An Optimization Procedure for the Aerodynamic Model Tuning of Centrifugal Compressor Stages
This paper presents an automated optimization procedure for tuning and optimizing the performance parameters of centrifugal compressor stages in order to improve the accuracy of a 1D performance prediction tool and performance database. An in-house, well-validated 1D tool is used to predict the performance of centrifugal compressor stages. The stages are usually tested under similitude conditions in order to verify the predicted performance with the experimental data. Continuous improvements have been done on the tool to improve its accuracy, but the tuning to test data is still done manually and separately for each tested design flow coefficient. As a further leap in this activity, an in-house developed optimization code (PEZ) is interfaced with the 1D prediction tool to provide the best possible solution within the given tuning limits. This provides the possibility to use an extended number of tuning parameters and to tune the entire design family simultaneously, thereby ensuring a smooth evolution of the tuning parameters within the database. The optimization plan consists of a Differential Evolution (DE) genetic algorithm followed by a simplex-based optimization method (AMOEBA) with an objective of reducing the Root Mean Square (RMS) value of the error with the specified constraints. The procedure was successfully challenged with several families of similar stages but with various design corrected mass flows, by setting different objective/constraints combinations. The Optimizer was able to reduce the total RMS value of the error by approximately 80% with respect to the baseline for one of the recently tuned families. The result is a minimal deviation between predicted and experimental data for entire families, as well as a significant time reduction compared to the previous tuning methodology.