Real gas-based mean-line analysis method for axial compressor performance prediction

This study presents the axial compressor mean-line analysis method that systematically incorporates real-gas thermodynamic effects through the integration of high-resolution property look-up tables. Implemented in the Axial Compressor Performance (ACP) program, this framework extends traditional mean-line methods—previously limited to ideal gas assumptions—to enable accurate performance prediction for compressors operating with non-ideal working fluids, including humid air and supercritical carbon dioxide (S-CO2). The approach is validated through two case studies: (1) the National Aeronautics and Space Administration (NASA) Stage 35 operating with humid air, where the predicted changes in the choked and stall margins and the efficiency trends show strong agreement with the computational fluid dynamics results; and (2) a performance evaluation of a three-stage S-CO2 axial compressor, in which ACP successfully identifies potential design issues at an early stage. The proposed method offers a practical, physics-based tool that enables rapid design iteration and performance validation prior to undertaking more computationally intensive three-dimensional simulations.