chapter 8 summary and conclusion -...

237

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

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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

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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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Measurement and Instrumentation, Vol.16, pp. 353–364.

257

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)

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