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Results for “Vehicle trajectories”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Planetary atmosphere representation for Monte-Carlo simulation of aerospace vehicle trajectories

Methods of simulating large scale random atmospheric variations are examined for use in Monte Carlo simulations of hypervelocity vehicles. Random density, temperature, and pressure variations have a significant impact on the performance of such vehicles. Methods for generating random atmosphere profiles are described and compared in the context of a Monte Carlo simulation of the Aeroassist Flight Experiment (AFE). It is seen that these techniques result in statistically significant differences in key simulation point statistics.

Dutton, K.↗
NASA NTRS2012

End-To-End Simulation of Launch Vehicle Trajectories Including Stage Separation Dynamics

The development of methodologies, techniques, and tools for analysis and simulation of stage separation dynamics is critically needed for successful design and operation of multistage reusable launch vehicles. As a part of this activity, the Constraint Force Equation (CFE) methodology was developed and implemented in the Program to Optimize Simulated Trajectories II (POST2). The objective of this paper is to demonstrate the capability of POST2/CFE to simulate a complete end-to-end mission. The vehicle configuration selected was the Two-Stage-To-Orbit (TSTO) Langley Glide Back Booster (LGBB) bimese configuration, an in-house concept consisting of a reusable booster and an orbiter having identical outer mold lines. The proximity and isolated aerodynamic databases used for the simulation were assembled using wind-tunnel test data for this vehicle. POST2/CFE simulation results are presented for the entire mission, from lift-off, through stage separation, orbiter ascent to orbit, and booster glide back to the launch site. Additionally, POST2/CFE stage separation simulation results are compared with results from industry standard commercial software used for solving dynamics problems involving multiple bodies connected by joints.

Albertson, Cindy W.↗
DOE OSTI2025

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Arterial corridor management↗
DOE OSTI2023

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar: SUMO Default vs. Radar-Measured CF model Parameters

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Schrader, Max (ORCID:0000000336737672)↗
NASA NTRS1988

Aeroassisted orbit transfer vehicle trajectory analysis

The emphasis in this study was on the use of multiple pass trajectories for aerobraking. However, for comparison, single pass trajectories, trajectories using ballutes, and trajectories corrupted by atmospheric anomolies were run. A two-pass trajectory was chosen to determine the relation between sensitivity to errors and payload to orbit. Trajectories that used only aerodynamic forces for maneuvering could put more weight into the target orbits but were very sensitive to variations from the planned trajectors. Using some thrust control resulted in less payload to orbit, but greatly reduced the sensitivity to variations from nominal trajectories. When compared to the non-thrusting trajectories investigated, the judicious use of thrusting resulted in multiple pass trajectories that gave 97 percent of the payload to orbit with almost none of the sensitivity to variations from the nominal.

Braun, Robert D.↗
DOE OSTI2023

Deep generative models for vehicle speed trajectories

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed-forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2011

Orbiter Trajectory Analysis for a Two-Stage Reusable Launch Vehicle

Trajectory analysis performed on NASA's reference two-stage-to-orbit launch vehicle upper stage will be presented. The work was completed in support of the Hypersonics Multidisciplinary Analysis and Optimization effort for the NASA-Air Force Joint System Study. Three degree-of-freedom (3-DOF) untrimmed trajectory analysis was performed for the orbiter ascent, closure and re-entry. An iterative closure process resulted in a 333,000 lb initial mass for the orbiter. The re-entry trajectory satisfied heating constraints for all payload out cases and met the constraints with reduced margins for payload in cases. Abort trajectories for engine out at staging, engine out during ascent, and failure to circularize in orbit, gave insight to the robustness of the orbiter. A trimmed ascent trajectory defined an engine gimbal location and the body flap angle best suited for maximizing injected mass. A trimmed re-entry trajectory revealed a need to update the trim routine to accommodate full flap aerodynamic data.

Cowling, Adam L.↗
NASA NTRS2013

Visiting Vehicle Ground Trajectory Tool

The International Space Station (ISS) Visiting Vehicle Group needed a targeting tool for vehicles that rendezvous with the ISS. The Visiting Vehicle Ground Trajectory targeting tool provides the ability to perform both realtime and planning operations for the Visiting Vehicle Group. This tool provides a highly reconfigurable base, which allows the Visiting Vehicle Group to perform their work. The application is composed of a telemetry processing function, a relative motion function, a targeting function, a vector view, and 2D/3D world map type graphics. The software tool provides the ability to plan a rendezvous trajectory for vehicles that visit the ISS. It models these relative trajectories using planned and realtime data from the vehicle. The tool monitors ongoing rendezvous trajectory relative motion, and ensures visiting vehicles stay within agreed corridors. The software provides the ability to update or re-plan a rendezvous to support contingency operations. Adding new parameters and incorporating them into the system was previously not available on-the-fly. If an unanticipated capability wasn't discovered until the vehicle was flying, there was no way to update things.

Hamm, Dustin↗