Design Optmization Methodology of High Pressure Axial Compressors

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Saeedipour, Mahdi | 2011

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 42867 (45)
University: Sharif University of Technology
Department: Aerospace Engineering
Advisor(s): Ghorbanian, Kaveh
Abstract:
Optimization methodologies with an emphasis on turbomachinery applications are of great interest. In the present study, a framework is proposed for the multi-objective optimization of a compressor blade using a coupled approximator and optimizer modules, as a mean for lowering the vast computational costs. The proposed framework consists of three main units: a CFD solver, an approximator unit, and an optimizer module. In this regard, a multi-layer perceptron artificial neural network is used as the approximator module while a multi-objective genetic algorithm, the non-dominated sorting genetic algorithm-NSGA II, is employed as the optimizer unit of the framework. In addition, a commercial CFD code (Ansys-CFX) is utilized as the CFD solver. The novel idea of the proposed framework is the two-stepped optimization. As the first test case, multi-objective optimization of tandem blade configuration is investigated. Further, the transonic axial flow NASA Rotor-67 is examined with a target on maximizing the total pressure ratio as well as the isentropic efficiency. The results indicate of possible performance improvements.
Keywords:
Multiobjective Optimization ; Turbomachines ; Genetic Algorithm ; Artificial Neural Network ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method ; Hybrid Algorithm

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