Detecting Multidimensional Pareto Fronts During the Optimization of a Return Channel Within a Centrifugal Compressor

Abstract We explain the advantages of using big-data methods for evaluating numerical optimization. The investigation focuses on the performance potential of three-dimensional return channel vanes under realistic manufacturing constraints. Based on an analysis of an optimization database, this paper presents a systematic approach for analysis and design guidelines for three-dimensional return channel vanes. To this end, a validated numerical setup was developed on the basis of experimental investigations, followed by a numerical optimization using a genetic algorithm and an artificial neural network. The optimization database was analyzed with a dimension reduction method called t-stochastic neighbor embedding. This method enabled linking geometric design features with physical correlations and, finally, with the objective functions of the optimization. With the help of the detected correlations within the database, it has been possible to work out a method for deciding on the selection of a design on the Pareto front and to draw new relevant conclusions. The systematic use of big-data methods proposed enables a more penetrating insight to be gained into numerical optimization, which is more general and relevant than those gained by simply comparing a single optimized design with a reference design. An analysis of the Pareto front reveals that 0.6% efficiency can be exchanged for 20% more homogeneous outflow.