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T.J. Wignall

Publications and source records attributed to T.J. Wignall.

5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Entry Descent and Landing Aerodynamic Databases with Uncertainty Quantification Developed Using Machine Learning Techniques

When developing the aerodynamic databases for use in trajectory simulations, it is important to develop a system of metrics to qualify which aerodynamic models are best to use. Since aerodynamics are just one input into trajectory simulations, the results of these simulations do not reflect on the quality of the aerodynamic database used. This means that aerodynamic database comparisons must be done offline. While traditional metrics that focus on mean/nominal predictions are a good first step, more robust estimates of the prediction interval become important as more focused uncertainty models are developed. We explore the limitations of evaluating aerodynamic models based purely on nominal-centered response surfaces. Before elaborating and evaluating metrics based on distributed models, the value of evaluating prediction interval and confidence interval are discussed to conclude that prediction intervals are more relevant to the use of trajectory analysis. Several metrics to evaluate the prediction interval are introduced with a focus on the standard calibration metric. Finally, we compare candidate models using both mean and distributed metrics. A finalized candidate model developed using state of the art machine learning methods is compared to a baseline model developed using traditional aerodynamic database modeling techniques.

Aerodynamic Database↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry, Descent, and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Ground Wind Loads on the Space Launch System’s Mobile Launcher Crew Access Arm

An aerodynamic database for the crew access arm during deployment is generated. While initial attempts used data derived from wind tunnel testing, limitations lead to the use of computational fluid dynamics to improve analysis. This computational data compared favorably to the experimental data increasing confidence in both. Further analysis lead to simplification showing the ability to build the database with a single relative wind variable which takes into account both arm deployment angle and wind direction. Eventually geometric simplifications and conservative results lead to the use of simulations of the CAA alone to build the database.Finally these static conditions are then compared to a dynamic simulation where the CAA rotates into position to test the quasi-steady state assumptions used to generate the database. The dynamic case showed an increase in the moment of interest but within acceptable bounds for now.

SLS↗
NASA NTRSDate not supplied

Experimental and Computational Examination of the Coandă Effect on the Space Launch System at Liftoff Conditions

During development of an aerodynamic database to cover ground wind loads, uncertainty quantification to account for the Coandă effect forced a closer look into how this phenomenon manifests on the Space Launch System. Aerodynamic data collected across the life of the pro-gram is explored to look for trends and the ability to characterize not just the bounds of forces and moments but also better understand their distributions. Experimental data collected in the NASA Langley 14- by 22-Foot Subsonic Tunnel is used to explore integrated forces and moments. This is followed up by a similar exploration using computational data generated using the Kestrel flow solver. After a survey of the data at large which confirms the existence of the Coandă states throughout the history of the program, a few highlighted cases are used to characterize the flow physics. This characterization is used to summarize how each Coandă state is predicted to load the vehicle.

Coandă Effect↗