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Research Papers: Hydrodynamic Lubrication

A Modified Particle Swarm Optimization Algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor

[+] Author and Article Information
S. H. Chang

Advanced Institute of Manufacturing with
High-Tech Innovations,
Department of Mechanical Engineering,
National Chung Cheng University,
Ming-Hsiung, Chia-Yi 62102, Taiwan
e-mail: jasperchang0314@gmail.com

Y. R. Jeng

Advanced Institute of Manufacturing with
High-Tech Innovations,
Department of Mechanical Engineering,
National Chung Cheng University,
Ming-Hsiung, Chia-Yi 62102, Taiwan
e-mail: imeyrj@ccu.edu.tw

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received May 23, 2013; final manuscript received November 10, 2013; published online December 27, 2013. Assoc. Editor: Prof. C. Fred Higgs III.

J. Tribol 136(2), 021701 (Dec 27, 2013) (7 pages) Paper No: TRIB-13-1108; doi: 10.1115/1.4026061 History: Received May 23, 2013; Revised November 10, 2013

The performance of an aerostatic bearing with a pocketed orifice-type restrictor is affected by the bearing size, pocket size, orifice design, supply pressure, and bearing load. This study proposes a modified particle swarm optimization (MPSO) algorithm to optimize a double-pad aerostatic bearing. In bearing optimization, the upper and lower bearing designs are independent and several design variables that affect bearing performance must be considered. This study also applies the concept of mutation from a genetic algorithm. The results show that the MPSO algorithm has a global search capability and high efficiency to optimize a problem with several design variables and that the mutation can provide an avenue for particles to escape from a local optimal value.

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Figures

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Fig. 1

(a) A single-pad aerostatic bearing with an orifice-type restrictor. (b) Bearing and pocket designs.

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Fig. 2

Diagram of a double-pad aerostatic bearing

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Fig. 3

Stiffness of the double-pad aerostatic bearing with the same designs of the upper and lower bearings

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Fig. 4

Comparison of the PSO and MPSO algorithms in the first test problem (Np = 5; Vmax = 0.2; K = 0.73; mutation rate = 0%)

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Fig. 5

Search process using the MPSO algorithm in the first test problem (Np = 5; Vmax = 0.2; c1 = 2.0; c2 = 1.00; c3 = 1.1; mutation rate = 0%)

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Fig. 6

Performance of the MPSO algorithm combined with the concept of mutation in the second test problem (Np = 50; Vmax = 0.2; K = 0.58; c1 = 2.2; c2 = 1.0; c3 = 1.1)

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Fig. 7

Bearing optimization using the MPSO algorithm with a mutation rate of 0%

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Fig. 8

Bearing optimization using the MPSO algorithm with a mutation rate of 1.0%

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Fig. 9

Bearing optimization using the MPSO algorithm with a mutation rate of 5.0%

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Fig. 10

Bearing optimization using the MPSO algorithm with a mutation rate of 10.0%

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Fig. 11

Performance of the optimized double-pad aerostatic bearing (c = 40 μm; upper bearing: L = B = 0.05 m, l = b = 3 cm, Ps = 5 kg/cm2, do = 0.1 mm, Cd = 0.65; lower bearing: L = B = 0.05 m, l = b = 1 cm, Ps = 5 kg/cm2, do = 0.5 mm, Cd = 0.65)

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Fig. 12

Pocket size influence of the lower bearing on the stiffness (c = 40 μm; upper bearing: L = B = 0.05 m, l = b = 3 cm, Ps = 5 kg/cm2, do = 0.1 mm, Cd = 0.65; lower bearing: L = B = 0.05 m, Ps = 5 kg/cm2, do = 0.5 mm, Cd = 0.65)

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