Probability and variance-based stochastic design optimization of a radial compressor concerning fluid-structure interaction

Abstract

Since the engineering of turbo machines began the improvement of specific physical behaviour, especially the efficiency, has been one of the key issues. However, improvement of the efficiency of a turbo engine, is hard to archive using a conventional deterministic optimization, since the geometry is not perfect and many other parameters vary in the real approach. In contrast, stochastic design optimization is a methodology that enables the solving of optimization problems which model the effects of uncertainty in manufacturing, design configuration and environment, in which robustness and reliability are explicit optimization goals. Therein, a coupling of stochastic and optimization problems implies high computational efforts, whereby the calculation of the stochastic constraints represents the main effort. In view of this fact, an industrially relevant algorithm should satisfy the conditions of precision, robustness and efficiency. In this paper an efficient approach is presented to assist reducing the number of design evaluations necessary, in particular the number of nonlinear fluid-structure interaction analyses. In combination with a robust estimation of the safety level within the iteration and a final precise reliability analysis, the method presented is particularly suitable for solving reliability-based structural design optimization problems with ever-changing failure probabilities of the nominal designs. The applicability for real case applications is demonstrated through the example of a radial compressor, with a very high degree of complexity and a large number of design parameters and random variables.