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Research Papers

A Novel Global Optimization Algorithm and Its Application to Airfoil Optimization

[+] Author and Article Information
B. Yang

Gas Turbine Institute,
Shanghai Jiaotong University,
No. 800 Dongchuan Road,
Shanghai 200000, China
e-mail: byang0626@sjtu.edu.cn

Q. Xu

Shanghai Electric Power Generation
R & D Center,
1st Floor, Building A,
No. 333, West Yindu Road,
Shanghai 201612, China
e-mail: xuqiang@shanghai-electric.com

L. He

Shanghai Electric Power Generation
R & D Center,
1st Floor, Building A,
No. 333, West Yindu Road,
Shanghai 201612, China
e-mail: helei@shanghai-electric.com

L. H. Zhao

Shanghai Electric Power Generation
R & D Center,
1st Floor, Building A,
No. 333, West Yindu Road,
Shanghai 201612, China
e-mail: zhaolh@shanghai-electric.com

Ch. G. Gu

School of Mechanical Engineering,
Shanghai Jiaotong University,
No. 800 Dongchuan Road,
Shanghai 200000, China
e-mail: cggu2006@126.com

P. Ren

Shanghai Electric Power Generation
R & D Center,
1st Floor, Building A,
No. 333, West Yindu Road,
Shanghai 201612, China
e-mail: renping2@shanghai-electric.com

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received September 15, 2014; final manuscript received September 22, 2014; published online November 26, 2014. Editor: Ronald Bunker.

J. Turbomach 137(4), 041011 (Apr 01, 2015) (10 pages) Paper No: TURBO-14-1242; doi: 10.1115/1.4028712 History: Received September 15, 2014; Revised September 22, 2014; Online November 26, 2014

In this paper, a novel global optimization algorithm has been developed, which is named as particle swarm optimization combined with particle generator (PSO–PG). In PSO–PG, a PG was introduced to iteratively generate the initial particles for PSO. Based on a series of comparable numerical experiments, it was convinced that the calculation accuracy of the new algorithm as well as its optimization efficiency was greatly improved in comparison with those of the standard PSO. It was also observed that the optimization results obtained from PSO–PG were almost independent of some critical coefficients employed in the algorithm. Additionally, the novel optimization algorithm was adopted in the airfoil optimization. A special fitness function was designed and its elements were carefully selected for the low-velocity airfoil. To testify the accuracy of the optimization method, the comparative experiments were also carried out to illustrate the difference of the aerodynamic performance between the optimized and its initial airfoil.

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References

Figures

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Fig. 1

Flow chart of PSO–PG

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Fig. 2

Griewank function: (a) xi ∈ [-600,600], (b) xi ∈ [-50,50], and (c) xi ∈ [-5,5]

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Fig. 3

Simulation experiments results: (a) tests of dependence on inertia weight, (b) tests of dependence on maximum flying velocity, (c) tests of dependence on positive constants, and (d) tests of dependence on particle size

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Fig. 4

Geometry comparison: dashed line—NACA 63012 and solid line—optimized

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Fig. 5

Single airfoil performance (cal., Re = 3.25 × 105)

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Fig. 6

Velocity contours: (a) α = 2.5 deg, NACA 63012; (b) α = 2.5 deg, the optimized; (c) α = 12.5 deg, NACA 63012; and (d) α = 12.5 deg, the optimized

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Fig. 7

Experimental rig: 1—centrifugal fan, 2—regulator, 3—test cascade, 4—angle scale, A—measure point for volume rate, B—measure point at inlet, C—measure point at outlet

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Fig. 8

Cascade performance (exp., Re = 3.25 × 105)

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Fig. 9

Pressure distribution: (a) cascade (α=5.32), single airfoil (α=5) and (b) cascade (α=12.67), single airfoil (α=12.5)

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