Radial basis function-based shape optimization of centrifugal impeller using sequential sampling

Abstract

Multi-objective optimization algorithms become more appropriate in general aerodynamic design problems, despite the number of objective functions evaluated by computational fluid dynamics (CFD) calculations. This paper presents a multi-objective aerodynamic shape optimization (ASO) procedure implemented to minimize the optimization cycle time. Its main ideas consist in replacing some of CFD evaluations by those provided by an approximate meta-model known as the radial basis functions (RBFs). In order to minimize the size of training samples when constructing the RBF meta-model, a sequential sampling strategy based on an iterative refinement of training samples database is considered. When compared with one-stage sampling, the sequential sampling has allowed more reduction in the computational time and a better accuracy in the approximation of the objectives by RBF meta-model. The MASSOUD “multidisciplinary aero/struc shape optimization using deformation” and Bspline techniques are combined to reduce the number of control points for the shape deformation or optimization variables. Once the database composed of a set of geometries is constructed and CFD is used to evaluate the objectives for each element of the database, RBF is used to approximate the objectives with respect to the optimization variables, and finally the NSGA-II “Non-Dominated Sorting Genetic Algorithm” is applied to produce the pareto optimal fronts. An application aimed to optimize the NASA low speed centrifugal compressor (LSCC) resulted in an optimal design within a reasonable computational time and produced the Pareto fronts of trade-offs enhancing the baseline performance.