Three-stage axial compressor reprofiling using mathematical optimization methods
Aircraft engines and power generators
Аuthors
*, **, ***, ****Samara National Research University named after Academician S.P. Korolev, 34, Moskovskoye shosse, Samara, 443086, Russia
*e-mail: oleg.v.baturin@gmail.com
**e-mail: popov@ssau.ru
***e-mail: evgeni0063@yandex.ru
****e-mail: y.d.smirnova@ya.ru
Abstract
Low-pressure compressor (LPC) operation has some peculiarities. First, the LPC stages operate with cold air. For this reason, transonic or subsonic flow exist in LPC. Second, the flow in LPC has complex spatial structure. LPC blade geometry is described by a large number of parameters. For this reason it’s difficult to pick up optimal combination of parameters manually. The solution of this problem is the usage of optimization methods to find the optimal combination of parameters. This approach was tested in this work. The main goal of this work was the LPC modernization for new parameters of gas turbine engine. The goals of the LPC modernization were as follows:
• the LPC total pressure ratio increase of 4% in comparison with the original LPC.
• the LPC rotation frequency increase of 2% in comparison with the original LPC.
• the LPC mass flow rate decrease of 11%.
• the LPC efficiency increase of 1%.
The LPC modernization was performed using optimization methods that implemented in the software package IOSO. To perform optimization the LPC numerical model was created using NUMECA FineTurbo software. It was verified before optimization by comparison of calculated and experimental LPC characteristics. The LPC numerical model was parametric and allowed changing geometry of all LPC blades. The total number of optimization variable parameters described the LPC geometry was 61. The optimization goals were the increase of LPC efficiency and decrease of mass flow rate. Thus, LPC efficiency and mass flow rate were used as the optimization criteria. The set of unimprovable solutions (Pareto set) was obtained as a result of solving optimization task. Pareto set was a compromise between the efficiency increase and the mass flow decrease. Each point from Pareto set had a correspondence with LPC unique geometry represented as an array of optimization parameters. One point of the Pareto set met all the required parameters of modernized LPC. The LPC geometry that guaranteed the efficiency increase of 1,3 %, the total pressure ratio increase of 4% and mass flow rate decrease of 11% in comparison with the original LPC was obtained as a result of the study.
Keywords:
gas-turbine engine, axial flow compressor, numerical modeling, optimization, computational model, efficiency, IOSO, blade airfoilReferences
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