Results for “vehicle data” — Page 64

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. Crew members operating independently during long duration space exploration missions will require a clinical decision support system (CDSS) to increase autonomy by augmenting their knowledge, skills and abilities in different scenarios. Significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities are needed to increase crew autonomy and self-reliance in decision making and task performance. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. Clinical decision support (CDS) presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. A comprehensive crew health and performance CDSS is required to augment crew capability and will be used in different scenarios for several reasons. In general, the CDSS’s role is to assist the crew in prevention, detection, diagnosis and treatment of crew health and performance related conditions that may arise in exploration spaceflight. For example, CDSS would assist a high acuity scenario such as a heart attack by supplying clear instructions, vital signs and treatment reminders. A lower severity scenario such as kidney stone risk could interface to vehicle systems and display more complex predictive data during a diagnosis. A CDSS needs to contribute to successful missions by maintaining a high performing crew who can potentially exhibit countless medical conditions related to derangements from the space environment (sleep, cognition, nutrition and exercise) as well as conditions intrinsic to humans anywhere. While supporting the crew’s ability to make sound clinical decisions is desirable in any mission, it is essential for exploration missions with significant communication delays, no evacuation capability, and extended exposure to the flight environment. Such missions correspond with medical Level of Care V (LOC V), the highest level specified in NASA-STD-3001. The project focuses on CDS implementation research to derive requirements for LOC V, where the need for increased autonomy results in new practices and the inclusion of non-clinical data, such as vehicle environmental measures and physical exercise results, from other human and vehicle domains and advanced analytics. The CDS project describes how the CDSS is intended to be used by defining concepts of operations (ConOps). The process to derive ConOps focuses on increased autonomy that reduces the likelihood and consequences of accepted medical conditions. These crew health and performance inputs are grouped by common datasets and analysis models. Use cases are derived to research new clinical scenarios, architectural development and workflows. Implementation research is conducted with protypes to inform assumptions and derive requirements. The project also establishes how externally developed analysis and approaches can be added to expand a clinical decision support system and thus highlight how a comprehensive system can be globally developed with collaborators. This presentation will cover example scenarios from the CDS ConOps and the method to derive them. One example scenario will be CDSS alerting an increased kidney stone risk during a mission, with diagnosis and treatment options provided during the intervention.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. Crew members operating independently during long duration space exploration missions will require a clinical decision support system (CDSS) to increase autonomy by augmenting their knowledge, skills and abilities in different scenarios. Significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities are needed to increase crew autonomy and self-reliance in decision making and task performance. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. Clinical decision support (CDS) presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. A comprehensive crew health and performance CDSS is required to augment crew capability and will be used in different scenarios for several reasons. In general, the CDSS’s role is to assist the crew in prevention, detection, diagnosis and treatment of crew health and performance related conditions that may arise in exploration spaceflight. For example, CDSS would assist a high acuity scenario such as a heart attack by supplying clear instructions, vital signs and treatment reminders. A lower severity scenario such as kidney stone risk could interface to vehicle systems and display more complex predictive data during a diagnosis. A CDSS needs to contribute to successful missions by maintaining a high performing crew who can potentially exhibit countless medical conditions related to derangements from the space environment (sleep, cognition, nutrition and exercise) as well as conditions intrinsic to humans anywhere. While supporting the crew’s ability to make sound clinical decisions is desirable in any mission, it is essential for exploration missions with significant communication delays, no evacuation capability, and extended exposure to the flight environment. Such missions correspond with medical Level of Care V (LOC V), the highest level specified in NASA-STD-3001. The project focuses on CDS implementation research to derive requirements for LOC V, where the need for increased autonomy results in new practices and the inclusion of non-clinical data, such as vehicle environmental measures and physical exercise results, from other human and vehicle domains and advanced analytics. The CDS project describes how the CDSS is intended to be used by defining concepts of operations (ConOps). The process to derive ConOps focuses on increased autonomy that reduces the likelihood and consequences of accepted medical conditions. These crew health and performance inputs are grouped by common datasets and analysis models. Use cases are derived to research new clinical scenarios, architectural development and workflows. Implementation research is conducted with protypes to inform assumptions and derive requirements. The project also establishes how externally developed analysis and approaches can be added to expand a clinical decision support system and thus highlight how a comprehensive system can be globally developed with collaborators. This presentation will cover example scenarios from the CDS ConOps and the method to derive them. One example scenario will be CDSS alerting an increased kidney stone risk during a mission, with diagnosis and treatment options provided during the intervention.

clinical decision support↗

NASA NTRS1973

Results of tests in the MSFC 14 x 14 inch trisonic wind tunnel on a .004 scale model of the Rockwell International Space Shuttle Vehicle 3, (integrated configuration)

Experimental aerodynamic investigations were conducted during mid-July, 1973 on a .004 scale model of the Rockwell International integrated configuration Space Shuttle Vehicle 3. The purpose of the tests was three fold: (1) to determine the static stability characteristics of the integrated vehicle, utilizing the Vehicle 3 orbiter configuration; (2) to determine the effect of interstage structure and tank external fuel lines on the integrated vehicle aerodynamic characteristics; (3) to determine the effects of the aft interstage structure on orbiter aerodynamic loads. Data were recorded on the integrated vehicle (test no. 579) at angles of attack and sideslip ranging from -10 deg to 10 deg over a Mach number schedule from 0.6 to 4.96. Data were obtained on the orbiter alone in the presence of the external tank with SRB attached (test no. 580) at angles of attack from -10 deg to 10 deg over a Mach number range from .6 to 1.96. Plotted data are presented in the body axis system.

Allen, E. C.↗

NASA NTRS1973

Results of tests OA12 and IA9 in the Ames Research Center unitary plan wind tunnels on an 0.030-scale model of the space shuttle vehicle 2A to determine aerodynamic loads, volume 1

Tests were conducted in unitary plan wind tunnels on an 0.030-scale replica of the space shuttle vehicle configuration 2A. Aerodynamic loads data were obtained at Mach numbers from 0.6 to 3.5. The investigation included tests on the integrated (launch) configuration and tests on the isolated orbiter (entry configuration). The integrated vehicle was tested at angles of attack and sideslip from minus 8 deg to plus 8 deg. The isolated orbiter was tested at angles of attack from minus 15 deg to plus 40 deg and angles of sideslip from minus 10 deg to plus 10 deg are dictated by trajectory considerations. The effects of orbiter/external tank incidence and deflected control surfaces on aerodynamic loads were also investigated.

Spangler, R. H.↗

NASA NTRS1958

Vehicle Motions as Inferred from Radio-signal- Strength Records

Considerable data on various parameters of a satellite and its launching vehicle can be obtained from the received-signal-strength records at the various satellite receiver stations. The doppler shifts of the satellite can be used to determine the orbital characteristics quite accurately. (Ref. 1 discusses the rapid determination of these parameters.) Information concerning the velocity increment and direction of each stage can be obtained if doppler data are received by several stations during the high-speed staging. The variations in signal strength during launch can be used to find the spin rate of the satellite before, during, and after staging, and to find variations in attitude, including precession, during launch. With spin and precession frequencies available, any changes in the ratios of moments of inertia can be determined. In this paper the launchings of the Explorer satellites will be used as a basis for the generalized conclusions concerning utilization of radio-signal-strength records. The discussion includes a description of the Explorer launching system.

Pilkington, W. C.↗

NASA NTRS1985

Transitional, hypervelocity aerodynamic simulation and scaling in light of recent flight data

Simulation and scaling procedures applicable to maneuvering vehicles for the transitional hypervelocity flow regime are discussed with reference to certain discrepancies between lifting reentry flight data and predicted results. Low-density hypervelocity wind-tunnel data are compared with CFD and in-flight measurements. A general correlation parameter for transitional flow is produced by incorporation of a new geometric term into a form of Reynolds number. It is shown that normalized drag coefficients of a variety of shapes, including spheres, blunt-nosed cones, lifting bodies, and an STS orbiter are correlated by the parameter. A provisional bridging formula for the coefficients is presented.

Potter, J. L.↗

NASA NTRS2000

Lab Development for INS/GPS Testing of Launch and Space Vehicles

NASA Marshall Space Flight Center's experience with different GPS simulators and receivers over the last 10 years has shown a need for testing the receivers in more than just a nominal mission. The Spaceliner 100 program is researching blended INS/GPS data tuned specifically for launch vehicles and orbital deployments. The paper will discuss layout of the testing lab, the test equipment, test scenarios that all receivers will be evaluated under, and a discussion of receiver types planned to test. It will conclude with a discussion of some of the current tests and goals of future testing.

Schrock, Ken↗

DOE OSTI2025

Fusing Edge Computing with Transport Security by Leveraging the Controller Area Network Transport Security Tracking and Reporting (C-STAR) Unit

Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗

NASA NTRS1988

Mars sample return power supply

A power supply is designed for a vehicle able to operate on the surface of Mars for a period of 5 to 10 years. This vehicle will be used for sample and data collection. The design is based on the assumption that the vehicle will be unmanned. Also, there will be no means by which components could be repaired or replaced while on the Martian surface. A consequence of this is that all equipment must meet high standards of reliability and, if possible, redundancy. Power will be supplied to the vehicle by means of a General Purpose Heat Source capable of producing a minimum of 7 kW of thermal power. The heat generated from the General Purpose Heat Source will be transferred to a Stirling engine via hot side heat pipes. The Stirling engine will then convert this heat into 2 kW of electrical power. Cold side heat pipes will be used to carry away waste heat, which will be released to the Martian environment via radiators connected to the end of the cold side heat pipes.

Hoang, Don↗

DOE OSTI2026

Raw_data_Batch_I: Downers Grove to Darien

Date of collection: May 11, 2023 Location: Downers Grove to Darien, IL This dataset contains lidar and vision data collected in Downers Grove and Darien, IL. The vehicle started in Downers Grove at the intersection of Main and Ogden, headed east. At the intersection of Odgen and IL 83, it then headed south until IL 33 and then west along IL 33 until the intersection of IL 33 and Lemont Road. It then headed north along Lemont Road/Main Street until the intersection of Main and Ogden. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![downers grove image](downers-grove-darien.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2001

Entry Debris Field Estimation Methods and Application to Compton Gamma Ray Observatory Disposal

For public safety reasons, the Compton Gamma Ray Observatory (CGRO) was intentionally deorbited on June 4, 2000. This deorbit was NASA's first intentional controlled deorbit of a satellite, and more will come including the eventual deorbit of the International Space Station. To maximize public safety, satellite deorbit planning requires conservative estimates of the debris footprint size and location. These estimates are needed to properly design a deorbit sequence that places the debris footprint over unpopulated areas, including protection for deorbit contingencies. This paper details a method for estimating the length (range), width (crossrange), and location of entry and breakup debris footprints. This method utilizes a three degree-of-freedom Monte Carlo simulation incorporating uncertainties in all aspects of the problem, including vehicle and environment uncertainties. The method incorporates a range of debris characteristics based on historical data in addition to any vehicle-specific debris catalog information. This paper describes the method in detail, and presents results of its application as used in planning the deorbit of the CGRO.

Mrozinski, Richard B.↗

NASA NTRSDate not supplied

Leveraging Large Eddy Simulations to Characterize Aeroacoustic Environments on Launch Vehicles

NASA's Space Launch System (SLS) program has relied on multiple wind tunnel campaigns to predict expected aeroacoustic environments. Time, cost, Reynolds number matching, contamination from test section acoustics, and limitations in spatial resolution are all factors that impact the use experimental data for development of launch vehicle environments. Methodological and computational advances over the last decade are enabling scale resolving computational fluid dynamics (CFD) simulations to be completed that can address some the challenges with conventional datasets, enabling more complete/accurate modeling of dynamic loading events on ascent. These simulations are classified as so-called "Large-Eddy Simulations" (LES). As the name implies, these simulations seek to directly resolve the turbulence in the largest, most-energetic turbulent structures, or eddies, and the dissipation of the smallest eddies in the flow field, is modeled. Like anything else, these simulations still must undergo rigorous testing and validation for the results to ultimately be trusted in the analysis of a launch vehicle. EV33 has begun to conduct some of this early-stage testing and validation to better support NASA missions in the future.

Jonathan Boustani↗

DOE OSTI2026

NLR's FleetDNA Speed Distributions for EPA MOVES

The U.S. Environmental Protection Agency (EPA) MOtor Vehicle Emission Simulator (MOVES) models mobile sources of air toxics at small and large scales across the Unites States. Vehicle speed distributions based on the type of vehicle, type of road traveled, and hour of the day are inputs into MOVES. In conjunction with vehicle miles traveled (VMT) inputs, they are used to estimate total operation time and select the relevant driving cycles from which running operating modes and emissions are estimated. In an ongoing research effort, the National Laboratory of the Rockies (NLR) partnered with the EPA to provide default average speed data for heavy-duty vehicles based on NLR's FleetDNA database and the Bourns College of Engineering - Center for Environmental Research and Technology (CE-CERT) data set from the University of California, Riverside.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Consumer-Oriented Energy Use and Range Metrics for Battery Electric Vehicles

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. Importantly, this regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data. Analyses of vehicles from model years 2020 – 2023 show that the 5-cycle range and energy use values from the window stickers of new vehicles are reasonable values for steady-speed driving at 65 MPH. Range decreases by a median value of approximately 15% for each 10 MPH increase in speed. The 5-cycle energy use (in kW-Hr / 100 miles) and energy economy (in miles / kW-Hr) derived from the 5-cycle energy use are reasonable estimates for 65 MPH steady speed driving. Energy economy decreases by about 0.5 miles / kW-Hr for every 10 MPH increase in speed.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Residential Vehicle-to-Home Backup Power Capabilities: Key Findings from a ComEd Beneficial Electrification R&D Pilot

This report summarizes key findings from a collaborative technical study of residential, non-grid-tied vehicle-to-home (V2H) backup power systems in Commonwealth Edison’s (ComEd’s) service territory. The work integrates (1) a feeder-level technoeconomic analysis (TEA) using historical outage-event data and simulated electric-vehicle (EV) driving/charging profiles to estimate potential reliability and customer interruption-cost impacts under V2H and vehicle-to-grid (V2G) adoption scenarios; (2) controlled laboratory performance testing of a representative V2H backup ecosystem to characterize transfer-to-backup behavior, sustained power delivery, efficiency trends, and repeatable reliability limitations; and (3) a cybersecurity assessment aligned with NIST Cybersecurity Framework (CSF) 2.0 and ISO/SAE 21434 to evaluate interface-level risk drivers and identify program-relevant mitigations. Results indicate that V2H can provide measurable resilience value, but outcomes are strongly context dependent on outage patterns and the share of events that are “V2H-applicable.” Typical transfer-to-backup behavior clustered on the order of minutes, but rare long-delay edge cases were observed (including an event approaching 30 minutes) and should be treated as a reliability risk. High-power testing showed that peak-rated output is not necessarily continuously deliverable; stable operation may require operation below nameplate ratings and attention to thermal and installation constraints. The cybersecurity assessment highlights a broad attack surface spanning commissioning, home networks, embedded services, and cloud/OTA pathways, motivating minimum controls for secure onboarding, signed updates, patch cadence, and coordinated vulnerability response for any scaled deployment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2011

Monitoring Spacecraft Telemetry Via Optical or RF Link

A patent disclosure document discusses a photonic method for connecting a spacecraft with a launch vehicle upper-stage telemetry system as a means for monitoring a spacecraft fs health and status during and right after separation and deployment. This method also provides an efficient opto-coupled capability for prelaunch built-in-test (BIT) on the ground to enable more efficient and timely integration, preflight checkout, and a means to obviate any local EMI (electromagnetic interference) during integration and test. Additional utility can be envisioned for BIT on other platforms, such as the International Space Station (ISS). The photonic telemetry system implements an optical free-space link with a divergent laser transmitter beam spoiled over a significant cone angle to accommodate changes in spacecraft position without having to angle track it during deployment. Since the spacecraft may lose attitude control and tumble during deployment, the transmitted laser beam interrogates any one of several low-profile meso-scale retro-reflective spatial light modulators (SLMs) deployed over the surface of the spacecraft. The return signal beam, modulated by the SLMs, contains health, status, and attitude information received back at the launch vehicle. Very compact low-power opto-coupler technology already exists for the received signal (requiring relatively low bandwidths, e.g., .200 kbps) to enable transfer to a forward pass RF relay from the launch vehicle to TDRSS (Tracking and Data Relay Satellite System) or another recipient. The link would be active during separation and post-separation to monitor spacecraft health, status, attitude, or other data inventories until attitude recovery and ground control can be re-established. An optical link would not interfere with the existing upper stage telemetry and beacon systems, thus meeting launch vehicle EMI environmental constraints.

Fielhauer, K. B.↗

NASA NTRS1982

An evaluation of Space Shuttle STS-1 payload bay acoustic data and comparison with predictions

During the first launch (STS-1) of the Space Shuttle orbiter vehicle, sound pressure levels were measured at several locations in the payload bay of the vehicle and on the exterior surface. The data were obtained in order to provide validation for prediction procedures for interior and exterior sound pressure levels and to determine, independently, the acoustic environment in the payload bay during actual launch conditions.

Wilby, J. F.↗

NASA NTRS1985

Electron and ion density depletions measured in the STS-3 orbiter wake

The third Space Shuttle flight on Columbia carried instrumentation to measure thermal plasma density and temperature. Two separate investigations, the Plasma Diagnostics Package (PDP) and the Vehicle Charging and Potential Experiment (VCAP), carried a Langmuir Probe, and the VCAP also included a Spherical Retarding Potential Analyzer (SRPA). Only those measurements made while the PDP is in the payload bay are discussed here since the VCAP instrumentation remains in the payload bay at all times and the two measurements are compared. The wake behind a large structure (in this case the Space Shuttle Orbiter) flying through the ionospheric plasma is discussed. Much theoretical work was done regarding plasma wakes. The instrumentation on this mission gives the first data taken with a large vehicle in the ionospheric laboratory. First, the PDP Langmuir Probe and its data set will be presented, then the VCAP Langmuir Probe and SRPA with associated data. The agreement between the two data sets is discussed and then followed by some other PDP data which infers an even lower wake density.

Murphy, G. B.↗