Niklas Reuter*, Benedikt Kriegesmann This contribution addresses the robust design optimization of stiffened cylinders under combined loading. The buckling load of axially compressed cylinders is known to be highly sensitive to random geometric imperfections. The consideration of random effect in the design optimization of cylinders is therefore crucial to avoid determining a highly sensitive design [1]. The sensitivity to imperfections however changes significantly for other types of loading such as torsion or combined loadings [2]. This works aims to optimize stiffened cylinders such that the cylinder is not only improved for the explicitly simulated load cases, but also for an arbitrary combination of these load cases. To achieve that goal, a robust design optimization of the thickness distribution of the stiffeners is carried out. The resulting large number of design variables requires the use of gradient-based optimization algorithms and hence, the computation of the gradients of the probabilistic objective function. The buckling loads are computed with nonlinear finite element analyses by evaluating the resulting force-displacement graph. The optimization objective is to increase the mean buckling load and decrease its standard deviation for each load case considered. At the same time, the correlation of the buckling load of these load cases shall be increased assuming that not simulated combinations of load cases are improved as well. The stochastic moments (mean, variance and covariance) and their gradients are computed efficiently using the first-order second-moment (FOSM) approach [3]. REFERENCES [1] B. Kriegesmann, R. Rolfes, E. L. Jansen, I. Elishakoff, C. Hühne, and A. Kling. Design Optimization of Composite Cylindrical Shells under Uncertainty. Computers, Materials, & Continua, Vol. 32, pp. 177–200, 2012. [2] N. Reuter, and B. Kriegesmann. Probabilistic analysis of cylindrical shells under continuously varying load combinations. Thin-Walled Structures, Vol. 215, 113319, 2025. [3] J. C. Krüger, M. Kranz, T. Schmidt, R. Seifried, and B. Kriegesmann. An Efficient and Non- Intrusive Approach for Robust Design Optimization with the First-Order Second-Moment Method. Computer Methods in Applied Mechanics and Engineering, Vol. 414, 116136, 2023.