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Research Papers: Hydrodynamic Lubrication

Discharge Coefficients in Aerostatic Bearings With Inherent Orifice-Type Restrictors

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

Department of Mechanical Engineering,
Advanced Institute of Manufacturing With
High-Tech Innovations,
National Chung Cheng University,
Ming-Hsiung, Chiayi County 621, Taiwan
e-mail: shchang314@gmail.com

C. W. Chan

Department of Mechanical Engineering,
Advanced Institute of Manufacturing With
High-Tech Innovations,
National Chung Cheng University,
Ming-Hsiung, Chiayi County 621, Taiwan
e-mail: addison0827@gmail.com

Y. R. Jeng

Department of Mechanical Engineering,
Advanced Institute of Manufacturing With
High-Tech Innovations,
National Chung Cheng University,
Ming-Hsiung, Chiayi County 621, 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 August 4, 2014; final manuscript received September 29, 2014; published online November 6, 2014. Assoc. Editor: George K. Nikas.

J. Tribol 137(1), 011705 (Nov 06, 2014) (7 pages) Paper No: TRIB-14-1194; doi: 10.1115/1.4028737 History: Received August 04, 2014; Revised September 29, 2014

In aerostatic bearing analysis, determining film pressure by solving the Reynolds equation in a numerical model is more effective than conducting bearing experiments or performing computational fluid dynamics (CFD) simulations. However, the discharge coefficient of an orifice-type restrictor is generally a given number that dominates model accuracy. This study investigated the influence of geometry and flow parameters on this discharge coefficient. The results indicate that this discharge coefficient is sensitive to the orifice diameter and film thickness and that the effects of the supply pressure, bearing radius, supply orifice length, supply passage diameter, conicity depth, and conicity angle can be disregarded. This study also built a surrogate model of this discharge coefficient based on the orifice diameter and film thickness by using artificial neural networks (ANNs).

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Figures

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Fig. 1

Aerostatic bearing with inherent orifice-type restrictor

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Fig. 2

Mesh for an aerostatic bearing (h = 9 μm, Rb = 20 mm, D = 3 mm, d = 0.2 mm, α = 118 deg, and l = 0.3 mm)

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Fig. 3

Convergence test of CFD simulation (h = 9 μm, Rb = 20 mm, D = 3 mm, d = 0.2 mm, α = 118 deg, l = 0.3 mm, and Ps = 0.5 MPa)

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Fig. 4

The comparison of CFD simulations and experimental results (Rb = 20 mm, D = 3 mm, d = 0.23 mm, α = 118 deg, l = 0.3 mm, and Ps = 0.5 MPa)

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Fig. 5

Comparison of FDM calculations and CFD simulations (Rb = 20 mm, D = 3 mm, d = 0.2 mm, α = 118 deg, l = 0.3 mm, and Ps = 0.5 MPa)

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Fig. 6

Schematic of a three-layer feed-forward neural network (2-6-4-1 network)

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Fig. 7

Influence of orifice diameter on the discharge coefficient (Rb = 20 mm, D = 3 mm, α = 118 deg, l = 0.3 mm, and Ps = 0.5 MPa)

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Fig. 8

Influence of supply pressure on the discharge coefficient (d = 0.1 mm, Rb = 20 mm, D = 3 mm, α = 118 deg, and l = 0.3 mm)

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Fig. 9

Influence of bearing radius on the discharge coefficient (d = 0.1 mm, D = 3 mm, α = 118 deg, Ps = 0.5 MPa, and l = 0.3 mm)

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Fig. 10

Influence of supply orifice length on the discharge coefficient (d = 0.1 mm, Rb = 20 mm, D = 3 mm, α = 118 deg, Ps = 0.5 MPa, and h = 10 μm)

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Fig. 11

Influence of supply passage diameter on the discharge coefficient (d = 0.1 mm, Rb = 20 mm, l = 0.3 mm, Ps = 0.5 MPa, and h = 10 μm)

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Fig. 12

Predictions of discharge coefficients from the 2-6-4-1 network

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Fig. 13

Comparison of FDM calculations and CFD simulation (Rb = 20 mm, d = 0.1 mm, D = 3 mm, α = 118 deg, Ps = 0.5 MPa, l = 0.3 mm, and h = 8 μm)

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