The multidisciplinary design optimization of high temperature blades is a typical high dimensional, computational expensive and black box problem, since too many design variables are involved and large amounts of CFD evaluations are usually demanded to ensure the convergence of global algorithms like GAs. By integrating the technique of analysis of variance (ANOVA), Self-adaptive Multi-objective Differential Evolution algorithm (SMODE), Conjugate Heat Transfer analysis and 3D parameterization method for both blade and the cooling holes, a knowledge-based aero-thermal multidisciplinary design optimization of a high temperature blade is carried out. Through the ANOVA analysis, an insight into the relation between significant design variables and the blade aero-thermal performance is obtained. By eliminating the variables which have small effects on the blade aero-thermal performance, the number of design variables for the optimization process is decreased from 36 to 15, which is verified by the numerical simulations. After optimization, 9 optimal Pareto solutions are achieved. Detailed aero-thermal analysis of typical optimal Pareto solutions indicates that the performance of optimal designs is significantly better than the reference design. Therefore, the effectiveness of the developed knowledge-based multidisciplinary design method for high temperature blades is demonstrated.
Multidiscilinary design optimization for aircraft wing using response surface method, genetic algorithm, and simulated annealing