Performance and model calibration ofhigh-pressure compressors
Abstract: To fully determine the performances of high-pressure compressors (HPC), modeling and partial tests can beperformed to characterize its different operating conditions. Nevertheless, there are still noticeable errorsbetween the results of these models and the tests: deformations and differences can appear, and notably becauseof a modeling defect or the assumptions made during the analysis (measurement corrections for example). Tomodel the closest to the tests, it is therefore necessary to include all the different elements impacting theperformances of the compressor performance map to model the operating points correctly. The purpose ofthe thesis is to take in charge a new software that predicts the performances of high-pressure compressors,built in Python where a compressor can be decomposed in elementary axial and/or centrifugal compressorsusing their respective compressor performance map. By a stacking technique it is possible to characterize theoperating point of the main compressor by knowing the inlet and the outlet conditions, and to include severaldeformation models such as Reynolds number effects, Variable Stator Vanes (VSV) effects, or tip clearanceeffects on the performances of the compressor. A calibration function also allows the development of newversions of the previous deformations models and it quantifies the unknowns and uncertainties betweencomputed and tested results. Several working points at off-design conditions were computed with specificoperating conditions such as the opening of a handling bleed valve. Deformations models of this specificcondition was built to mitigate the uncertainties between computed and tested results and include thephenomenon to the modeling. Several deformations models were also compared for efficiency purposes.
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