Stochastic shape optimization via design-space augmented dimensionality reduction and RANS computations

The paper presents how to efficiently and effectively solve stochastic shape optimization problems by combing Reynolds-averaged Navier-Stokes (RANS) equation solvers with design-space augmented dimensionality reduction (ADR). This study has been conducted within the NATO Science and Technology Organization, Applied Vehicle Technology, Task Group AVT-252 "Stochastic Design Optimization for Naval and Aero Military Vehicles." The application pertains to the robust and the reliability-based robust design optimization of a destroyer hull-form for resistance in calm water and waves and seakeeping performance, under stochastic environmental and operating conditions (speed, sea state, heading). The current work extends previous research by the authors, presented at earlier AIAA conferences [1-3], where only potential flow solvers were used. In the present work, the expected value of the total resistance is reduced respectively by 4.4 and 3% in calm water and waves. An 8% improvement of the seakeeping performance is also achieved. Design-space assessment by ADR is demonstrated to be a viable option in solving the curse of dimensionionality in shape optimization, especially when high-fidelity CPU-expensive solvers are used.