Recent developments of axial flow compressors under transonic flow conditions

Frontiers in Automobile and Mechanical Engineering

Srinivas, G.^1 ; Raghunandana, K.^2 ; Satish Shenoy, B.^1

Dept of Aero and Auto Engineering, Manipal Institute of Technology, Manipal University, Manipal, Udupi, Karnataka

576104, India^1

Dept of Mechatronics Engineering, Manipal Institute of Technology, Manipal University, Manipal, Udupi, Karnataka

576104, India^2

关键词: Advanced technology;    Artificial viscosity;    Evolutionary algorithms (EAs);    Optimization techniques;    Potential flow equation;    Response surface methodology;    Transonic conditions;    Turbo-machinery design;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/197/1/012078/pdf
DOI  :  10.1088/1757-899X/197/1/012078

来源: IOP

【 摘 要 】

The objective of this paper is to give a holistic view of the most advanced technology and procedures that are practiced in the field of turbomachinery design. Compressor flow solver is the turbulence model used in the CFD to solve viscous problems. The popular techniques like Jameson's rotated difference scheme was used to solve potential flow equation in transonic condition for two dimensional aero foils and later three dimensional wings. The gradient base method is also a popular method especially for compressor blade shape optimization. Various other types of optimization techniques available are Evolutionary algorithms (EAs) and Response surface methodology (RSM). It is observed that in order to improve compressor flow solver and to get agreeable results careful attention need to be paid towards viscous relations, grid resolution, turbulent modeling and artificial viscosity, in CFD. The advanced techniques like Jameson's rotated difference had most substantial impact on wing design and aero foil. For compressor blade shape optimization, Evolutionary algorithm is quite simple than gradient based technique because it can solve the parameters simultaneously by searching from multiple points in the given design space. Response surface methodology (RSM) is a method basically used to design empirical models of the response that were observed and to study systematically the experimental data. This methodology analyses the correct relationship between expected responses (output) and design variables (input). RSM solves the function systematically in a series of mathematical and statistical processes. For turbomachinery blade optimization recently RSM has been implemented successfully. The well-designed high performance axial flow compressors finds its application in any air-breathing jet engines.