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).

Issue Section:
Hydrodynamic Lubrication
Keywords:
Bearings, Gas (air) bearings, Hydrostatic lubrication, Thrust bearings
Topics:
Bearings, Computational fluid dynamics, Discharge coefficient, Pressure, Simulation, Artificial neural networks, Flow (Dynamics)

References

1.
Chen
,
X. D.
, and
He
,
X. M.
,
2006
, “
The Effect of the Recess Shape on Performance Analysis of the Gas-Lubricated Bearing in Optical Lithography
,”
Tribol. Int.
,
39
(
11
), pp.
1336
–
1341
.10.1016/j.triboint.2005.10.005
Google Scholar
Crossref
Search ADS
 
2.
Li
,
Y.
, and
Ding
,
H.
,
2007
, “
Influences of the Geometrical Parameters of Aerostatic Thrust Bearing With Pocketed Orifice-Type Restrictor on Its Performance
,”
Tribol. Int.
,
40
(
7
), pp.
1120
–
1126
.10.1016/j.triboint.2006.11.001
Google Scholar
Crossref
Search ADS
 
3.
Jeng
,
Y. R.
, and
Chang
,
S. H.
,
2013
, “
Comparison Between the Effects of Single-Pad and Double-Pad Aerostatic Bearings With Pocketed Orifices on Bearing Stiffness
,”
Tribol. Int.
,
66
, pp.
12
–
18
.10.1016/j.triboint.2013.04.003
Google Scholar
Crossref
Search ADS
 
4.
Wang
,
N.
, and
Cha
,
K. C.
,
2010
, “
Multi-Objective Optimization of Air Bearings Using Hypercube-Dividing Method
,”
Tribol. Int.
,
43
(
9
), pp.
1631
–
1638
.10.1016/j.triboint.2010.03.009
Google Scholar
Crossref
Search ADS
 
5.
Chang
,
S. H.
, and
Jeng
,
Y. R.
,
2014
, “
A Modified Particle Swarm Optimization Algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor
,”
ASME J. Tribol.
,
136
(
2
), p.
021701
.10.1115/1.4026061
Google Scholar
Crossref
Search ADS
 
6.
Belforte
,
G.
,
Raparelli
,
T.
,
Trivella
,
A.
,
Viktorov
,
V.
, and
Visconte
,
C.
,
2006
, “
Numerical Analysis on the Supply Hole Discharge Coefficient in Aerostatic Bearings
,”
International Conference on Tribology
,
Parma, Italy
, Sept. 20–22, Paper No. AITC-AIT 2006.
7.
Eleshaky
,
M. E.
,
2009
, “
CFD Investigation of Pressure Depressions in Aerostatic Circular Thrust Bearings
,”
Tribol. Int.
,
42
(
7
), pp.
1108
–
1117
.10.1016/j.triboint.2009.03.011
Google Scholar
Crossref
Search ADS
 
8.
Nishio
,
U.
,
Somaya
,
K.
, and
Yoshimoto
,
S.
,
2011
, “
Numerical Calculation and Experimental Verification of Static and Dynamic Characteristics of Aerostatic Thrust Bearings With Small Feedholes
,”
Tribol. Int.
,
44
(
12
), pp.
1790
–
1795
.10.1016/j.triboint.2011.07.004
Google Scholar
Crossref
Search ADS
 
9.
Belforte
,
G.
,
Raparelli
,
T.
,
Viktorov
,
V.
, and
Trivella
,
A.
,
2007
, “
Discharge Coefficients of Orifice-Type Restrictor for Aerostatic Bearings
,”
Tribol. Int.
,
40
(
3
), pp.
512
–
521
.10.1016/j.triboint.2006.05.003
Google Scholar
Crossref
Search ADS
 
10.
Miyatake
,
M.
, and
Yoshimoto
,
S.
,
2010
, “
Numerical Investigation of Static and Dynamic Characteristics of Aerostatic Thrust Bearings With Small Feed Holes
,”
Tribol. Int.
,
43
(
8
), pp.
1353
–
1359
.10.1016/j.triboint.2010.01.002
Google Scholar
Crossref
Search ADS
 
11.
Neves
,
M. T.
,
Schwarz
,
V. A.
, and
Menon
,
G. J.
,
2010
, “
Discharge Coefficient Influence on the Performance of Aerostatic Journal Bearings
,”
Tribol. Int.
,
43
(
4
), pp.
746
–
751
.10.1016/j.triboint.2009.11.001
Google Scholar
Crossref
Search ADS
 
12.
Senatore
,
A.
,
D'Agostino
,
V.
,
Di Giuda
,
R.
, and
Petrone
,
V.
,
2011
, “
Experimental Investigation and Neural Network Prediction of Brakes and Clutch Material Frictional Behaviour Considering the Sliding Acceleration Influence
,”
Tribol. Int.
,
44
(
10
), pp.
1199
–
1207
.10.1016/j.triboint.2011.05.022
Google Scholar
Crossref
Search ADS
 
13.
Gyurova
,
L. A.
, and
Friedrich
,
K.
,
2011
, “
Artificial Neural Networks for Predicting Sliding Friction and Wear Properties of Polyphenylene Sulfide Composites
,”
Tribol. Int.
,
44
(
5
), pp.
603
–
609
.10.1016/j.triboint.2010.12.011
Google Scholar
Crossref
Search ADS
 
14.
Kurban
,
A. O.
, and
Yildirim
,
S.
,
2003
, “
Analysis of a Hydrodynamic Thrust Bearing With Elastic Deformation Using a Recurrent Neural Network
,”
Tribol. Int.
,
36
(
12
), pp.
943
–
948
.10.1016/S0301-679X(03)00090-2
Google Scholar
Crossref
Search ADS
 
15.
Chang
,
Y. Z.
, and
Wang
,
N.
,
2005
, “
Tribological Performance Prediction Using an Artificial Neural Network Optimized by the DIRECT Algorithm
,”
ASME
Paper No. WTC2005-63413. 10.1115/WTC2005-63413
16.
Tsai
,
C. M.
, and
Wang
,
N.
,
2007
, “
An Assessment of Neural Network Metamodels for Thermohydrodynamic Lubrication Analysis
,”
Society of Tribologists and Lubrication Engineers (STLE) 62nd Annual Meeting
,
Philadelphia, PA
, May 6–10.
Copyright © 2015 by ASME
You do not currently have access to this content.