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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Evaluation of Artemis I Aerodynamic Force and Moment Reconstructions and Pre-Flight Predictions for Ascent Flight

The Artemis I launch of the Space Launch System provided the first flight data for the new launch vehicle. The current paper shows comparisons of the preflight force and moment coefficient databases developed using traditional ground test and computation fluid dynamics to post-flight data reconstructions of the ascent aerodynamic force and moment coefficients. The post-flight reconstructions were developed using instrumentation on the vehicle, meteorological data, and Newton’s laws. In general, the preflight databases and post-flight reconstructed data showed similar trends throughout ascent from Mach 0.20 to Mach 3.50. Axial force coefficient showed the largest discrepancies, which is likely due to the difficulty of computing axial force from the flight data.

Space Launch System↗
NASA NTRSDate not supplied

Evaluation of Artemis I Aerodynamic Force and Moment Reconstructions and Preflight Predictions for Ascent Flight

The Artemis I launch of the Space Launch System provided the first flight data for the new launch vehicle. The current paper shows comparisons of the preflight force and moment coefficient databases developed using traditional ground test and computation fluid dynamics to post-flight data reconstructions of the ascent aerodynamic force and moment coefficients. The post-flight reconstructions were developed using instrumentation on the vehicle, meteorological data, and Newton’s laws. In general, the preflight databases and post-flight reconstructed data showed similar trends throughout ascent from Mach 0.20 to Mach 3.50. Axial force coefficient showed the largest discrepancies, which is likely due to the difficulty of computing axial force from the flight data.

Space Launch System↗
NASA NTRS1976

Titian/Centaur D-1TTC-4 Viking A flight data report

Titan/Centaur TC-4 was launched from the Eastern Test Range, Complex 41, at 05:22 PM EDT on Wednesday, August 20, 1975. This was the second operational flight of the newest NASA unmanned launch vehicle. The spacecraft was the Viking A, the first of two orbiting and landing missions to Mars planned for the 1975 Martian launch opportunity. The objective of the launch phase of the mission, to inject the Viking spacecraft onto the planned transfer orbit to Mars, was successfully accomplished. This report presents a review of the launch vehicle system flight data.

Source record↗
NASA NTRS1976

Titan/Centaur D-1T TC-3 Viking B Flight Data Report

Titan/Centaur TC-3 was launched from the Eastern Test Range, Complex 41, at 02:34 PM EDT on Tuesday, September 9, 1975. This was the third operational flight of the newest NASA unmanned launch vehicle. The spacecraft was the Viking B, the second of two orbiting and landing missions to Mars planned for the 1975 Martian launch opportunity. The objective of the launch phase of the mission, to inject the Viking spacecraft onto the planned transfer orbit to Mars, was successfully accomplished. A review of the launch vehicle system flight data is presented.

Source record↗
NASA NTRS2013

Determine ISS Soyuz Orbital Module Ballistic Limits for Steel Projectiles Hypervelocity Impact Testing

A new orbital debris environment model (ORDEM 3.0) defines the density distribution of the debris environment in terms of the fraction of debris that are low-density (plastic), medium-density (aluminum) or high-density (steel) particles. This hypervelocity impact (HVI) program focused on assessing ballistic limits (BLs) for steel projectiles impacting the enhanced Soyuz Orbital Module (OM) micrometeoroid and orbital debris (MMOD) shield configuration. The ballistic limit was defined as the projectile size on the threshold of failure of the OM pressure shell as a function of impact speeds and angle. The enhanced OM shield configuration was first introduced with Soyuz 30S (launched in May 2012) to improve the MMOD protection of Soyuz vehicles docked to the International Space Station (ISS). This test program provides HVI data on U.S. materials similar in composition and density to the Russian materials for the enhanced Soyuz OM shield configuration of the vehicle. Data from this test program was used to update ballistic limit equations used in Soyuz OM penetration risk assessments. The objective of this hypervelocity impact test program was to determine the ballistic limit particle size for 440C stainless steel spherical projectiles on the Soyuz OM shielding at several impact conditions (velocity and angle combinations). This test report was prepared by NASA-JSC/ HVIT, upon completion of tests.

Lyons, Frankel↗
NASA NTRSDate not supplied

Prediction-Based Approaches for Generation of Noise-Power-Distance Data with Application to Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes AEDT’s fixed-wing aircraft modeling approach and the other utilizes the rotary-wing aircraft modeling approach.

noise-power-distance↗
NASA NTRSDate not supplied

Comparison of Surface Pressure Fluctuations from Flight and Wind Tunnel Tests on the Orion Multi-Purpose Crew Vehicle

Transonic and supersonic wind-tunnel test of scaled-models are used to predict the surface pressure fluctuations on aerospace vehicles. An outstanding question is how good are such tests? The present paper attempts to answer this question for the Multi-Purpose Crew Vehicle by comparing data from the Ascent Abort-2 (AA2) flight test with those measured in two different scaled-model wind tunnel tests (WTT) 51AS and 134AS. A sensor by sensor and a Mach by Mach comparisons of spectra of surface pressure fluctuations are presented for nominal ascent part of flight. Spectra measured over a large part of the vehicle surface were found to be dominated by the wake vortices shed from the four Abort Motor nozzles situated on the upper part of the Launch Abort System. It was found that the shapes of the scaled-up spectra from WTTs were very close to those measured in the flight, but the levels, for the most part, were found to be lower than the flight data. The highest differences were from the regions of complex flows where wakes from the adjacent nozzles interacted with the free-stream flow. There the WTT predicted levels were consistently 2dB to 6dB lower than the flight data, across all spectral bands. Another region of significant under-prediction was the local separated flow region at the Fillet-Ogive junction. The accuracy of the sharp spectral peaks from regions along a nozzle axis was found to be dependent on the fidelity of the model. The model in the 51AS WTT used a simplified contour, which resulted in a large under prediction of the spectral peak by 1dB to 10dB. A limited number of data from 134AS, where the model accurately captured all features of the nozzles of the flight vehicle, however; showed that the under prediction was lower: 1-2dB. The fluctuation spectra on the large protuberance of the Umbilical Cover was found to be reasonably well-predicted by 134AS WTT, which meticulously reproduced a scaled-down shape of this protuberance. Such information will help to determine the margins to be applied to wind-tunnel data to create flight environments.

MPCV↗
NASA NTRS1982

A preliminary correlation of the orbiter stability and control aerodynamics from the first two Space Shuttle flights /STS-1 & 2/ with preflight predictions

A correlation of the stability and control derivatives from flight (STS-1 & 2) with preflight predictions is presented across the Mach range from 0.9 to 25. Flight data obtained from specially designed flight test maneuvers as well as from conventional bank maneuvers generally indicate good agreement with predicted data. However, the vehicle appears to be lateral-directionally more stable than predicted in the transonic regime. Aerodynamic 'reasonableness tests' are employed to test for validity of flight data. The importance of testing multiple models in multiple wind tunnels at the same test conditions is demonstrated.

Underwood, J. M.↗
NASA NTRS1972

Skylab Saturn 1B flight manual

A Saturn 1B Flight Manual provides launch vehicle systems descriptions and predicted performance data for the Skylab missions. Vehicle SL-2 (SA-206) is the baseline for this manual; but, as a result of the great similarity, the material is representative of SL-3 and SL-4 launch vehicles, also. The Flight Manual is not a control document but is intended primarily as an aid to astronauts who are training for Skylab missions. In order to provide a comprehensive reference for that purpose, the manual also contains descriptions of the ground support interfaces, prelaunch operations, and emergency procedures. Mission variables and constraints are summarized, and mission control monitoring and data flow during launch preparation and flight are discussed.

Source record↗
NASA NTRS2013

Doppler Lidar Sensor for Precision Navigation in GPS-Deprived Environment

Landing mission concepts that are being developed for exploration of solar system bodies are increasingly ambitious in their implementations and objectives. Most of these missions require accurate position and velocity data during their descent phase in order to ensure safe, soft landing at the pre-designated sites. Data from the vehicle's Inertial Measurement Unit will not be sufficient due to significant drift error after extended travel time in space. Therefore, an onboard sensor is required to provide the necessary data for landing in the GPS-deprived environment of space. For this reason, NASA Langley Research Center has been developing an advanced Doppler lidar sensor capable of providing accurate and reliable data suitable for operation in the highly constrained environment of space. The Doppler lidar transmits three laser beams in different directions toward the ground. The signal from each beam provides the platform velocity and range to the ground along the laser line-of-sight (LOS). The six LOS measurements are then combined in order to determine the three components of the vehicle velocity vector, and to accurately measure altitude and attitude angles relative to the local ground. These measurements are used by an autonomous Guidance, Navigation, and Control system to accurately navigate the vehicle from a few kilometers above the ground to the designated location and to execute a gentle touchdown. A prototype version of our lidar sensor has been completed for a closed-loop demonstration onboard a rocket-powered terrestrial free-flyer vehicle.

Amzajerdian, F.↗
DOE OSTI2026

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗
NASA NTRS1981

Advanced oxygen-hydrocarbon rocket engine study

This study identifies and evaluates promising LO2/HC rocket engine cycles, produces a consistent and reliable data base for vehicle optimization and design studies, demonstrates the significance of propulsion system improvements, and selects the critical technology areas necessary to realize an improved surface to orbit transportation system. Parametric LO2/HC engine data were generated over a range of thrust levels from 890 to 6672 kN (200K to 1.5M 1bF) and chamber pressures from 6890 to 34500 kN (1000 to 5000 psia). Engine coolants included RP-1, refined RP-1, LCH4, LC3H8, LO2, and LH2. LO2/RP-1 G.G. cycles were found to be not acceptable for advanced engines. The highest performing LO2/RP-1 staged combustion engine cycle utilizes LO2 as the coolant and incorporates an oxidizer rich preburner. The highest performing cycle for LO2/LCH4 and LO2/LC3H8 utilizes fuel cooling and incorporates both fuel and oxidizer rich preburners. LO2/HC engine cycles permitting the use of a third fluid LH2 coolant and an LH2 rich gas generator provide higher performance at significantly lower pump discharge pressures. The LO2/HC dual throat engine, because of its high altitude performance, delivers the highest payload for the vehicle configuration that was investigated.

Obrien, C. J.↗
NASA NTRS2014

Applying Motion Constraints Based on Test Data

MSC ADAMS is a simulation software that is used to analyze multibody dynamics. Using user subroutines, it is possible to apply motion constraints to the rigid bodies so that they match the motion profile collected from test data. This presentation describes the process of taking test data and passing it to ADAMS using user subroutines, and uses the Morpheus free-flight 4 test as an example of motion data used for this purpose. Morpheus is the name of a prototype lander vehicle built by NASA that serves as a test bed for various experimental technologies (see backup slides for details) MSC.ADAMS"TM" is used to play back telemetry data (vehicle orientation and position) from each test as the inputs to a 6-DoF general motion constraint (details in backup slides) The MSC.ADAMS"TM" playback simulations allow engineers to examine and analyze flight trajectory as well as observe vehicle motion from any angle and at any playback speed. This facilitates the development of robust and stable control algorithms, increasing reliability and reducing development costs of this developmental engine The simulation also incorporates a 3D model of the artificial hazard field, allowing engineers to visualize and measure performance of the developmental autonomous landing and hazard avoidance technology ADAMS is a multibody dynamics solver. It uses forces, constraints, and mass properties to numerically integrate equations of motion. The ADAMS solver will ask the motion subroutine for position, velocity, and acceleration values at various time steps. Those values must be continuous over the whole time domain. Each degree of freedom in the telemetry data can be examined separately; however, linear interpolation of the telemetry data is invalid, since there will be discontinuities in velocity and acceleration.

Burlone, Michael↗
NASA NTRS2023

Flight Test Data for Prototype Air-Ground Control and Non-Payload Communications Radio Link

Extensive flight testing was conducted over flat terrain, hilly terrain, and open freshwater settings to examine performance of an air-ground command and control radio link for unmanned aerial vehicles. The flight data collected on bidirectional radio range, signal fading, and transmitted data loss were necessary to validate a proposed air-ground radio waveform that was being considered by a U.S. standards organization. The NASA test system operated at C-band frequencies and utilized a prototype, software-defined radio developed in cooperation with a commercial avionics supplier. Results are presented for multiple altitudes, ranges, and flight conditions, indicating the signal fade depths, occurrence rates, and durations for each flight environment.

Kurt Shalkhauser↗
NASA NTRS1972

Preliminary design of an auxiliary power unit for the space shuttle. Volume 5: Selected system cycle performance data

Detailed cycle steady-state performance data are presented for the final auxiliary power unit (APU) system configuration. The selection configuration is a hydrogen-oxygen APU incorporating a recuperator to utilize the exhaust energy and using the cycle hydrogen flow as a means of cooling the component heat loads. The data are given in the form of computer printouts and provide the following: (1) verification of the adequacy of the design to meet the problem statement for steady-state performance; (2) overall system performance data for the vehicle system analyst to determine propellant consumption and hydraulic fluid temperature as a function for varying mission profiles, propellant inlet conditions, etc.; and (3) detailed component performance and cycle state point data to show what is happening in the cycle as a function of the external forcing functions.

Hamilton, M. L.↗
NASA NTRS1974

Navigation and performance analysis of a Mars surface sample return /MSSR/ mission

A Viking-derived MSSR mission involves a rendezvous in Mars orbit between the sample-carrying Mars Ascent Vehicle (MAV) and a modified Viking Orbiter spacecraft. Conditions for the automatic terminal rendezvous phase are established by an initial earth-controlled phase which uses very accurate relative state information obtained by multi-vehicle interferometric tracking data. A navigation analysis has demonstrated sufficient performance and control capability to satisfy those conditions prior to terminal rendezvous. Specific performance constraints for the mission are developed, leading to a sizing of vehicle weight requirements and trades applicable to a 1981 MSSR, launched by a single Titan IIIE/Centaur.

Satin, A. L.↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗