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Multi-Objective Optimisation of an Aerostatic Pad: Design of Position, Number and Diameter of the Supply Holes

Published online by Cambridge University Press:  13 January 2020

F. Colombo Open the ORCID record for F. Colombo [Opens in a new window] ,
F. Della Santa  and
S. Pieraccini
F. Colombo*
Affiliation:
Department of Mechanical and Aerospace Engineering, Politecnico di Torino Torino, Italy
F. Della Santa
Affiliation:
Department of Mathematical Sciences Politecnico di Torino Torino, Italy SmartData@PoliTO Politecnico di Torino Torino, Italy
S. Pieraccini
Affiliation:
Department of Mechanical and Aerospace Engineering Politecnico di Torino Torino, Italy Member of the INdAM research group GNCS
*
*Corresponding author ([email protected])
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Abstract

In this paper, a rectangular aerostatic bearing with multiple supply holes is optimised with a multiobjective optimisation approach. The design variables taken into account are the supply holes position, their number and diameter, the supply pressure, while the objective functions are the load capacity, the air consumption and the stiffness and damping coefficients. A genetic algorithm is applied in order to find the Pareto set of solutions. The novelty with respect to other optimisations which can be found in literature is that number and location of the supply holes is completely free and not associated to a pre-defined scheme. A vector x associated with the supply holes location is introduced in the design parameters and given in input to the optimizer.

Keywords

Four at mostseparated by Multi-objective optimisationgas lubricationgenetic algorithm
Type
Research Article
Information
Journal of Mechanics , Volume 36 , Issue 3 , June 2020 , pp. 347 - 360
Copyright
Copyright © 2020 The Society of Theoretical and Applied Mechanics

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References

REFERENCES

Huang, H. C., Al-Bender, F. and Van Brussel, H., “Optimum design of inherently compensated aerostatic thrust bearings”, in Kals, H. and Van Houten, F., “Integration of Process Knowledge into Design Support Systems”, Springer, Dordrecht (1999). https://doi.org/10.1007/978-94-017-1901-8 17.Google Scholar
Sescu, A., Sescu, C., Dimofte, F., Cioc, S., Afjeh, A. A. and Handschuh, R., “A study of the steady-state performance of a pressurized air wave bearing at concentric position”, Tribology Transactions, 52(4), pp. 544–552 (2011). https://doi.org/10.1080/10402000902774275.CrossRefGoogle Scholar
Lu, C. J., Chiou, S. S. and Wang, T. K., “Adaptive multilevel method for the air bearing problem in hard disk drives”, Tribology International, 37(6), pp. 473–480, (2004). ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2004.01.001.CrossRefGoogle Scholar
Wang, N., Tsai, C. M. and Cha, K. C., “Optimum design of externally pressurized air bearing using cluster openMP”, Tribology International, 42(8), pp. 1180–1186 (2009). ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2009.03.016.CrossRefGoogle Scholar
Wang, N. and Cha, K. C., “Multi-objective optimization of air bearings using hypercubedividing method”, Tribology International, 43(9), pp. 1631–1638 (2010). ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2010.03.009.CrossRefGoogle Scholar
Hashimoto, H. and Ochiai, M., “Optimization of groove geometry for thrust air bearing to maximize bearing stiffness”. ASME. J. Tribol., 130(3), pp.031101--031101-11 (2008). https://doi.org/10.1115/1.2913546.CrossRefGoogle Scholar
Schiffmann, J. J., “Integrated design and multiobjective optimization of a single stage heat-pump turbocompressor”, ASME. J. Turbomach., 137(7), pp.071002--071002-9 (2015). https://doi.org/10.1115/1.4029123.CrossRefGoogle Scholar
Schiffmann, J. J. and Favrat, D. D., “Integrated design and optimization of gas bearing supported rotors”, ASME. J. Mech. Des., 132(5), pp.051007--051007-11 (2010). https://doi.org/10.1115/1.4001381.CrossRefGoogle Scholar
Zhu, H. and Bogy, D. B., “Hard disc drive air bearing design: modified direct algorithm and its application to slider air bearing surface optimization”, Tribology International, 37(2), pp. 193–201 (2004). ISSN 0301-679X, https://doi.org/10.1016/S0301-679X(03)00036-7.CrossRefGoogle Scholar
Zhang, J. and Talke, F. E., “Optimization of slider air bearing contours using the combined genetic algorithm subregion approach”, Tribology International, 38(6-7), pp. 566–573 (2005). ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2005.01.013.CrossRefGoogle Scholar
Jayson, E. M. and Talke, F. E., “Optimization of air bearing contours for shock performance of a hard disk drive”, ASME. J. Tribol., 127(4), pp. 878–883 (2005). https://doi.org/10.1115/1.2000979.CrossRefGoogle Scholar
Kotera, H. and Shima, S., “Shape optimization to perform prescribed air lubrication using genetic algorithm”, Tribology Transactions, 43(4), pp. 837–841 (2008). https://doi.org/10.1080/10402000008982416.CrossRefGoogle Scholar
Holland, J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor (1975).Google Scholar
Schaffer, J. D. “Multiple Objective optimization with vector evaluated genetic algorithms”, International Conference on Genetic Algorithm and their applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93-100, 1985.Google Scholar
Fonseca, C. M. and Fleming, P. J. “Multiobjective genetic algorithms”, Proceedings of IEE Colloquium on ‘Genetic Algorithms for Control Systems Engineering (Digest No. 1993/130), 28 May 1993. 1993. London, UK: IEE.Google Scholar
Fonseca, C. M. and Fleming, P. J., “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization”, Proceedings of ICGA-93: Fifth International Conference on Genetic Algorithms, 17-22 July 1993. 1993. Urbana Champaign, IL, USA: Morgan Kaufmann.Google Scholar
Coello, C. A. C., “A comprehensive survey of evolutionary-based multiobjective optimization techniques”, Knowledge and Information Systems, 1(3), pp. 269–308 (1999).CrossRefGoogle Scholar
Zitzler, E. and Thiele, L., “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach”, IEEE Transactions on Evolutionary Computation, 3(4), pp. 257–271 (1999).CrossRefGoogle Scholar
Zitzler, E., Deb, K., and Thiele, L., “Comparison of multiobjective evolutionary algorithms: empirical results”, Evolutionary Computation, 8(2), pp. 173–195 (2000).CrossRefGoogle ScholarPubMed
Sarker, R., Liang, K.-H., and Newton, C., “A new multiobjective evolutionary algorithm”, European Journal of Operational Research, 140(1), pp. 12–23 (2002).CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, 6(2), pp. 182–197 (2002).CrossRefGoogle Scholar
Lu, H. and Yen, G.G., “Rank-density-based multiobjective genetic algorithm and benchmark test function study”, IEEE Transactions on Evolutionary Computation, 7(4), pp. 325–343 (2003).Google Scholar
Wang, N., “A parallel computing application of the genetic algorithm for lubrication optimization”, Tribology Letters, 18 (2005). https://doi.org/10.1007/s11249-004-1763-x.CrossRefGoogle Scholar
Schiffmann, J. J. and Favrat, D. D., “Multi-objective optimisation of herringbone grooved gas bearings supporting a high speed rotor, taking into account rarefied gas and real gas effects”, ASME Engineering Systems Design and Analysis, Volume3: Dynamic Systems and Controls, Symposium on Design and Analysis of Advanced Structures, and Tribology, pp.857-865. https://doi.org/10.1115/ESDA2006-95085.CrossRefGoogle Scholar
Lu, C. and Wang, T., “New designs of HDD airlubricated sliders via topology optimization”, ASME. J. Tribol., 126(1), pp.171–176 (2004). https://doi.org/10.1115/1.1631016.CrossRefGoogle Scholar
Wang, N. and Chang, Y. Z., “A hybrid search algorithm for porous air bearings optimization”, Tribology Transactions, 45(4), pp. 471–477 (2008). https://doi.org/10.1080/10402000208982576.CrossRefGoogle Scholar
Zhong, W., Li, X., Tao, G. and al., “Theoretical and experimental investigation on optimization of a noncontact air conveyor”, J. Cent. South Univ. (2016). https://doi.org/10.1007/s11771-016-3080-6.CrossRefGoogle Scholar
Lu, S., Hu, Y., O’Hara, M., Bogy, D. B., Singh Bhatia, C. and Hsia, Yiao-Tee, “Air bearing design, optimization, stability analysis and verification for sub-25 nm flying”, IEEE Transactions on Magnetics, 32(1), pp. 103–109 (1996). https://doi.org/10.1109/20.477558.Google Scholar
Yoon, S. and Choi, D., “An optimum design of the transverse pressure contour slider for enhanced flying characteristics”, ASME. J. Tribol., 119(3), pp. 520–524 (1997). https://doi.org/10.1115/1.2833531.CrossRefGoogle Scholar
Zhu, Hong and Bogy, D. B., “Direct algorithm and its application to slider air-bearing surface optimization”, IEEE Transactions on Magnetics, 38(5), pp. 2168–2170 (2002). https://doi.org/10.1109/TMAG.2002.802794.CrossRefGoogle Scholar
Bhat, N. and Barrans, S. M., “Design and test of a Pareto optimal flat pad aerostatic bearing”, Tribology International, 41(3), pp. 181–188 (2008). https://doi.org/10.1016/j.triboint.2007.07.011.CrossRefGoogle Scholar
Bhat, N., Barrans, S. M. and Kumar, A. S., “Performance analysis of Pareto optimal bearings subject to surface error variations”, Tribology Interna tional, 43(11), pp. 2240–2249 (2010). ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2010.07.012.CrossRefGoogle Scholar
Colombo, F. and Conte, M., “Multi-objective optimization of a rectangular air bearing by means of genetic algorithms”, Journal of Mechanics Engineering and Automation, 2, pp. 355–364 (2012).Google Scholar
Colombo, F., Lentini, L., Raparelli, T., Trivella, A. and Viktorov, V., “Dynamic characterisation of rectangular aerostatic pads with multiple inherent orifices”, Tribology Letters, pp. 66–133 (2018). https://doi.org/10.1007/s11249-018-1087-x.Google Scholar
Belforte, G., Colombo, F., Raparelli, T., Trivella, A. and Viktorov, V., “Experimental analysis of air pads with micro holes”, Tribology Transactions, 56(2), pp. 169–177 (2013).CrossRefGoogle Scholar
“Global Optimization Toolbox User’s Guide”, The MathWorks, Inc., chapter 9.Google Scholar
“Global Optimization Toolbox User’s Guide”, The MathWorks, Inc., chapter 11.Google Scholar