Reliability-Based Design Optimization of a Transonic Compressor

A multiobjective, reliability-based design optimization method for computationally intensive problems is proposed. In this method we use a genetic algorithm to facilitate the multiobjective optimization. To further improve the convergence of the genetic algorithm, we augment it with a local search. Reliability analysis is performed using Monte Carlo simulation. Quadratic design response surfaces are utilized to filter the noise from the Monte Carlo simulation and facilitate the multidisciplinary design optimization. In addition, response surface approximations greatly reduce the computational cost. To improve the accuracy of probability computation in the regions of low probability of failure and to provide useful information for the optimization, oprobabilistic sufficiency factor is used as an alternative measure of safety. To demonstrate the capabilities of this approach, we employ it to optimize the NASA rotor67 transonic blade. Numerical results show that with this proposed approach we can obtain a reliable design with better aerodynamic performance and less weight. Error analysis is also reported so that readers can understand not only the advantages but also the disadvantages of this approach.