Search NASA⌕ Search

SEARCH · Search NASA

Results for “trajectory 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.

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

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Orion Exploration Flight Test 1 (EFT-1) Best Estimated Trajectory Development

The Orion Exploration Flight Test 1 (EFT-1) mission successfully flew on Dec 5, 2014 atop a Delta IV Heavy launch vehicle. The goal of Orions maiden flight was to stress the system by placing an uncrewed vehicle on a high-energy trajectory replicating conditions similar to those that would be experienced when returning from an asteroid or a lunar mission. The Orion navigation team combined all trajectory data from the mission into a Best Estimated Trajectory (BET) product. There were significant challenges in data reconstruction and many lessons were learned for future missions. The team used an estimation filter incorporating radar tracking, onboard sensors (Global Positioning System and Inertial Measurement Unit), and day-of-flight weather balloons to evaluate the true trajectory flown by Orion. Data was published for the entire Orion EFT-1 flight, plus objects jettisoned during entry such as the Forward Bay Cover. The BET customers include approximately 20 disciplines within Orion who will use the information for evaluating vehicle performance and influencing future design decisions.

Holt, Greg N.↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

ASTP (SA-210) Launch vehicle operational flight trajectory. Part 3: Final documentation

Trajectory data are presented for a nominal and two launch window trajectory simulations. These trajectories are designed to insert a manned Apollo spacecraft into a 150/167 km. (81/90 n. mi.) earth orbit inclined at 51.78 degrees for rendezvous with a Soyuz spacecraft, which will be orbiting at approximately 225 km. (121.5 n. mi.). The launch window allocation defined for this launch is 500 pounds of S-IVB stage propellant. The launch window opening trajectory simulation depicts the earliest launch time deviation from a planar flight launch which conforms to this constraint. The launch window closing trajectory simulation was developed for the more stringent Air Force Eastern Test Range (AFETR) flight azimuth restriction of 37.4 degrees east-of-north. These trajectories enclose a 12.09 minute launch window, pertinent features of which are provided in a tabulation. Planar flight data are included for mid-window reference.

Carter, A. B.↗
NASA NTRS1972

Mathematical model of a flexible space shuttle vehicle

The development of a mathematical model of the lateral motion of a flexible space shuttle vehicle during ascent is described. The model was developed to perform control system synthesis using stochastic constrained optimization techniques. The goals of the control system synthesis are to demonstrate the applicability of the techniques and to discover any problems peculiar to the flexible nature of a shuttle vehicle. The equations of motion are derived. A brief description of the generation of numerical data is given. Explicit definitions and numerical values of trajectory data and coefficients appearing in the equations of motion are included.

Harvey, C. A.↗
NASA NTRS1978

Experimental investigation of effects of jet decay rate on jet-induced pressures on a flat plate: Tabulated data

Tabular data are presented for an experimental study of the effects of jet decay rate on the jet-induced pressure distribution on a flat plate for a single jet issuing at right angle to the flat plate into a uniform crossflow. The data are presented in four sections: (1) presents the static nozzle calibration data; (2) lists the plate surface static pressure data and integrated loads; (3) lists the jet centerline trajectory data; and (4) lists the centerline dynamic pressure data.

Kuhlman, J. M.↗
NASA NTRS1991

Unsteady surface pressure measurements on a slender delta wing undergoing limit cycle wing rock

An experimental investigation of slender wing limit cycle motion known as wing rock was investigated using two unique experimental systems. Dynamic roll moment measurements and visualization data on the leading edge vortices were obtained using a free to roll apparatus that incorporates an airbearing spindle. In addition, both static and unsteady surface pressure data was measured on the top and bottom surfaces of the model. To obtain the unsteady surface pressure data a new computer controller drive system was developed to accurately reproduce the free to roll time history motions. The data from these experiments include, roll angle time histories, vortex trajectory data on the position of the vortices relative to the model's surface, and surface pressure measurements as a function of roll angle when the model is stationary or undergoing a wing rock motion. The roll time history data was numerically differentiated to determine the dynamic roll moment coefficient. An analysis of these data revealed that the primary mechanism for the limit cycle behavior was a time lag in the position of the vortices normal to the wing surface.

Arena, Andrew S., Jr.↗
NASA NTRS1992

Computations of the unsteady flow about a generic wing/pylon/finned-store configuration

An overset grid approach is used to carry out a set of computations of the unsteady flow about a generic wing, pylon, and finned-store configuration. The geometry, discretization procedure, and governing equations are presented. Thin-layer Navier-Stokes solutions are presented for four store separation cases: store in carriage position, store at two different separation positions, and a time-accurate simulation of the forced store separation from the wing pylon carriage. Computational results are compared with wind tunnel data for the three 'static store' cases. Dynamic loads and trajectory data are presented for the forced separation case.

Meakin, Robert L.↗
DOE OSTI2025

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1987

Shuttle derived atmosphere

The shuttle descends along a rather shallow path, thus providing some information on the horizontal structure of the atmosphere. Small scale structures were suggested (shears, potholes). The best estimates of the shuttle drag coefficient and projected areas are used to go from accelerometer data to density through the use of BET's (Best Estimated Trajectories). Data are from the IMU's (Inertial Measurement Unit) and the HiRAP (High Resolution Accelerometer Package).

Findlay, John↗
NASA NTRS1979

Flight design system level C requirements. Solid rocket booster and external tank impact prediction processors

The prediction of the SRB and ET impact areas requires six separate processors. The SRB impact prediction processor computes the impact areas and related trajectory data for each SRB element. Output from this processor is stored on a secure file accessible by the SRB impact plot processor which generates the required plots. Similarly the ET RTLS impact prediction processor and the ET RTLS impact plot processor generates the ET impact footprints for return-to-launch-site (RTLS) profiles. The ET nominal/AOA/ATO impact prediction processor and the ET nominal/AOA/ATO impact plot processor generate the ET impact footprints for non-RTLS profiles. The SRB and ET impact processors compute the size and shape of the impact footprints by tabular lookup in a stored footprint dispersion data base. The location of each footprint is determined by simulating a reference trajectory and computing the reference impact point location. To insure consistency among all flight design system (FDS) users, much input required by these processors will be obtained from the FDS master data base.

Seale, R. H.↗
NASA NTRS1993

ICE/ISEE plasma wave data analysis

This report is one of the final processing of ICE plasma wave (pw) data and analysis of late ISEE 3, ICE cometary, and ICE cruise trajectory data, where coronal mass ejections (CME's) were the first locus of attention. Interest in CME's inspired an effort to represent our pw data in a condensed spectrogram format that facilitated rapid digestion of interplanetary phenomena on long (greater than 1 day) time scales. The format serendipitously allowed us to also examine earth-orbiting data from a new perspective, invigorating older areas of investigation in Earth's immediate environment. We, therefore, continued to examine with great interest the last year of ISEE 3's precomet phase, when it spent considerable time far downwind from Earth, recording for days on end conditions upstream, downstream, and across the very weak, distant flank bow shock. Among other motivations has been the apparent similarity of some shock and post shock structures to the signatures of the bow wave surrounding comet Giacobini-Zinner, whose ICE-phase data we revisited.

Greenstadt, E. W.↗
NASA NTRS1972

IMP F and G phase 1 magnetic field analysis

The program developed to analyze magnetic field data from the magnetic field experiment flown in IMP F is reported. The analysis converts the raw X, Y, Z sensor data as received on the magnetic field experiment tape into vector measurements of the ambient magnetic field observed by the experiment. These data are computed for four frames of reference -- apparent, payload, solar ecliptic and solar magnetospheric. In addition 20.45 second statistics are computed for the last three coordinate systems. Finally, a summary tape is produced containing detailed data and sequence statistics as well as the output from the autocorrelation computer, trajectory data and identification information.

Mish, W. H.↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗