Multidisciplinary Design Optimization for a Centrifugal Compressor Based on Proper Orthogonal Decomposition and an Adaptive Sampling Method

Abstract

A centrifugal compressor is required to increase aerodynamic efficiency, ensure structural integrity, and reduce processing costs. This paper presents a dimension reduction technique based on proper orthogonal decomposition (POD) in combination with an adaptive sampling method to reduce computational costs. Design of experiment (DOE) is first used to choose initial sampling points. Then, parts of the sampling points are selected to format the snapshot matrix. Subsequently, the number of principal components to be retained is determined after POD analysis. An adaptive sampling point adding approach is used to increase new sampling points. The approach places more points around the regions of initial optimum designs by learning the information from previous data through POD analysis. Finally, the POD coefficients are selected to act as new design variables in the following multidisciplinary design optimization process. The method is first tested by three mathematical benchmark functions. The proposed method is then used to optimize a centrifugal compressor, of which the results are verified by tests. A normalized isentropic efficiency improvement of 3.7% and 3.0% in the maximum speed state and cruise state has been obtained after optimization. Additionally, the processing costs are reduced by about 30% owing to the number of blades reduced.