Mean-line prediction for sCO₂ axial turbine performance characteristics using a novel method for critical state quick judgement

The axial turbine is a critical component of the supercritical carbon dioxide (sCO₂) cycle system, where fast and accurate off-design performance prediction is essential for improving system optimization and design efficiency. This paper presents a one-dimensional calculation method for real gas turbines. The method introduces a detailed computational preprocessing approach to handle blade choking, enabling rapid determination of the turbine's choked state and accurate performance prediction under both off-design and choked conditions. Validation was conducted using two air turbines and two sCO₂ turbines, including published literature data and experimental data. The results show that the method achieves high accuracy, with a maximum relative error of 0.3% for choking mass flow prediction and efficiency prediction errors consistently below 3%. Additionally, the proposed method significantly enhances computational efficiency, reducing the calculation time for 30 operating points on a characteristic curve to 5.6% of that required by traditional methods. This method provides a reliable and efficient tool for the design and optimization of real gas turbines.