Non-probabilistic reliability-based design optimization of stiffened shells under buckling constraint

摘要: Stiffened shells are affected by numerous uncertainty factors, such as the variations of manufacturing tolerance, material properties and environment aspects, etc. Due to expensive experimental cost stiffened shell, only a limited quantity statistics about its factors available. In this case, an unjustified assumption probabilistic model may result in misleading outcomes reliability-based design optimization (RBDO), non-probabilistic convex method is promising alternative. study, hybrid based on single-ellipsoid proposed minimize weight with uncertain-but-bounded variations, where adaptive chaos control (ACC) applied ensure robustness search process model, particle swarm (PSO) algorithm together smeared stiffener utilized guarantee global optimum design. A 3 m-diameter benchmark example illustrates advantage over RBDO deterministic methods for shell variations.