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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Flight-Test Approach for X-57 Mod II Configuration

The primary objective of the X-57 Mod II test flights is to perform a functional checkout of the research traction battery power and motor systems, perform an airworthiness assessment of the modified vehicle, and to collect baseline performance, stability, and acoustic data for comparisons with later modifications of the X-57 airplane. This paper describes the planned flight operations of the Mod II airplane, the unique considerations and decisions made in operating an electric aircraft, and the development and planning of the research test points necessary to achieve the Mod II flight objectives. Due to the constrained flight time, which resulted from limited capacity of the battery system, careful scripting of flights was required to maximize test time and execute test points efficiently.

James Reynolds↗
DOE OSTI2020

The energy footprint of automotive electronic sensors

We discuss how Information and communication technologies (ICT) have emerged as one of the leading technologies to reduce global emissions, particularly in the mobility sector. Automotive electronics, such as sensors/actuators and microcontrollers (commonly known as electronic control units (ECU) which control one or more of the electrical systems or subsystems in a vehicle), play a key role in ICT. Sensors/actuators are key in electronic ICT devices, starting with the data collection and data communication with the internet. The latest two big trends of electrification and automation in vehicles, are projected to increase the use of worldwide automotive sensors from 7.5 billion units in 2017 to 11.0 billion units by the year 2024. A representative state-of-the art automotive sensor system, (i.e., an ultrasonic backup system), including the ECUs controlling the vehicle electrical systems/subsystems, has been considered to estimate the energy footprint in terms of manufacturing and operational energy of global automotive sensors use. A widely used life cycle energy assessment method (i.e., cumulative energy demand) was used as both direct and indirect (including the extraction, manufacturing, and disposal of the raw and auxiliary materials) energy use can be considered for the energy footprint estimation. The embodied manufacturing energy impacts of the system was estimated to be 559 MJ/system, compared to the 417 MJ/system for lifetime system power and additional gasoline use. The share of purchased energy to the embodied energy where the upstream energy isn't included in the former case, is less than 10% and ~ 85% for the component manufacturing and vehicle operation energy uses, respectively. As the purpose of this ultrasonic backup system is to prevent rear crashes, an estimated 1.0 MJ/system is avoided from reduced lifetime vehicular repairs (from an estimated 11% chance of requiring a rear bumper replacement). While all of this is small compared to the overall automotive manufacturing and use energy, the 11 billion automotive sensors expected to be produced in 2024 could require 1540 PJ for manufacturing and those sensors would require an additional 780–1150 PJ for lifetime energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Spline Trajectory Algorithm Development: Bezier Curve Control Point Generation for UAVs

A greater need for sophisticated autonomous piloting systems has risen in direct correlation with the ubiquity of Unmanned Aerial Vehicle (UAV) technology. Whether surveying unknown or unexplored areas of the world, collecting scientific data from regions in which humans are typically incapable of entering, locating lost or wanted persons, or delivering emergency supplies, an unmanned vehicle moving in close proximity to people and other vehicles, should fly smoothly and predictably. The mathematical application of spline interpolation can play an important role in autopilots' on-board trajectory planning. Spline interpolation allows for the connection of Three-Dimensional Euclidean Space coordinates through a continuous set of smooth curves. This paper explores the motivation, application, and methodology used to compute the spline control points, which shape the curves in such a way that the autopilot trajectory is able to meet vehicle-dynamics limitations. The spline algorithms developed used to generate these curves supply autopilots with the information necessary to compute vehicle paths through a set of coordinate waypoints.

Howell, Lauren R.↗
NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Neural Networks↗
DOE OSTI2026

VECTOR Phase 1 Dataset: CAV Trajectory and Energy Consumption Records

This dataset contains benchmark experimental data from Phase 1 of the VECTOR project, focusing on the energy impact of CAV hardware components. The dataset includes vehicle trajectory data (speed and position) and corresponding energy consumption records collected from a CAV platform equipped with lidar, cameras, onboard computation units, and communication modules. The primary objective is to quantify the baseline energy consumption attributable to sensing and computing systems, independent of any advanced cooperative control strategies. During experiments, the leading vehicle followed a predetermined velocity profile, and the following CAV mirrored this trajectory using a basic car-following control to ensure consistent driving behavior. This setup enables a reliable benchmark for assessing the energy cost introduced by onboard CDA hardware (e.g., lidar and GPU-based processing). The dataset is essential for evaluating energy baselines and supports future comparative studies involving additional cooperative strategies. ![system img](system.png) ![vector img](vector.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2003

Development and Flight Testing of an Adaptable Vehicle Health-Monitoring Architecture

Development and testing of an adaptable wireless health-monitoring architecture for a vehicle fleet is presented. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained adaptable expert system. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate, and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear.

Woodard, Stanley E.↗
DOE OSTI2026

EV Watts Public Database

With the rapid increase in vehicle electrification, there is a need for up-to-date, publicly available national data to understand end user charging and driving patterns, as well as vehicle and infrastructure performance, to inform research planning. Energetics worked with various partners to collect and analyze plug-in electric vehicle (PEV) and electric vehicle supply equipment (EVSE) data from 2019 to 2022. All sensitive attributes have been removed from this publicly available dataset. Researchers from one of the partner national labs under non-disclosure agreement (NDA) can request access to additional attributes by reaching out to evwattsdata@energetics.com.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2002

A Ground-Based Research Vehicle for Base Drag Studies at Subsonic Speeds

A ground research vehicle (GRV) has been developed to study the base drag on large-scale vehicles at subsonic speeds. Existing models suggest that base drag is dependent upon vehicle forebody drag, and for certain configurations, the total drag of a vehicle can be reduced by increasing its forebody drag. Although these models work well for small projectile shapes, studies have shown that they do not provide accurate predictions when applied to large-scale vehicles. Experiments are underway at the NASA Dryden Flight Research Center to collect data at Reynolds numbers to a maximum of 3 x 10(exp 7), and to formulate a new model for predicting the base drag of trucks, buses, motor homes, reentry vehicles, and other large-scale vehicles. Preliminary tests have shown errors as great as 70 percent compared to Hoerner's two-dimensional base drag prediction. This report describes the GRV and its capabilities, details the studies currently underway at NASA Dryden, and presents preliminary results of both the effort to formulate a new base drag model and the investigation into a method of reducing total drag by manipulating forebody drag.

Diebler, Corey↗
NASA NTRSDate not supplied

NASA’s Space Launch System: Artemis I Results and the Path Forward

On Nov. 16, 2022, NASA launched the Artemis I mission on the agency’s new SLS (Space Launch System) rocket. The debut launch of the integrated vehicle sent an uncrewed Orion spacecraft into a distant retrograde (DRO) orbit about the Moon to accomplish multiple mission objectives, including evaluating Orion’s heatshield at lunar return velocities and collecting critical data from the first flight of the new superheavy lift launch vehicle. Post-flight data analyses show that the rocket performed with a high degree of precision and accuracy. In addition to completing testing and launch of the Artemis I SLS in 2022, notable progress was made on hardware for future launch vehicles that will support Artemis missions II, III, IV, and V. The Artemis II core stage is mostly complete, and its RS-25 engines were integrated in the fall of 2023. The Artemis II solid rocket booster motor segments were shipped to the launch site in September 2023. Propellant tanks are being manufactured for additional missions, solid booster segments are being cast and prepared, software is being developed, and production of new RS-25 engines is underway. A more capable upper stage is in development, as well as a large adapter to encapsulate a 10-metric ton (t) co-manifested payload. This paper will provide details on the Artemis I flight as well as cover hardware progress for the future flights as available.

John Honeycutt↗
NASA NTRS1977

Design of satellite flexibility experiments

A preliminary study has been completed to begin development of a flight experiment to measure spacecraft control/flexible structure interaction. The work reported consists of two phases: identification of appropriate structural parameters which can be associated with flexibility phenomena, and suggestions for the development of an experiment for a satellite configuration typical of near-future vehicles which are sensitive to such effects. Recommendations are made with respect to the type of data to be collected and instrumentation associated with these data. The approach consists of developing the equations of motion for a vehicle possessing a flexible solar array, then linearizing about some nominal motion of the craft. A set of solutions are assumed for array deflection using a continuous normal mode method and important parameters are exposed. Inflight and ground based measurements are distinguished. Interrelationships between these parameters, measurement techniques, and input requirements are discussed which assure minimization of special vehicle maneuvers and optimization of data to be obtained during the normal flight sequence.

Kaplan, M. H.↗
NASA NTRSDate not supplied

How To Train Your Dragon (and Vice Versa) – Human Spaceflight Research and The First Commercial Crews

The International Space Station has been an orbiting laboratory for human spaceflight research for over 20 years. NASA’s human research implementation teams have worked to develop, refine, and execute pre-, in-, and post-flight sessions to collect critical research data that will enable future spaceflight missions. The teams have worked closely with the spaceflight vehicle teams, including those for Soyuz and the now retired Space Shuttle vehicles, to successfully conduct this research. As NASA has transitioned to commercial vehicles for transport of crews to the ISS, the implementation teams have expanded their scope to include the SpaceX and upcoming Boeing vehicles. This session will focus on the unique implementation needs for the first available commercial platform, SpaceX. We will identify implementation adjustments made to accommodate the SpaceX Dragon system, including crew training and preflight baseline data collection, inflight operations tied to landing, and postflight planning and crew testing, as well as offer lessons learned from the past three SpaceX long-duration crew missions. Representatives from the Flight Operations Directorate, including the Astronaut Office and the vehicle recovery team, will also provide their perspectives on this new system, and how it affects their work with the human research community. A panel discussion with dedicated time for Q&A will follow.

Pasha Morshedi↗
NASA NTRSDate not supplied

NASA’s Space Launch System: Powering Artemis and Enabling Discovery

The debut launch of NASA’s super-heavy lift SLS (Space Launch System) rocket in November 2022 opened a new era of deep space exploration. Called the Artemis I mission, the launch sent an uncrewed Orion spacecraft more than 268,000 miles from Earth into a distant retrograde orbit (DRO) about the Moon, before splashing down in the Pacific Ocean 25 days later. While the mission was critical for multiple reasons to NASA’s Artemis campaign, the data collected during the first launch of SLS was vital to understanding how the launch vehicle performs in the flight environment. The test flight provided real-world data to ground and improve models, on-ground testing, and vehicle performance. Data returned show a nominal launch, with many parameters accurate to pre-flight predictions to within tenths of a percent. Equipped with the data, the SLS team is readying the Artemis II vehicle. Beginning with the fourth flight, SLS variants will have multiple upgrades, which increase the capability of the rocket to enable the nation’s most ambitious missions of returning astronauts to the Moon and lunar vicinity. Current hardware progress, as well as Artemis I flight data, will be provided. Additionally, the test programs that enabled the successful first flight will be discussed.

John Honeycutt↗
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Introduction - Paradigm shift from one operator supervising a single vehicle, to an operator supervising multiple highly automated vehicles (One-to-Many) - One-to-Many application - Search and Rescue - Foraging - Military ops - Etc. - Calibrated trust in automation enables human operators to effectively manage highly automated vehicles - Past studies show that trust mediates relationship between reliability and dependence (Chancey et al., 2017; Chancey et al., 2015) - Future studies needed to further understand relationship

trust↗
NASA NTRS1979

Aircrew displays and avionics for application in a future national airspace system

A concept for increased pilot involvement in a future National Airspace System was evolved during the FAA New Initiatives in Engineering and Development Users responsibilities and ways in which they might interact. The technical feasibility of the system is indicated by the sophisticated level of presently manufactured digital computers and display avionics, and the application of that technology under design by the major airframe manufacturers. Data collected during simulations and flights with the Terminal Configured Vehicle Program B-737 airplane are shown to have direct application to the new system concept. The adoption of the operational changes envisioned, offers some potentially significant advantages to the user.

Salmirs, S.↗
NASA NTRS1993

Compact Robotic Vehicle

Radio-controlled microrover features light weight and agility. Miniature robotic vehicle, called Go-For, implements new fork-wheeled mobility concept to traverse extremely rough terrain. Weighs 4 kg and is 0.4 m long, climbs over obstacles as large as 60 percent of its length. Mobility concept applied to much larger vehicles. Demonstrates such applications as exploration of planetary surfaces, military surveillance, and assessing hazardous situations. Video camera on vehicle sends images to control station, where human supervisor chooses sequence of paths to traverse to reach locations of interest. For planetary exploration, spectrometer and seisometer on vehicle sends scientific data to control station, and onboard tools collect soil and rock samples. Terrestrial version equipped similarly to take samples in chemically and/or biologically contaminated areas.

Wilcox, Brian H.↗
NASA NTRS1998

Terrier-Black Brant Sounding Rocket Microacceleration Environment

On September 10, 1997 a Terrier-Black Brant Sounding Rocket was launched to support the Lewis Research Center DARTFire combustion experiment at the White Sands Missile Range in New Mexico. The sounding rocket also carried two microacceleration measurement systems to support the DARTFire experiment by measuring the microacceleration environment. The SAMS-FF system and a SAMS-type triaxial sensor head (TSH) were flown. The SAMS-FF system consisted of a variable frequency, digital output TSH and a roll rate sensor. Data from the two systems were analyzed and compared. Data will be presented from both systems in order to characterize the sounding rocket microacceleration environment. The data collected demonstrated that a sounding rocket is a viable vehicle for conducting space experiments that require a quiet, dedicated microacceleration environment.

Kacpura, Thomas↗
NASA NTRS2004

Lightning Optical Pulse Statistics from Storm Overflights During the Altus Cumulus Electrification Study

The Altus Cumulus Electrification Study (ACES) was conducted during the month of August, 2002 in an area near Key West, Florida. One of the goals of this uninhabited aerial vehicle (UAV) study was to collect time resolved optical pulse data from thunderstorms. During the month long campaign, we acquired 5294 lightning generated optical pulses. Most of these observations were made while close to the top of the storms. We divided our data into two amplitude groups based on prior NASA U2 aircraft optical data and our pulse characteristics. The group of large pulses with radiance greater than 2.1 mW /sq m sr had mean and median 10 - 10% optical pulse widths of 765 and 735 microns respectively, the 50-50% pulse widths of 396 and 355 microns respectively, and 10-90% rise times of 290 and 260 microns. These values are very similar to the previous U2 based optical results The other group of pulses consisting of slightly more than a quarter of the total pulses observed had radiances less than the minimum values detected in the U2 study. The small pulses were narrower than the large pulses with 5040% mean and median values of 198 and 160 ps respectively. Only 12 % of the flashes contained only small pulses, minimizing the impact of this data on the estimates of detection efficiencies of the orbital instruments, the Lightning Imaging Sensor and Optical Transient Detector.

Mach, D. M.↗