Data Driven Modelling of Centrifugal Compressor Maps for Control and Optimization Applications

Korkmaz, Buse Sibel; Mercangöz, Mehmet

doi:10.23919/ecc55457.2022.9838008

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…To reduce the calculation cost, the surrogate model has been widely used to predict the performance curve of the centrifugal compressor in recent years. Korkmaz et al [3] used Gaussian procession (GP) modeling to predict the performance graph of centrifugal compressors, and compared it with the prediction results of multiple regression model. It showed that the prediction accuracy of GP was better than that of multiple regression model, especially under the condition of lower training parameters.…”

Section: Introductionmentioning

confidence: 99%

Prediction of centrifugal compressor performance curves based on multi-task Gaussian process

Fang

Zhu

et al. 2023

Third International Conference on Mechanical Design and Simulation (MDS 2023)

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The performance curves are crucial to the safety operation of the compressor. As a method to obtain the performance curves of centrifugal compressor, Computational Fluid Dynamics(CFD)has the advantages of high data accuracy, but its calculation cost is expensive. In order to reduce the calculation cost, surrogate model has been widely used to predict the performance curve of the centrifugal compressor in recent years. In previous studies, PR and EFF at various speed lines were predicted separately neglecting the similarities across speed lines. Considering the correlation between different speeds, the multi-task Gaussian Process (MTGP) method was adopted in this paper to predict the performances curves of a centrifugal compressor in a more efficient manner than the conventional models by leveraging the cross-line information.To validate the effectiveness of the method, the predictions from MTGP are compared with that of multi-fidelity method CoKriging and single-fidelity Kriging. Finally, we found that MTGP has the highest prediction accuracy under the same data cost.

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Section: Introductionmentioning

confidence: 99%