Yuanzineng kexue jishu (Sep 2023)
Uncertainty Quantification of Performance of Supercritical Carbon Dioxide Compressor
Abstract
The supercritical carbon dioxide (S-CO2) based power cycle is very promising for its potentially higher efficiency and compactness compared with steambased power cycle. Compressor is the critical component of the S-CO2 based power cycle. The inlet flow state of the S-CO2 compressor is typically set close to the critical point of the fluid. As a result, small variations in the inlet flow state during operation might cause significant variations in fluid properties, which have an impact on the compressor aerodynamic performance. Therefore, it is necessary to investigate the influence of inlet flow state uncertainty on the compressor performance. In this work, the numerical simulation of an S-CO2 compressor was carried out with both the meanline model and the three-dimensional computational fluid dynamics (CFD) method. The efficiency and the pressure ratio with the meanline model show obvious difference with the CFD results. The accuracy of the meanline model relies on verified empirical correlations. As the existing empirical correlations in open literature and commercial software are typically derived from air compressor test data, due to the lack of S-CO2 compressor test data, the accuracy of the meanline model in the existing commercial software for the S-CO2 compressor has not been systematically verified. Compared with the meanline model, the three-dimensional CFD simulation is based on a more accurate geometric model and the flow model is less simplified. Therefore, based on the more time-consuming CFD results, the uncertainty quantification of the aerodynamic performance of the S-CO2 compressor with stochastic inlet total temperature variations was carried out using the arbitrary polynomial chaos (aPC) expansion. The aPC method performs polynomial chaos expansion based on the statistical moments of the data and is suitable for both continuous distributions and discrete data sets of uncertain variables. For the case in this work, when using the 4th order aPC expansion, the relative error of the efficiency variance is 3.2%, and the relative error of the flow variance is 2.1%. The results show that the aPC method can efficiently and accurately predict the statistics of performance parameters, especially when the statistics of random parameters are limited and discrete. A small variation in the inlet temperature leads to significant variations in the mass flow rate and isentropic efficiency of the S-CO2 compressor, which highlights the importance of robustness with respect to inlet flow state uncertainty in the aerodynamic design of S-CO2 compressors. The aPC method can be used to quantitively evaluate the robustness of S-CO2 compressor performance to the uncertain parameters, which forms a basis for the robust optimal design of the S-CO2 compressor.
