Dr. Alicia Kim is a Professor in Structural and Material Optimization in the Structural Engineering Department of the University of California San Diego. Her interests are in level set topology optimization, multiscale and multiphysics optimization, modeling and optimization of composite materials and multifunctional structures. She has published nearly 200 journal and conference papers in these fields including award winning papers at the AIAA conferences and World Congresses on Structural and Multidisciplinary Optimization. She is a Review Editor for the Structural and Multidisciplinary Optimization journal. Professor Kim’s research is primarily focused on topology optimization, in particular level set based method. She and her M2DO lab researchers are developing level set topology optimization methods for multiscale and multiphysics problems leading to optimum multifunctional structures. Today's engineering structures are designed to carry out multiple functionalities and structural optimizations provide a common design platform under multiphysics considerations. She develops and applies optimization methods for challenging problems involving issues such as large-scale design variables, global optimization, nonlinearity, complex coupling of physics and high computational cost. She works closely with multidisciplinary researchers at the interface of engineering, computer science and applied mathematics and strives to find simple and powerful solutions for engineers. One focus area of Professor Kim’s current research activities is in coupled multiscale topology optimization for integrated material-structural systems. She developed an integrated framework that coupled two scales via homogenization enabling the macroscopic and the microscopic material optimization simultaneously. This paves the way for a new paradigm of tailoring material properties specific to the functional requirements of the application usually defined at the macroscopic scale.
With increasing computational power and sophisticated computational models, today’s complex systems engineering is turning to the concept of digital twin, a virtual representation of a connected physical asset. Its primary value is in the capability to predict asset behavior in a range of conditions by leveraging the digital model. The digital model is constructed by coupling multiple disciplinary models and physical governing equations such that the coupled behavior across different physics and scales can be efficiently and effectively simulated, which in turn, informs decision making. In a complex system, these coupled behaviors can be emergent and unintuitive, and high-fidelity models are necessary to correctly capture and translate the full effects. It has been well-observed that there are often unintended consequences arising at the disciplinary and model interfaces. Another challenge in such model-based convergent engineering is that the behaviors are often unintuitive thus, communicating simply the system response often does not provide sufficient information to make design decisions. This seminar presents multiscale and multiphysics design optimization research that aims to utilize the high-fidelity model information and optimize a coupled system to assist in the design decision making. A series of numerical case studies will demonstrate that there are potentially orders of magnitude performance improvements in future engineering by taking the integrated coupled approach to a complex system design.