mars/Phobos Using "Bimodal" NTR and LANTR Propulsion

The nuclear thermal rocket (NTR) is one of the leading propulsion options for future human missions to Mars because of its high specific impulse (Isp-850-1000 s) capability and its attractive engine thrust-to-weight ratio (approximately equal 3-10). To stay within the available mass and payload volume limits of a "Magnum" heavy lift vehicle, a high performance propulsion system is required for trans-Mars injection (TMI). An expendable TMI stage, powered by three 15 thousand pounds force (klbf) NTR engines is currently under consideration by NASA for its Design Reference Mission (DRM). However, because of the miniscule burnup of enriched uranium-235 during the Earth departure phase (approximately 10 grams out of 33 kilograms in each NTR core), disposal of the TMI stage and its engines after a single use is a costly and inefficient use of this high performance stage. By reconfiguring the engines for both propulsive thrust and modest power generation (referred to as "bimodal" operation), a robust, multiple burn, "power-rich" stage with propulsive Mars capture and reuse capability is possible, A family of modular "bimodal" NTR (BNTR) vehicles are described which utilize a common "core" stage powered by three 15 klbf BNTRs that produce 50 kWe of total electrical power for crew life support, an active refrigeration / reliquification system for long term, "zero-boiloff" liquid hydrogen (LH 2 ) storage, and high data rate communications. An innovative, spine-like "saddle truss" design connects the core stage and payload element and is open underneath to allow supplemental "in-line" propellant tanks and contingency crew consumables to be easily jettisoned to improve vehicle performance. A "modified" DRM using BNTR transfer vehicles requires fewer transportation system elements, reduces IMLEO and mission risk, and simplifies space operations. By taking the next logical step--use of the BNTR for propulsive capture of all payload elements into Mars orbit--the power available in Mars orbit grows to 150 kWe compared to 30 kWe for the DRM. Propulsive capture also eliminates the complex, higher risk aerobraking and capture maneuver which is replaced by a simpler reentry using a standardized, lower mass "aerodescent" shell. The attractiveness of the "all BNTR" option is further increased by the substitution of the lightweight, inflatable "TransHab" module in place of the heavier, hard-shell hab module. Use of TransHab introduces the potential for propulsive recovery and reuse of the BNTR/ERV. It also allows the crew to travel to and from Mar on the same BNTR transfer vehicle thereby cutting the duration of the ERV mission in half--from approximately 4.7 to 2.5 years. Finally, for difficult Mars options, such as Phobos rendezvous and sample return missions, volume (not mass) constraints limit the performance of the "all LH 2 " BNTR stage. The use of "LOX-augmented" NTR (LANTR) engines, operating at a modest oxygen-to-hydrogen mixutre ratio (MR) of 0.5, helps to increase "bulk" propellant density and total thrust during the TMI burn. On all subsequent burns, the bimodal LANTR engines operate on LH 2 only (MR=0) to maximize vehicle performance while staying within the lift capability of two Magnum launches.

Nuclear thermal rocket↗

NASA NTRS2002

Vehicle and Mission Design Options for the Human Exploration of Mars/Phobos Using "Bimodal" NTR and LANTR Propulsion

The nuclear thermal rocket (NTR) is one of the leading propulsion options for future human missions to Mars because of its high specific impulse (lsp~850-1000 s) capability and its attractive engine thrust-to-weight ratio (~3-10). To stay within the available mass and payload volume limits of a “Magnum” heavy lift vehicle, a high performance propulsion system is required for trans-Mars injection (TMI). An expendable TMI stage, powered by three 15 thousand pounds force (klbf) NTR engines is currently under consideration by NASA for its Design Reference Mission (DRM). However, because of the miniscule burnup of enriched uranium-235 during the Earth departure phase (~10 grams out of 33 kilograms in each NTR core), disposal of the TMI stage and its engines after a single use is a costly and inefficient use of this high performance stage. By reconfiguring the engines for both propulsive thrust and modest power generation (referred to as “bimodal” operation), a robust, multiple burn, “power-rich” stage with propulsive Mars capture and reuse capability is possible. A family of modular “bimodal” NTR (BNTR) vehicles are described which utilize a common “core” stage powered by three 15 klbf BNTRs that produce 50 kWe of total electrical power for crew life support, an active refrigeration / reliquification system for long term, zero-boiloff liquid hydrogen (LH 2 ) storage, and high data rate communications. An innovative, spine-like “saddle truss” design connects the core stage and payload element and is open underneath to allow supplemental “in-line” propellant tanks and contingency crew consumables to be easily jettisoned to improve vehicle performance. A “modified” DRM using BNTR transfer vehicles requires fewer transportation system elements, reduces IMLEO and mission risk, and simplifies space operations. By taking the next logical step—use of the BNTR for propulsive capture of all payload elements into Mars orbit—the power available in Mars orbit grows to 150 kWe compared to 30 kWe for the DRM. Propulsive capture also eliminates the complex, higher risk aerobraking and capture maneuver which is replaced by a simpler reentry using a standardized, lower mass “aerodescent” shell. The attractiveness of the “all BNTR” option is further increased by the substitution of the lightweight, inflatable “TransHab” module in place of the heavier, hard-shell hab module. Use of TransHab introduces the potential for propulsive recovery and reuse of the BNTR / Earth return vehicle (ERV). It also allows the crew to travel to and from Mars on the same BNTR transfer vehicle thereby cutting the duration of the ERV mission in half—from ~4.7 to 2.5 years. Finally, for difficult Mars options, such as Phobos rendezvous and sample return missions, volume (not mass) constraints limit the performance of the “all LH 2 ” BNTR stage. The use of “LOX-augmented” NTR (LANTR) engines, operating at a modest oxygen-to-hydrogen mixture ratio (MR) of 0.5, helps to increase “bulk” propellant density and total thrust during the TMI burn. On all subsequent burns, the bimodal LANTR engines operate on LH 2 only (MR=0) to maximize vehicle performance while staying within the mass limits of two Magnum launches.

Nuclear thermal rocket↗

DOE OSTI2020

Alternative Fuels Data Center and Related Technology Integration Tools

The U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy's Vehicle Technologies Office (VTO) facilitates national coordination of the Clean Cities coalitions through its Technology Integration Program. VTO's Technology Integration Program supports a broad technology portfolio that can reduce transportation energy costs for businesses and consumers. Fleets, cities, and regions can use the tools featured in this guide to implement alternative fuels and advanced vehicles. Also learn how Clean Cities coalitions and their stakeholders across the country have used the tools.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Applying novel analytical tools for analyzing multidimensional secondary organic aerosol measurements

In the atmosphere, secondary organic aerosols (SOA) are often the major components of fine particulate matter and interact with clouds and radiation. SOA comprises a mixture of thousands of organic compounds. There is tremendous complexity and uncertainty in understanding SOA formation, since it is formed by oxidation and gas to particle conversion of a variety of sources: natural biogenic, anthropogenic (vehicles, cooking coal combustion) and biomass burning. The Aerosol Mass Spectrometer (AMS) produces multidimensional chemical information about SOA but analyzing this data to understand SOA sources relies on time consuming analyses (~months to years) such as the positive matrix factorization (PMF). PMF also becomes difficult for aircraft data where signal to noise ratio is weaker. There is a critical need to develop fast machine learning techniques that can analytically provide information about SOA sources using AMS data on the same timescales as the data is being collected (~minutes). We apply a machine learning supervised classification approach: the multinomial logistic regression to rapidly classify AMS data obtained from aircraft measurements.

47 OTHER INSTRUMENTATION↗

NASA NTRS1986

Using PCDS to study the influence of UV flux variation on the middle atmosphere

The value of the Pilot Climate Data System (PCDS) in examining the influences of short-period solar flux variations on the middle atmosphere was demonstrated. Several Nimbus satellite data sets proposed for the study exist in the PCDS. Planned for retrieval through the PCDS are ozone mixing ratios and cumulative ozone profiles available from the Backscatter Ultraviolet Spectrophotometer on Nimbus-4. Also to be accessed are Nimbus-7 data sets that will provide ozone and nitrogen dioxide mixing ratios, temperature profiles from the Limb Infrared Monitor of the Stratosphere, and ozone mixing ratio profiles from the Solar Backscatter Ultraviolet Spectrophotometer. Bypassing the time-consuming process of reading raw data tapes, the researchers plan to transfer the PCDS processed data to an IBM PC at Iowa State University. The IBM PC will serve as an intermediate vehicle for transferring the data to the NOAA CYBER 840 at Boulder, Colorado, where research will continue on both an eight-layer radiative-photochemical numerical model and on a nonlinear dynamical model. in this research are the FGGE and ERB data sets. These relevant data sets also reside in the PCDS. Problems associated with remote access to the PCDS were discussed in regard to all the PCDS data sets.

Heilman, W.↗

NASA NTRS2004

Rapid Structured Volume Grid Smoothing and Adaption Technique

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reduction in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

Alter, Stephen J.↗

NASA NTRS2006

Rapid Structured Volume Grid Smoothing and Adaption Technique

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reductions in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

Alter, Stephen J.↗

DOE OSTI2020

Alternative Fuels Data Center and Related Technology Integration Tools

AFDC↗

DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Varela, Sebastian↗