Reducing solid particle erosion of an axial fan with sweep and lean using multidisciplinary design optimization

Abstract

A multidisciplinary design optimization (MDO) system is established to reduce solid particle erosion of an axial induced draft fan with sweep and lean. The method improves the erosion resistance of the fan blade in the aerodynamic design stage through a change of blade sweep and lean. The multidisciplinary design optimization approach takes the place of the traditional time-consuming design method by automatic calculation of the flow field, stress distribution, dynamic frequencies, and erosion distribution for blade, controlled by an optimization strategy. A multi-objective particle swarm optimization (MOPSO) algorithm combined with radial basis function approximation model is employed for finding a compromise between the conflicting demands of high efficiency and low average erosion rate with constraints on the pressure ratio and structural responses for the blade. The Navier–Stokes solver, finite element method (FEM) is used to predict the aerodynamic performance and mechanical performance of the blade, respectively. Particle paths in a viscous flow are calculated using the Lagrangian method and Tabakoff rebound model. And then Tabakoff erosion model is used to predict erosion of the blade surface. Several representative designs are selected along the Pareto front to verify using computer aided engineering tools. A compromise solution is used to analyze in detail. Compared with the reference design, the optimal design increases the η/η₀ slightly by 0.53%, while decreases the ɛ_avg /ɛ_avg0 markedly by 13.7%. The result shows that the optimized blade favors a reduced total pressure due to its forward sweep. The decrease of the ɛ_avg/ɛ_avg0 is attributed to a reduced impact velocity and impact angle. The analysis of variance technique indicates that the blade lean has a direct impact on performances with respect to efficiency, erosion, and von Mises stress of the blade, and the blade sweep law near hub has an immeasurable influence on blade von Mises stress. As a conclusion, it can be drawn that the proposed approach may open a new opportunity for the design of axial fan to reduce erosion damage of blade taking other disciplines into consideration. Meanwhile, the multidisciplinary design optimization system can be extended to other turbomachinery and erosion-resistant design fields.

Keywords

Multidisciplinary design optimization antierosion design solid particle erosion sweep and lean axial fan

Get full access to this article

View all access options for this article.

References

Corsini

Marchegiani

Rispoli

. Predicting blade leading edge erosion in an axial induced draft fan. J Eng Gas Turbines Power 2012; 134: 42601–42601.

Tabakoff

Hamed

Wenglarz

. Particulate flows, turbomachinery erosion and performance deterioration. Von Karman Lect Ser 1988; 89: 24–27.

Wheeler

Wood

. High velocity sand impact damage on CVD diamond. Diamond Relat Mater 2001; 10: 459–462.

Wood

RJK

. Tribo-corrosion of coatings: A review. J Phys D Appl Phys 2007; 40: 5502–5502.

Hamed

Tabakoff

Rivir

. Turbine blade surface deterioration by erosion. J Turbomach 2005; 127: 445–445.

Sumner

Vogan

Lindinger

. Reducing solid particle erosion damage in large steam turbines. Proc Am Power Conf 1985; 47: 196–212.

Kawagishi

Kawassaki

Ikeda

. Protective design and boride coating against solid particle erosion of first-stage turbine nozzles. ASME Power Div 1990; 10: 23–29.

Dai

. Nozzle passage aerodynamic design to reduce solid particle erosion of a supercritical steam turbine control stage. Wear 2007; 262: 104–111.

Wang

Mao

Liu

. Reduction of solid-particle erosion on the control-stage nozzle of a steam turbine through improved end-wall contouring. Proc IMechE, Part C: J Mechanical Engineering Science 2010; 224: 2199–2199.

10.

Taylor

Wright

Place

. The use of sweep and dihedral in multistage axial flow compressor blading—Part I: University research and methods development. J Turbomach 2002; 124: 521–532.

11.

Denton JD and Xu L. The effects of lean and sweep on transonic fan performance. In: ASME paper, 2002.

12.

Govardhan

Kumar

OGK

Sitaram

. Computational study of the effect of sweep on the performance and flow field in an axial flow compressor rotor. Proc IMechE, Part A: J Power and Energy 2007; 221: 315–329.

13.

Sasaki

Breugelmans

. Comparison of sweep and dihedral effects on compressor cascade performance. J Turbomach 1998; 120: 454–464.

14.

Verstraete

Alsalihi

Van den Braembussche

. Multidisciplinary optimization of a radial compressor for microgas turbine applications. J Turbomach 2010; 132: 31004–31004.

15.

Pierret

Filomeno Coelho

Kato

. Multidisciplinary and multiple operating points shape optimization of three-dimensional compressor blades. Struct Multidiscip Optim 2007; 33: 61–70.

16.

Lian

Lion

. Multi-objective optimization of transonic compressor blade using evolutionary algorithm. J Propul Power 2005; 21: 979–987.

17.

Lian

Kim

. Reliability-based design optimization of a transonic compressor. AIAA J 2006; 44: 368–368.

18.

Lian

Oyama

Liou

. Progress in design optimization using evolutionary algorithms for aerodynamic problems. Prog Aerospace Sci 2010; 46: 199–223.

19.

Xi G, Wang Z, Zhang C, et al. Aerodynamic optimization design of vaned diffusers for the 100 kW micro gas turbine’s centrifugal compressor. In: ASME paper, 2008.

20.

Wang H and Xi G. Optimization of high speed centrifugal compressor for a micro gas turbine based on CFD and FEM analysis. In: ASME paper, 2010.

21.

Luo C, Song L, Li J, et al. A study on multidisciplinary optimization of an axial compressor blade based on evolutionary algorithms. Journal of turbomachinery 2012; 135(5): 81--93.

22.

Samad

Kim

. Multi-objective optimization of an axial compressor blade. J Mech Sci Technol 2008; 22: 999–1007.

23.

Shahpar S. A comparative study of optimization methods for aerodynamic design of turbomachinery blades. In: ASME paper, 2000(2000-GT), p.523.

24.

Khan

Wang

. Three-dimensional modeling for wet compression in a single stage compressor including liquid particle erosion analysis. J Eng Gas Turbines Power 2011; 133: 12001–12001.

25.

Corsini

Marchegiani

Rispoli

. Predicting blade leading edge erosion in an axial induced draft fan. J Eng Gas Turbines Power 2012; 134: 42601–42601.

26.

Ghenaiet

Tan

Elder

. Prediction of an axial turbomachine performance degradation due to sand ingestion. Proc IMechE, Part A: J Power and Energy 2005; 219: 273–273.

27.

Haider

Levenspiel

. Drag coefficient and terminal velocity of spherical and nonspherical particles. Powder Technol 1989; 58: 63–70.

28.

Gosman

Ioannides

. Aspects of computer simulation of liquid-fueled combustors. J Energy 1983; 7: 482–490.

29.

Tabakoff W, Grant G and Ball R. An experimental investigation of certain aerodynamic effects on erosion. In: AIAA paper, No. 74-639, 1974.

30.

Ansys C. Ansys CFX-solver theory guide. Ansys Europe, 2010.

31.

Zou

Sun

Qiu

. Numerical simulation of the pulverized coal track in BF tuyere. Iron Steel (China) 2000; 35: 9–11.

32.

Postlethwaite

Nesic

. Erosion in disturbed liquid/particle pipe flow: Effects of flow geometry and particle surface roughness. Corrosion 1993; 49: 850–857.

33.

Grasso

. Usage of numerical optimization in wind turbine airfoil design. J Aircraft 2011; 48: 248–248.

34.

Benini

. Three-dimensional multi-objective design optimization of a transonic compressor rotor. J Propul Power 2004; 20: 559–565.

35.

Denton

. The effects of lean and sweep on transonic fan performance: a computational study. Task Quart 2002; 6: 7–23.

36.

Samareh

. Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization. AIAA J 2001; 39: 877–884.

37.

Wang

Shan

. Review of metamodeling techniques in support of engineering design optimization. J Mech Des 2007; 129: 370–370.

38.

Kansa

. Motivation for using radial basis functions to solve PDEs. RN 1999; 64: 1–1.

39.

Schilling

Carroll

Jr Al-Ajlouni

. Approximation of nonlinear systems with radial basis function neural networks. IEEE Trans Neural Netw 2001; 12: 1–15.

40.

Coello

CAC

Pulido

Lechuga

. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 2004; 8: 256–279.

41.

Jin

Chen

Sudjianto

. An efficient algorithm for constructing optimal design of computer experiments. J Stat Plan Inference 2005; 134: 268–287.