MMAE Seminar - Dr. Vahid Keshavarzzadeh - Robust Computational Design with Reduced-Order Models

Armour College of Engineering's Mechanical, Materials & Aerospace Engineering Department will welcome Dr. Vahid Keshavarzzadeh, Postdoctoral Fellow in the Scientific Computing and Imaging Institute at the University of Utah, to present his lecture, Robust Computational Design with Reduced-Order Models.

Abstract

The design of complex physical systems requires accurate analysis of interactions among multiple components which are typically influenced by challenging uncertainty and nonlinear effects. Addressing these challenges calls for rigorous yet efficient tools in numerical optimization, stochastic modeling and high-performance computing. This talk demonstrates the synthesis of such tools to facilitate the design of complex engineering systems. First, we discuss the structural topology optimization which has emerged as a powerful computational tool in designing vast variety of high performance structures. In particular, we present a novel design under uncertainty approach where the effect of geometric uncertainty in the form of manufacturing tolerances is introduced in the topology optimization process. The effectiveness of our approach and the improved performance of risk-aware designs compared to “usual” deterministic designs is demonstrated via an illustrative example which mimics a 3D printing process. Next, we present a systematic framework for shape optimization of a wind turbine blade considering the uncertainty effects in the wind loads and material properties of the blade. Particularly, our framework combines a reduced order finite element model, aeroelastic simulation and gradient-based optimization which provides a computationally tractable design platform for such a complex engineering system. We also briefly discuss the computational aspects of reduced order models in analysis and design where we appreciably decrease the number of function evaluations (corresponding to high-fidelity simulations) via 1) a novel numerical quadrature rule for integration on domains possibly with high dimensions and general geometries, and 2) incorporation of multi-resolution finite element models for stochastic/parametric design optimization. Finally, future research directions will be discussed.

Biography

Vahid Keshavarzzadeh is currently a Postdoctoral Fellow in the Scientific Computing and Imaging Institute (SCI) at the University of Utah (Feb 2017-Present). Prior to joining SCI, he served as a Postdoctoral Research Associate in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (Nov 2014-Jan 2017). He received his Ph.D. in Structural Engineering (2014) and a M.Sc. in Electrical Engineering (2010) from the University of Southern California, and a M.Sc. in Structural Engineering (2007) and B.Sc. in Civil Engineering (2005) from Sharif University of Technology, Tehran, Iran. His research mainly focuses on the design optimization and uncertainty quantification for complex engineering systems by building computational predictive models. To that end, he also focuses on developing novel algorithms to address the computational challenges associated with design optimization and uncertainty quantification methods.