Surrogate Reduced-Order Modeling for Structural Dynamic and Aeroelastic Systems Using a Frequency-Domain Modal Analysis

Dr. John T. Kim

Abstract

Surrogate reduced-order modeling refers to model reduction with parameter changes/uncertainties in the system accounted for. It has gained much attention and interest in recent years due to its potential usefulness in many engineering applications such as optimization and parameter study. In this work, a novel model reduction methodology for structural dynamic and aeroelastic systems subject to parameter variations is presented based on a frequency domain formulation and use of the Proper Orthogonal Decomposition (POD). For an efficient treatment of the parameter variations, the solutions and system matrices are divided into nominal and incremental parts. It is shown that the perturbed part is modally equivalent to a new system where the incremental matrices are isolated into the forcing term and do not appear in the homogeneous part of the equation. To account for the continuous changes in the parameters, the Single-Composite-Input (SCI) is invoked with a finite number of predetermined incremental matrices. The POD is then used to calculate a rich set of basis modes accounting for the variations. For demonstration, the new procedure is applied to Goland wing model undergoing aeroelastic oscillations as well as structural vibrations and shown to produce extremely accurate Reduced-Order Surrogate Model (ROSM) for a wide range of parameter variations/uncertainties.

Time : April 6^th (Mon) 11AM~

Place : 4^th floor meeting room (4301)

Bio Sketch of Dr. John T. Kim

Dr. Kim worked at the Boeing Company, Seattle, for sixteen and half years. Prior to this, he was a research associate at Georgia Tech. His areas of specialty are structural dynamics, fluid-structure-control interaction (a.k.a., aeroservoelasticity), system identification and reduced-order modeling of large-scaled dynamic systems, integration of dynamic systems, unsteady aerodynamics, and composite structures. At Boeing, he developed innovative computational and experimental tools to enhance accurate and rapid estimation of dynamic loads, flutter and control laws, all of which are essential in design and analysis of modern aircraft structures. From 2005 until 2012 he taught the AIAA short course, “Computational Methods in Aeroelasticity” at AIAA Structural Dynamics and Materials Conf., Boeing Ed Wells, National Aerospace Laboratory in Bangalore, India, and NASA Langley. He is an Associate Fellow of American Institute of Aeronautics and Astronautics. In 2013 he joined National University of Singapore in the Department of Mechanical Engineering. He earned a BS from Ajou University, Korea, an MS from University of Texas at Austin, and a Ph.D. from Massachusetts Institute of Technology in Aeronautics and Astronautics