A Sequential Robust Approach for Multi-Disciplinary Design Optimization With Uncertainty

The real challenge for multi-disciplinary design optimization (MDO) problems to gain a robust solution is the propagation of uncertainty from one discipline to another. Most existing methods only consider a MDO problem in deterministic manner or find a solution which is robust for a single-disciplinary optimization problem. These rare methods for solving MDO problems under uncertainty are usually computational expensive. This research proposes a robust sequential MDO (RS-MDO) approach based on a sequential MDO (S-MDO) framework. Firstly, a robust solution is obtained by giving each discipline full autonomy to perform optimization. Tolerance range is specified for the coupling variable to model uncertainty propagation in the original coupled system. Then the obtained robust extreme points of global variable and coupling variable are dispatched into subsystems to perform optimization sequentially. Additional constraints are added to keep consistency and guarantee a robust solution. To find a solution with such strict constraints, genetic algorithm (GA) is used as a solver in each optimization stage. Since all iterations in the sequential optimization stage can be processed in parallel, this robust MDO approach can be more time-saving. Numerical examples are provided to demonstrate the availability and effectiveness of proposed approach.