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CHAPTER 8
SUMMARY AND CONCLUSION
Stability analysis of hydrodynamic journal bearing is carried out using stiffness
coefficients. Theoretical synchronous whirl occurs at 1666 cycles/min when the bearing
is operating at 800 rpm and 150 N load. i.e. the bearing is stable up to 1666 rpm
Fig 8.1. Bearing Performance factors at 800 rpm and 150 N load
Fig . 8.2 Pressure Distribution for 1666 rpm and 150 N load
238
From experimental pressure distribution on journal bearing test rig it is observed that
maximum pressure is 52 Psi at 82.5 degrees and are is in good agreement with the
theoretical results of maximum pressure which is 425 kPa (61.64 PSI) at 1200. Also the
pressure distribution is regular. i.e. the bearing is stable up to 1666 rpm. When Journal
bearing is operating at 900 rpm the stability speed is 3984 rpm and if journal operates at
1000 rpm the stability speed is 4326 rpm. At 3984 rpm and 4326 rpm speeds the
experimental analysis on set up is not possible as maximum operating speed of set up is
limited to 2000 rpm.
Minimum oil film thickness is limited from practical considerations like surface finish of
bearing, rigidity of journal, geometry of bearing surface, type of loading. Theoretically the
nature of oil film thickness is symmetrical about y axis.
Graph8.1 Theoretical Oil Film Thickness Vs Angle
239
Experimentally oil film thickness is determined by attachment of inductive transducer to
Journal bearing test rig at speed of 800 rpm, 900 rpm, 1000 rpm and 1100 rpm and load
of 150 N and 300 N.
Graph 8.2 Comparison of theoretical and experimental oil film thickness at 300N Load
Theoretical oil film thickness is symmetric about y-axis and shows minimum oil film
thickness at 90 degrees. The experimental reading obtained shows that minimum oil film
thickness is shifted away from 90 degrees and minimum value is observed between 120
to 140 degrees. Results obtained are in good agreement within the range of 70 degree to
140 degree of the bearing rotation. Experimental results show changes which are due to
surface roughness, vibration of the set up and errors in the inductive transducer due to the
stray magnetic field. Further we can conclude that oil film thickness obtained
experimentally is in good agreement with the theoretical values for a speed of 800 and
900 rpm.
In artificial neural network analysis of hydrodynamic journal bearings using oil film
pressure measurement it is concluded that predicted pressure distribution by artificial
neural network is in good agreement with experimental values.
240
Fig. 8.3 Output for SAE20w40
Fig. 8.4 Output for SAE 90
In every case, it is not possible to determine exact pressure distribution of hydrodynamic
journal bearing due to sevier operating conditions such as high temperatures, rigidity of
structure etc. thus it becomes difficult to further analyze stability of the hydrodynamic
241
journal bearing. In such cases artificial neural network can be effectively used which
predicts pressure distribution with good accuracy.
In artificial neural network analysis of hydrodynamic journal bearings using oil film
thickness measurement
Fig.8.5 Output at 300N and 1000rpm
Fig. 8.6 Output at 300N and 1100rpm
242
It can be concluded that, predicted oil film thickness by artificial neural network is in
good agreement with experimental values.
In system identification of hydrodynamic journal bearings using oil film pressure
measurement step response and transfer function of hydrodynamic journal bearing system
are obtained from experimental data of journal speed, load on bearing and fluid film
pressure recorded during determination of pressure distribution.
Fig. 8.7 Transient response of speed in term of step
Fig.8.8Measured and simulated model output
243
Fig. 8.9 Frequency Response for Speed
The identified system is MISO system with two transfer functions for two inputs namely
speed of journal and load on bearing. Final transfer function of journal bearing system is
obtained by adding these two transfer functions. Also it is concluded that outputs for the
simulated and experimental model are in good agreement. Transfer function model of a
journal bearing based on oil film pressure is used for determining the step response and
Fig. 8.10 Transient response of speed in term of step
244
Fig. 8.11 Measured and simulated model output
Fig. 8.12 Frequency Response for Speed
frequency response of journal bearing system. Transfer function created by system
identification is further used to develop a feedback control system for stability of oil film
thickness. Transfer function model of hydrodynamic journal bearing using system
identification is obtained from experimental data recorded during determination of oil
film thickness. The system is MISO system where two transfer functions are obtained for
two inputs namely speed of journal and load on bearing. The outputs for the simulated
and experimental modal are in good agreement. This model is used for determining the
step response and frequency response of journal bearing system. Experiments conducted
on journal bearing test rig for stability analysis of oil film thickness by feedback control
system shows satisfactory results at different operating conditions.
245
Future scope
In present work stability analysis of hydrodynamic journal bearing is done using stiffness
coefficients. Feedback control system is developed for stability of oil film thickness in
hydrodynamic journal bearing in a test rig. Further same system can be implemented for
actual hydrodynamic journal bearings installed in practical applications like power plants,
rolling mills etc. In practical systems where speed variation is not permitted, stability of
oil film thickness can be achieved by developing feedback control system in which
hybrid lubrication (combination of hydrodynamic and hydrostatic lubrication) can be
used as controlling parameter and oil film thickness as controlled parameter.
ANN technique can be applied for analysis and prediction of wear in hydrodynamic
journal bearings. System identification of hydrodynamic journal bearings is possible by
experimental values of wear.
246
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LIST OF PUBLICATIONS BASED ON RESEARCH WORK.
Publications in Refereed Journals
[1] RavindraR.Navthar, Dr. N.V.Halegowda: “Stability analysis of Hydrodynamic
journal bearings using stiffness Coefficients”, International Journal of Engineering
Science and Technology (IJEST),vol.2, Feb 2010, pp. 87-93, ISSN 0975-5462
(www.ijest.info/docs/IJEST10-02-02-05.pdf)
[2] RavindraR.Navthar, Dr. N.V.Halegowda: “ Experimental investigation of oil film
thickness for hydrodynamic Journal bearing”, International Journal of Applied Mechanics
and Material (AMM), vol. 110-116(2012), pp 2377-2382 ,Trans Tech publications,
Switzerland. ISBN 978-3-03785-262-0
(www.scientific.net/AMM.110-116.2377.pdf)
[3] RavindraR.Navthar,Dr.N.V.Halegowda : “Analysis of Oil Film Thickness in
Hydrodynamic Journal Bearing Using Artificial Neural Networks”,CiiT International
Journal of Artificial intelligent system and Machine learning, Vol.3, No.12, Nov 2011,
pp. 722-766, Print: ISSN 0974 – 9667 & Online: ISSN 0974 – 9543
(www.ciitresearch.org/aimlnov2011.html)
[4] RavindraR.Navthar, Dr. N.V.Halegowda, ShantanuDeshpande: “ Application of
artificial neural network in pressure distribution analysis of hydrodynamic journal
bearing’, International conference on power science and Engineering (ICPSE) 2011,
Chengdu, China. Published in ASME Digital Library, ISBN: 9780-7918-59919.
(asmedl.org/ebooks/asme/asme_press/859919/859919_paper383)
[5] RavindraR.Navthar, Dr.N.V.Halegowda, ShantanuDeshpande: “Pressure
Distribution analysis of hydrodynamic journal bearings using Artificial neural networks”,
4th
International conference on Computer and Automation Engineering (ICCAE 2012)
Mumbai, India, 14-15 Jan 2012.
Published in ASME Digital Library, ISBN: 978-0-7918-5994.
(asmedl.org/ebooks/asme/asme_press/859940/859940_paper24)
[6] RavindraR.Navthar, Dr. N.V.Halegowda, Dr. V.S.Patil: “System identification of
hydrodynamic journal bearings using oil film Thickness measurement”,CiiT International
Journal of Artificial intelligent system and Machine learning, Vol. 4 No.5 May 2012, pp
263-266, Print: ISSN 0974 – 9667 &Online :ISSN 0974 – 9543
258
(www.ciitresearch.org/aimlmay2012.html)
[7] RavindraR.Navthar, Dr. N.V.Halegowda, Dr. V.S.Patil: “System identification of
hydrodynamic journal bearings using oil film Pressure Measurement”, CiiT International
Journal of Artificial intelligent System and Machine learning, Vol. 4 No.8 Aug .2012,
Print ISSN 0974 – 9667 & Online: ISSN 0974 – 9543
(www.ciitresearch.org/aimlaugust2012.html)