Michael Kokkolaras

Primary Research Theme: Design and Manufacturing
Research Group/Lab: Systems Optimization Lab (SOL)

Multidisciplinary design optimization of complex engineering systems; simulation-based engineering design; uncertainty quantification; optimization theory and algorithms; decomposition and coordination methods; design validation; platform-based product families, systems of systems and product-service systems; transportation (automotive and aerospace) and energy systems

The complexity and increasing interconnectivity of modern engineering systems necessitate an analytical, decomposition-based approach to optimal design: Subsystem interactions must be taken into account to ensure system integration and optimality; component design specifications need to be determined, as design targets are given only for the systems; uncertainties need to be quantified and propagated. This requires coordination and optimization of multiple disciplines, appropriate uncertainty modeling and validation of obtained design solutions.

Our research focuses on developing methodologies to address these issues. We use mathematical programming to model, coordinate and solve the decomposed problems so that large and complex problems can be solved efficiently. We adopt different quantification and propagation approaches depending on the amount of available information to account for uncertainties, and use statistics-based methods to quantify design confidence. While these methodologies are being developed to be applicable to any engineering system, emphasis is given on transportation (automotive and aerospace) and energy applications.