Mean-line analysis and optimal design of turbines using a proper algorithm for choked conditions

• Published in: The International journal of heat and fluid flow 2024-07, Vol.107, p.109370, Article 109370
• Main Authors: Esfahanian, Vahid, Taghavi, Arman, Shahbazi, Ali Akbar
• Format: Article
• Language: English
• Subjects: Choked flow; Mean-line analysis; Mean-line design; Performance estimation; Turbine optimization
• ISSN: 0142-727X; 1879-2278
• DOI: 10.1016/j.ijheatfluidflow.2024.109370

Compressible flow turbines usually work under choked conditions, requiring an analysis for the performance estimation. The Mean-line modeling is a fast and accurate method extensively used for the analysis and design purposes. However, it has not already been well-investigated for axial flow turbines specially in choked conditions. The present study investigates the issue from three novel aspects. First, a detailed study is accomplished on the inconsistencies of Mean-line method respect to the physics of real flows. It is discussed that the conditions related to maximum mass flow rate through a nozzle and the Mach number one at the throat are different. In fact, the latter condition is not quite physical. Second, a complete algorithm is utilized to implement the aforementioned considerations for the analysis of turbines. This algorithm includes an innovative Blade Solution Algorithm (BSA) and Turbine Solution Algorithm (TSA). The BSA solves the blade rows of turbine using the basic laws of Physics and real-gas properties. In the TSA, a robust and efficient procedure is presented so the solution of turbine converges in wide ranges of boundary conditions with minimum iterations. The results demonstrate that the algorithm generate close maps to the previous studies. Eventually in the third part, since a fast and robust Mean-line algorithm is crucial in optimization, the code is utilized for a design based on optimization using the Genetic Algorithm. In this procedure, all geometrical parameters are optimized as decision parameters rather than the use of old and approximate correlations. This is done along with novel constraints, which not only maximizes efficiency, but also puts the output power, mass flow rate and turbine size in appropriate ranges. The optimization results in a 3.5% rise in efficiency. •Two efficient and real-time approaches are proposed for Mean-line analysis of axial-flow turbines.•All the procedures are based on the real-gas models.•The performance of turbines can be estimated under a wide range of working conditions, including choked conditions.•Optimization is accomplished on an experimental turbine using the Genetic Algorithm, resulting in an almost 3.5% rise in efficiency.•The impact of various parameters on the turbine efficiency are discussed.