Yonsei University · Engineering
Professor Jongsoo Lee's research lab specializes in advanced simulation-driven design optimization, with a focus on multidisciplinary engineering systems involving structural mechanics, materials behavior, and nuclear energy applications. The lab develops and applies meta-modeling techniques—such as Kriging and surrogate modeling—combined with evolutionary algorithms like genetic and micro-genetic algorithms to solve complex, constrained optimization problems in aerospace, automotive, and nuclear engineering. Research emphasizes improving computational efficiency, constraint feasibility, and reliability in design under uncertainty, particularly for high-performance materials and components under extreme conditions. The lab also integrates statistical and probabilistic methods to model material fatigue, viscoplasticity, and system-level performance in safety-critical applications.
Figures are computed from collected data and may differ slightly.
Abstract : The present paper describes an adaptation of genetic algorithms in the design of large-scale multidisciplinary optimization problems. A hingeless composite rotor blade is used as the test problem, where the formulation of the objective and constraint functions requires the consideration of disciplines of aerodynamics, performance, dynamics, and structures. A rational decomposition approach is proposed for partitioning the large-scale multidisciplinary design problem into smaller, more
When Kriging is used as a meta-model for an inequality constrained function, approximate optimal solutions are sometimes infeasible in the case where they are active at the constraint boundary. This article explores the development of a Kriging-based meta-model that enhances the constraint feasibility of an approximate optimal solution. The trust region management scheme is used to ensure the convergence of the approximate optimal solution. The present study proposes a method of enhancing the co
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