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Applied Physics Group Members and Collaborators Receive 2021 AIAA Modeling and Simulation Best Paper Award
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Applied Physics Group Members and Collaborators Receive 2021 AIAA Modeling and Simulation Best Paper Award

Members of the Applied Physics Group and Aeromechanics Office (Ames Code AV) have won the 2021 American Institute of Aeronautics and Astronautics’ (AIAA) Modeling and Simulation Best Paper Award for a paper entitled “A Gaussian Process Enhancement to Linear Parameter Varying Models” (AIAA-2021-3006). The paper was presented at the AIAA Aviation and Aeronautics Forum and Exposition in August 2021.

BACKGROUND: Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets – all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility (AAM), where rapidly emerging vehicle designs are significantly more complex, while at the same time also have to be both cost-effective and safe. To meet these engineering challenges, Machine Learning (ML) methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency.

This paper examines the use of Gaussian process ML to generalize and enhance the commonly used class of quasi-Linear Parameter Varying (qLPV) models for fast full-envelope simulation, while also supporting control system design and analysis with model uncertainty. Gaussian process ML is selected because it can fuse multiple datasets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require initiation from a large training data set. To demonstrate the benefits of the approach, we perform a robust stability analysis with Gaussian process uncertainty on a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

NASA PROGRAM FUNDING: NASA Revolutionary Vertical Lift Technologies (RVLT) project, Advanced Air Vehicles Program (AAVP), Aeronautics Research Mission Directorate (ARMD)

TEAM: Jeremy Aires, Carlos Malpica (AV), and Stefan Schuet

POINT OF CONTACT: Stefan Schuet, stefan.r.schuet@nasa.gov

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