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Research Papers: Applications

Modified Particle Swarm Optimization Algorithm for Multi-Objective Optimization Design of Hybrid Journal Bearings

[+] Author and Article Information
Chia-Wen Chan

Advanced Institute of Manufacturing With
High-Tech Innovations,
Department of Mechanical Engineering,
National Chung Cheng University,
168, Sec. 1, University Road,
Ming-Hsiung, Chia-Yi 621, Taiwan
e-mail: addison0827@gmail.com

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received June 3, 2014; final manuscript received August 12, 2014; published online October 7, 2014. Assoc. Editor: George K. Nikas.

J. Tribol 137(2), 021101 (Apr 01, 2015) (7 pages) Paper No: TRIB-14-1125; doi: 10.1115/1.4028606 History: Received June 03, 2014; Revised August 12, 2014; Online November 24, 2014

The objective of design optimization is to determine the design that minimizes the objective function by changing design variables and satisfying design constraints. During multi-objective optimization, which has been widely applied to improve bearing designs, designers must consider several design criteria or objective functions simultaneously. The particle swarm optimization (PSO) method is known for its simple implementation and high efficiency in solving multifactor but single-objective optimization problems. This paper introduces a new multi-objective algorithm (MOA) based on the PSO and Pareto methods that can greatly reduce the number of objective function calls when a suitable swarm size is set.

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References

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Figures

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Fig. 1

Geometry of the simulated bearing and journal system, where e is the shaft eccentricity, α is the orientation angle, θ is the loading angle, and ω is the rotation speed

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Fig. 2

A self-compensating modular of hydrostatic bearing [21]

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Fig. 3

The flowchart of the proposed MOA: modified PSO method

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Fig. 4

The Pareto front approaching in criterion space for the convex MOOP (the size of swarm is 20 and the maximum search step is 5)

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Fig. 5

The Pareto front approaching in criterion space for the nonconvex MOOP (the size of swarm is 20 and the maximum search step is 15)

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Fig. 6

The Pareto front of the convex MOOP applying the new MOA and HDM in criterion space (with 900 randomly generated gray circles)

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Fig. 7

The Pareto front of the nonconvex MOOP using the new MOA and HDM in criterion space (with 900 randomly generated gray circles)

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Fig. 8

The Pareto fronts of the two-factor MOOP: self-compensation journal bearing optimization with different swarm size in criterion space

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Fig. 9

The distribution of the Pareto front in design space for the two-factor two-objective journal bearing optimization using 20 particles

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Fig. 10

The ideal Pareto point of the two-factor multi-objective optimization for the journal bearing in criterion space

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Fig. 11

The Pareto fronts of the four-factor two-objective journal bearing optimization with different swarm size in criterion space

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Fig. 12

The ideal Pareto point of the four-factor two-objective journal bearing optimization in criterion space

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Fig. 13

The results of the four-factor two-objective journal bearing optimization in design space

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Fig. 14

The pressure distribution of the optimal design: the recess length and width are both 20 mm, the radial clearance is 12.2 μm, and the orientation angle is 43.48 deg

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