A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spaces

Abstract

Realistic wind data are essential in developing, testing, and ensuring the safety of small unmanned aerial systems in operation. We present a non-intrusive reduced order modeling (NIROM) approach to replicate realistic wind data and predict wind fields. The method uses a LES model to generate high-fidelity data. To create a reduced-order model, we use proper orthogonal decomposition to extract modes from the three-dimensional space and use specialized recurrent neural networks and long short-term memory to step in time. This paper combines the traditional approach of using computational fluid dynamic simulations to generate wind data with deep learning and reduced-order modeling techniques to devise a methodology for a non-intrusive data-based model for wind field prediction. A model of an urban subspace with a building setup in neutral atmospheric conditions is tested to demonstrate the method.

Rohit Vuppala

Ph.D., Current Position: University of Chicago

Kursat Kara

Assistant Professor, Mechanical and Aerospace Engineering

Dr. Kara is the principal investigator of the Kara Aerodynamics Research Laboratory at Oklahoma State University. He teaches the Fundamentals of Aerodynamics, Unsteady Aerodynamics, Computational Fluid Dynamics, and Quantum Computing. Previously, he was an assistant professor at Khalifa University, where he received the Faculty Excellence Award for Outstanding Teaching in 2015.