Surrogate model-based optimization for the headform design of an axisymmetric body

The surrogate-based method is proposed to facilitate the optimization of a wide variety design problems. In the present work, the applicability of the optimization framework is demonstrated using the headform design of an axisymmetric body. The goal of this effort is to optimize the headform for satisfying multiple objectives simultaneously, including the cubage coefficient, the absolute value and located position of minimum pressure coefficient. In the surrogate framework, several surrogate models are validated and the PWS (Predicted residual sum of square-based Weighted average Surrogate) model is chosen for our further investigation. The global sensitivity analysis shows that the effects of design variables on objective variables are substantially different and no variable should be eliminated from the analysis. Besides, the design space refinement can improve the accuracy of surrogate model and reduce some poor performance points in the Pareto front. The elitist non-dominated sorting genetic algorithm with a parallel archiving strategy is used to generate the Pareto front. The refinement of objective space leads to a fast selection from optimal solutions and finally the selected optimal solution improves the cubage coefficient and the absolute value with the minimum pressure coefficient through a small penalty in the located position of minimum pressure coefficient. This work confirms the efficiency of the surrogate-based method in the engineering environment.