Search NASA⌕ Search

SEARCH · Search NASA

Results for “vehicle dynamics”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Variable Dynamic Testbed Vehicle: Dynamics Analysis

The Variable Dynamic Testbed Vehicle (VDTV) concept has been proposed as a tool to evaluate collision avoidance systems and to perform driving-related human factors research. The goal of this study is to analytically investigate to what extent a VDTV with adjustable front and rear anti-roll bar stiffnesses, programmable damping rates, and four-wheel-steering can emulate the lateral dynamics of a broad range of passenger vehicles.

Variable Dynamic Testbed Vehicle VDTV collision av↗
DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗
NASA NTRS1975

Dispersion analysis techniques within the space vehicle dynamics simulation program

The Space Vehicle Dynamics Simulation (SVDS) program was evaluated as a dispersion analysis tool. The Linear Error Analysis (LEA) post processor was examined in detail and simulation techniques relative to conducting a dispersion analysis using the SVDS were considered. The LEA processor is a tool for correlating trajectory dispersion data developed by simulating 3 sigma uncertainties as single error source cases. The processor combines trajectory and performance deviations by a root-sum-square (RSS process) and develops a covariance matrix for the deviations. Results are used in dispersion analyses for the baseline reference and orbiter flight test missions. As a part of this study, LEA results were verified as follows: (A) Hand calculating the RSS data and the elements of the covariance matrix for comparison with the LEA processor computed data. (B) Comparing results with previous error analyses. The LEA comparisons and verification are made at main engine cutoff (MECO).

Snow, L. S.↗
NASA NTRS1994

Rotorcraft control system design for uncertain vehicle dynamics using quantitative feedback theory

Quantitative Feedback Theory describes a frequency-domain technique for the design of multi-input, multi-output control systems which must meet time or frequency domain performance criteria when specified uncertainty exists in the linear description of the vehicle dynamics. This theory is applied to the design of the longitudinal flight control system for a linear model of the BO-105C rotorcraft. Uncertainty in the vehicle model is due to the variation in the vehicle dynamics over a range of airspeeds from 0-100 kts. For purposes of exposition, the vehicle description contains no rotor or actuator dynamics. The design example indicates the manner in which significant uncertainty exists in the vehicle model. The advantage of using a sequential loop closure technique to reduce the cost of feedback is demonstrated by example.

Hess, R. A.↗
NASA NTRS1963

Launch Vehicle Dynamics Demonstrator Model

The effect of vibration on launch vehicle dynamics was studied. Conditions included three modes of instability. The film includes close up views of the simulator fuel tank with and without stability control.

[Division] Vibration and Dynamics↗
DOE OSTI2025

Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Civil engineering↗
NASA NTRS2015

Airborne Simulation of Launch Vehicle Dynamics

In this paper we present a technique for approximating the short-period dynamics of an exploration-class launch vehicle during flight test with a high-performance surrogate aircraft in relatively benign endoatmospheric flight conditions. The surrogate vehicle relies upon a nonlinear dynamic inversion scheme with proportional-integral feedback to drive a subset of the aircraft states into coincidence with the states of a time-varying reference model that simulates the unstable rigid body dynamics, servodynamics, and parasitic elastic and sloshing dynamics of the launch vehicle. The surrogate aircraft flies a constant pitch rate trajectory to approximate the boost phase gravity turn ascent, and the aircraft's closed-loop bandwidth is sufficient to simulate the launch vehicle's fundamental lateral bending and sloshing modes by exciting the rigid body dynamics of the aircraft. A novel control allocation scheme is employed to utilize the aircraft's relatively fast control effectors in inducing various failure modes for the purposes of evaluating control system performance. Sufficient dynamic similarity is achieved such that the control system under evaluation is configured for the full-scale vehicle with no changes to its parameters, and pilot-control system interaction studies can be performed to characterize the effects of guidance takeover during boost. High-fidelity simulation and flight-test results are presented that demonstrate the efficacy of the design in simulating the Space Launch System (SLS) launch vehicle dynamics using the National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center Fullscale Advanced Systems Testbed (FAST), a modified F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois), over a range of scenarios designed to stress the SLS's Adaptive Augmenting Control (AAC) algorithm.

airborne simulation↗
NASA NTRS2014

Airborne Simulation of Launch Vehicle Dynamics

In this paper we present a technique for approximating the short-period dynamics of an exploration-class launch vehicle during flight test with a high-performance surrogate aircraft in relatively benign endoatmospheric flight conditions. The surrogate vehicle relies upon a nonlinear dynamic inversion scheme with proportional-integral feedback to drive a subset of the aircraft states into coincidence with the states of a time-varying reference model that simulates the unstable rigid body dynamics, servodynamics, and parasitic elastic and sloshing dynamics of the launch vehicle. The surrogate aircraft flies a constant pitch rate trajectory to approximate the boost phase gravity-turn ascent, and the aircraft's closed-loop bandwidth is sufficient to simulate the launch vehicle's fundamental lateral bending and sloshing modes by exciting the rigid body dynamics of the aircraft. A novel control allocation scheme is employed to utilize the aircraft's relatively fast control effectors in inducing various failure modes for the purposes of evaluating control system performance. Sufficient dynamic similarity is achieved such that the control system under evaluation is optimized for the full-scale vehicle with no changes to its parameters, and pilot-control system interaction studies can be performed to characterize the effects of guidance takeover during boost. High-fidelity simulation and flight test results are presented that demonstrate the efficacy of the design in simulating the Space Launch System (SLS) launch vehicle dynamics using NASA Dryden Flight Research Center's Full-scale Advanced Systems Testbed (FAST), a modified F/A-18 airplane, over a range of scenarios designed to stress the SLS's adaptive augmenting control (AAC) algorithm.

Gilligan, Eric T.↗
NASA NTRS1975

Dispersion analysis and linear error analysis capabilities of the space vehicle dynamics simulation program

Previous error analyses conducted by the Guidance and Dynamics Branch of NASA have used the Guidance Analysis Program (GAP) as the trajectory simulation tool. Plans are made to conduct all future error analyses using the Space Vehicle Dynamics Simulation (SVDS) program. A study was conducted to compare the inertial measurement unit (IMU) error simulations of the two programs. Results of the GAP/SVDS comparison are presented and problem areas encountered while attempting to simulate IMU errors, vehicle performance uncertainties and environmental uncertainties using SVDS are defined. An evaluation of the SVDS linear error analysis capability is also included.

Snow, L. S.↗
NASA NTRS2016

Experimental Evaluation of the Scale Model Method to Simulate Lunar Vehicle Dynamics

As compared to driving on Earth, the presence of lower gravity and uneven terrain on planetary bodies makes high speed driving difficult. In order to maintain ground contact and control vehicles need to be designed with special attention to dynamic response. The challenge of maintaining control on the Moon was evident during high speed operations of the Lunar Roving Vehicle (LRV) on Apollo 16, as at one point all four tires were off the ground; this event has been referred to as the Lunar Grand Prix. Ultimately, computer simulation should be used to examine these phenomena during the vehicle design process; however, experimental techniques are required for the validation and elucidation of key issues. The objectives of this study were to evaluate the methodology for developing a scale model of a lunar vehicle using similitude relationships and to test how vehicle configuration, six or eight wheel pods, and local tire compliance, soft or stiff, affect the vehicles dynamic performance. A wheel pod consists of a drive and steering transmission and wheel. The Lunar Electric Rover (LER), a human driven vehicle with a pressurized cabin, was selected as an example for which a scale model was built. The scaled vehicle was driven over an obstacle and the dynamic response was observed and then scaled to represent the full-size vehicle in lunar gravity. Loss of ground contact, in terms of vehicle travel distance with tires off the ground, was examined. As expected, local tire compliance allowed ground contact to be maintained over a greater distance. However, switching from a six-tire configuration to an eight-tire configuration with reduced suspension stiffness had a negative effect on ground contact. It is hypothesized that this was due to the increased number or frequency of impacts. The development and testing of this scale model provided practical lessons for future low-gravity vehicle development.

Moon↗
NASA NTRS2020

Enabling Dynamic Vehicle Analyses With Improved Atmospheric Attenuation Models in Glenn Research Center Communication Analysis Suite

To aid in meeting the NASA objective of returning humans to the Moon, the Glenn Research Center’s Communication Analysis Suite was augmented with two distinct capabilities. The first capability added was the vehicle propagator. This allows the addition of dynamic aircraft and ground vehicles around any celestial body within the solar system during an analysis. This functionality interpolates the position and velocity of the vehicle relative to a celestial body at the time steps analyzed using the type of path and either a series of waypoints or a direction and duration of travel. The implications of this new capability include lunar rovers and/or drones, such as Dragonfly, where the vehicle propagator will analyze the communications architecture. The newly created vehicle propagator is now in use in communications studies for the 2024 lunar missions, simulating the movement of lunar rovers across the Moon’s southern pole. The second capability added was the augmentation of the atmospheric attenuation model. The previous model did not have a uniform low-elevation attenuation model due to the trigonometric approximation for path length and the exponential nature of low-elevation scintillation. User-defined weather parameters were also added to the updated atmospheric attenuation model. The previous model solely used tabular data based upon the season and location of the transmitting antenna. Multiple simulations of the same configuration now return different results based on the differing weather parameters. Cognitive communications analysis efforts can use this second capability to generate neural network training data based on differing weather conditions at utilized ground stations, a critical step in allowing neural networks to learn how weather parameters impact communications performance.

vehicle propagation↗
NASA NTRS2015

Modeling and Simulation of Vehicle Dynamics on the Surface of Phobos

In this paper we analyze the dynamics of a spacecraft in proximity of Phobos by developing the equations of motion of a test mass in the Phobos rotating frame using a model based on circularly-restricted three body problem, and by analyzing the dynamics of a ATHLETE hopper vehicle interacting with the soil under different soil-interaction conditions. The main conclusion of the numerical studies is that the system response is dominated by the stiffness and damping parameters of the leg springs, with the soil characteristics having a much smaller effect. The system simulations identify ranges of parameters for which the vehicle emerges stably (relying only on the passive viscoelastic damper at each leg) or unstably (needing active attitude control) from the hop. The implication is that further experimental and possibly computational modeling work, as well as site characterization (from precursor missions) will be necessary to obtain validated performance models.

soil-structure interaction↗