Optimization problems in many engineering applications are usually considered as complex subjects. Researchers are often obliged to solve a multi-objective optimization problem. Several methodologies such as genetic algorithm (GA) and artificial neural network (ANN) are proposed to optimize multi-objective optimization problems. In the present study, various levels of sweep and lean were exerted to blades of an existing transonic rotor, the well-known NASA rotor-67. Afterward, an ANN optimization method was used to find the most appropriate settings to achieve the maximum stage pressure ratio, efficiency, and operating range. At first, the study of the impact of sweep and lean on aerodynamic and performance parameters of the transonic axial flow compressor rotors was undertaken using a systematic step-by-step procedure. This was done by employing a three-dimensional (3D) compressible turbulent model. The results were then used as the input data to the optimization computer code. It was found that the optimized sweep angles can increase the safe operating range up to 30% and simultaneously increase the pressure ratio and subsequently the efficiency by 1% and 2%. Moreover, it was found that the optimized leaned blades, according to their target function, had positive (forward (FW)) or negative (backward (BW)) optimized angles. Leaning the blade at the optimum point can increase the safe operating range up to 12% and simultaneously increase the pressure ratio and subsequently the efficiency by 4% and 5%.
Multidisciplinary Design and Optimizations of Swept and Leaned Transonic Rotor
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received April 30, 2017; final manuscript received May 30, 2017; published online August 23, 2017. Editor: David Wisler.
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Razavi, S. R., Sammak, S., and Boroomand, M. (August 23, 2017). "Multidisciplinary Design and Optimizations of Swept and Leaned Transonic Rotor." ASME. J. Eng. Gas Turbines Power. December 2017; 139(12): 122601. https://doi.org/10.1115/1.4037456
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