Senior Research & Application Engineer, ESTECO North America
Dr. Zhendan Xue is currently working as Senior Research and Application Engineer for ESTECO from Novi MI office. He has been with ESTECO for over 6 years supporting ESTCO customers for their engineering optimization activities.
He graduated in 2009 with PhD in Mechanical Engineering from the State University of New York at Buffalo, with research specialization in design optimization. His general technical interest includes: Engineering Optimization, Machine Learning, Design for Six Sigma, and Data Analytics.
Multidisciplinary Design Optimization (MDO) is often required in aircraft design to address the multidisciplinary feasibility issues due to the disciplines, for example, aerodynamics, propulsion, and structures, are heavily coupled. However, in automobile designs, can we apply different type of MDO decomposition originated from aircraft design, to some MDO problem, for example, a vehicle weight reduction example? Also, to effectively and efficiently accommodate design changes, multi-party collaboration between discipline specialists, and fast decision making, a web-based MDO platform with knowledge-based repository for resource sharing, capability of version control, and enhancing data security, is very much needed. Two types of MDO decomposition: All-at-Once (AAO) and Collaborative Optimization (CO) are formulated for the weight reduction example. A typical six-step MDO process, from building single discipline work flow to comparing optimization results, is illustrated step-by-step. Post-processing methods such as Multi-Criteria Decision Making (MCDM) and Constraint Relaxation Tool (CRT) are illustrated by using problems with results happen to be all infeasible.