Robust design and optimization of stochastic wind-excited systems: an adaptive kriging-based approach

This research proposes a robust design framework for wind-excited systems in which performance is estimated at a system level in terms of state-of-the-art performance-based design metrics. In particular, the robust design problem is formulated as a stochastic optimization with objective the minimization of the variance of the performance metric. Constraints are also imposed on the initial cost of the system and expected value of the performance metric. To effectively treat the performance metrics within the optimization problem, adaptive kriging models of the deagreggated loss metrics are defined in terms of the second order statistics of the demands. By then relating the demand statistics to the design variables through the concept of the Auxiliary Variable Vector, a deterministic optimization sub-problem is defined that can handle high-dimensional design variable vectors and general stochastic excitation. By solving a sequence of sub-problems, each formulated in the solution of the previous, solutions to the original robust design problem are found. A case study consisting in a large-scale system subject to stochastic wind excitation is used to illustrate the applicability of the proposed framework.This research effort was supported in part by the National Science Foundation (NSF) through grants CMMI-1462084 and CMMI-1562388. This support is gratefully acknowledged

Performance optimization of uncertain and dynamic high-dimensional wind-excited systems

This paper focuses on the development of an efficient design optimization framework for wind-excited systems that is capable of handling not only high-dimensional and complex probability spaces, but also high-dimensional spaces of design parameters. Data-driven simulation models are utilized in assessing the system-level probabilistic measures. To efficiently solve the performance-based design optimization problem, a framework is proposed that is based on approximately decoupling the stochastic simulation from the optimization process. Local approximation models, constructed from results of a single stochastic simulation, are used to define a deterministic composite function that relates the design parameters to the system-level performance metrics. The explicit nature of this relationship is then exploited to define a sequence of deterministic optimization sub-problems that yield solutions to the original stochastic optimization problem. To illustrate the applicability of the proposed approach, a large-scale building system is optimized under stochastic wind tunnel-informed excitations and subject to system-level loss constraints.This research effort was supported in part by the National Science Foundation (NSF) through grants CMMI-1462084 and CMMI-1562388. This support is gratefully acknowledged