A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

被引:16
作者
Maral, H. [1 ]
Senel, C. B. [1 ]
Deveci, K. [2 ]
Alpman, E. [3 ]
Kavurmacioglu, L. [1 ]
Camci, Cengiz [4 ]
机构
[1] Istanbul Tech Univ, Dept Mech Engn, TR-34437 Istanbul, Turkey
[2] Istanbul Tech Univ, Energy Inst, Ayazaga Campus, TR-34469 Istanbul, Turkey
[3] Marmara Univ, Dept Mech Engn, Goztepe Campus, TR-34722 Istanbul, Turkey
[4] Penn State Univ, Dept Aerosp Engn, Aeroheat Transfer Lab, University Pk, PA 16802 USA
来源
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME | 2020年 / 142卷 / 02期
关键词
multi-objective optimization; artificial neural networks; genetic algorithm; tip leakage flow; squealer tip; axial turbine; ARTIFICIAL NEURAL-NETWORK; LEAKAGE FLOW; BLADE TIP; HEAT-TRANSFER; SIMULATION; PERFORMANCE; DESIGN;
D O I
10.1115/1.4044721
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The "squealer" design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.
引用
收藏
页数:12
相关论文
共 43 条
[31]   Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network [J].
Magnier, Laurent ;
Haghighat, Fariborz .
BUILDING AND ENVIRONMENT, 2010, 45 (03) :739-746
[32]  
Maral H, 2016, PROCEEDINGS OF THE ASME TURBO EXPO: TURBINE TECHNICAL CONFERENCE AND EXPOSITION, 2016, VOL 2B
[33]   TIP LEAKAGE FLOW IN A LINEAR TURBINE CASCADE [J].
MOORE, J ;
TILTON, JS .
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, 1988, 110 (01) :18-26
[34]   Neural network design for engineering applications [J].
Rafiq, MY ;
Bugmann, G ;
Easterbrook, DJ .
COMPUTERS & STRUCTURES, 2001, 79 (17) :1541-1552
[35]   The Reduction of Over Tip Leakage Loss in Unshrouded Axial Turbines Using Winglets and Squealers [J].
Schabowski, Zbigniew ;
Hodson, Howard .
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, 2014, 136 (04)
[36]   An aerothermal study of the influence of squealer width and height near a HP turbine blade [J].
Senel, C. B. ;
Maral, H. ;
Kavurmacioglu, L. A. ;
Camci, C. .
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2018, 120 :18-32
[37]  
Sugiyama M, 2006, J MACH LEARN RES, V7, P141
[38]   Numerical simulation of tip leakage flows in axial flow turbines, with emphasis on flow physics: Part I - Effect of tip clearance height [J].
Tallman, J ;
Lakshminarayana, B .
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, 2001, 123 (02) :314-323
[39]   Combining various wall materials for encapsulation of blueberry anthocyanin extracts: Optimization by artificial neural network and genetic algorithm and a comprehensive analysis of anthocyanin powder properties [J].
Tao, Yang ;
Wang, Ping ;
Wang, Jiandong ;
Wu, Yue ;
Han, Yongbin ;
Zhou, Jianzhong .
POWDER TECHNOLOGY, 2017, 311 :77-87
[40]  
Task Force on Sudden Infant Death Syndrome, 2016, PEDIATRICS, V138, pe2016