Investigation Of Aerodynamic Analysis And Optimization For Highly Loaded Axial Flow Compressors

Multistage axial flow compressor is the core component in modern gas turbines.Its performance directly affects the efficiency of power equipment.Nowadays the compressors are continuously developed to achieve higher stage loading while the growth of efficiency is maintained,thus making the stage matching and aerodynamic optimization a big issue for the design process.In order to support the independent research and development,it is essential to establish an integrated system including the aerodynamic analysis and optimization for overall performance and aerodynamic configuration of modern compressors.Based on the above background,the current research is conducted regarding the investigation of aerodynamic analysis and optimization for highly loaded axial flow compressors.A fast,reliable and robust analysis and optimization framework for variable vanes is developed and applied successfully;and the aerodynamic analysis of the compressor in the semi-closed Brayton cycle with multiple working fluid is also conducted.In this dissertation,a mean-line method based on blade stack approach is developed for the aerodynamic analysis of compressors.Different types of empirical models and correlations are integrated into a model library.To validate the method,series of test cases are conducted to examine the performances of the method in different aspects.The method is then employed in the design and analysis of a 5-stage compressor.The numerical results of overall performance and aerodynamic configuration both at design-and part-speed conditions are analyzed and checked by the comparison with experimental investigations.A more in-depth study is conducted with the help of CFD to reveal the reason of the efficiency decrease at lower speed conditions.Based on the developed mean-line method,an aerodynamic optimization framework of stator vane settings for multi-stage compressors with the integration of artificial intelligence technology is established.For the purpose of universality,usability and scalability of the optimization framework,the artificial neural network is incorporated into the optimization system acting as a surrogate model.An upgraded genetic algorithm with the advanced strategies,including the elitism operator,blend crossover,non-uniform mutation and self-adaption parameters is employed to promote the searching efficiency and solution globality.The optimization is conducted on the previous 5-stage compressor.Regardless of the assumption of quasi-one-dimensional flow,the effectiveness of the optimization framework in dealing with the stage-mismatching is demonstrated by the results.Furthermore,additional research is carried on for the aerodynamic design of compressor in the promising semi-closed Brayton cycle.The model library developed in the current research is used to upgrade a previously developed throughflow method.The throughflow analysis is subsequently conducted on the 5-stage compressor with different working medium trying to offer some suggestions for the future modification and design of multistage axial flow compressor in the new cycle.