a novel mean-line design algorithm for orc axial turbines with choked flow consideration

This paper presents a novel mean-line design algorithm for axial turbines in organic rankine cycle (orc) applications, specifically addressing the challenges associated with choked flow conditions. conventional turbine design methods struggle to account for choked flow in early design stages, leading to potential inaccuracies in high-performance applications. to overcome this, the proposed algorithm integrates both design and analysis modes of mean-line method, allowing for iterative refinement of turbine geometry and performance evaluation under various boundary conditions. a multi-objective optimization framework coordinates the interaction between design and analysis modes, with real-time checking for choked conditions to ensure optimal performance. the algorithm penalizes designs that approach choked flow conditions under expected operational parameters, enabling fine-tuning of geometry and flow characteristics to maximize efficiency. results demonstrate that the algorithm successfully identifies turbine configurations that operate at optimal performance points near the choked flow threshold, as evidenced by mach number assessments at critical locations within the turbine. the implementation of this method offers a robust approach to turbine design, with significant implications for the efficiency and reliability of orc systems.