Using Metamodeling and Fluid-Structure Interaction Analysis in Multi-Objective Optimization of a Butterfly Valve

  • Laura Pałys Opole University of Technology
  • Mirosław W. Mrzygłód Opole University of Technology

Abstract

Along with the increase in computing power, new possibilities for the use of parametric coupled analysis of fluid flow machines and metamodeling for many branches of industry and medicine have appeared. In this paper, the use of a new methodology for multiobjective optimization of a butterfly valve with the application of the fluid-structure interaction metamodel is presented. The optimization objective functions were to increase the value of the KV valve’s flow coefficient while reducing the disk mass. Moreover, the equivalent von Mises stress was accepted as an additional constraint. The centred composite designs were used to plan the measuring point. Full second-order polynomials, non-parametric regression, Kriging metamodeling techniques were implemented. The optimization process was carried out using the multi-objectives genetic algorithm. For each metamodel, one of the optimization candidates was selected to verify its results. The best effect was obtained using the Kriging method. Optimization allowed to improve the KV value by 37.6%. The metamodeling process allows for the coupled analysis of the fluid flow machines in a shorter time, although its main application is geometry optimization.

Keywords

metamodeling, surrogate model, computational fluid dynamics, design of experiment, optimization, butterfly valve,

References

1. M.M. Said, H.S.S. Abdelmeguid, L.H. Rabie, The accuracy degree of CFD turbulence models for butterfly valve flow coefficient prediction, American Journal of Industrial Engineering, 4(1): 14–20, 2016, doi: 10.12691/ajie-4-1-3.

2. X.-M. Zhou, Z.-K. Wang, Y.-F. Zhang, A simple method for high-precision evaluation of valve flow coefficient by computational fluid dynamics simulation, Advances in Mechanical Engineering, 9, Article ID: 1687814017713702, 2017, doi: 10.1177/1687814017713702.

3. M.I. Al-Amayreh, M.I. Kilani, A.S. Al-Salaymeh, Numerical study of a butterfly valve for vibration analysis and reduction, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 8(12): 1970–1974, 2014.

4. M. Charlebois-Ménard, M. Sanjosé, A. Marsan, A. Chauvin, Y. Pasco, S. Moreau, M. Brouillette, Experimental and numerical study of the noise generation in an outflow butterfly valve, [in:] 21st AIAA/CEAS Aeroacoustics Conference, 22–26 June, 2015, Dallas, TX, doi: 10.2514/6.2015-3123.

5. X. Song, L. Wang, Y. Park, Fluid and structural analysis of a large diameter butterfly valve, Journal of Advanced Manufacturing Systems, 8(1): 81–88, 2009, doi: 10.1142/S0219686709001663.

6. S.Y. Jeon, J.Y. Yoon, M.S. Shin, Flow characteristics and performance evaluation of butterfly valves using numerical analysis, IOP Conference Series: Earth and Environmental Science, 12(1): 012099, 6 pp., 2010, doi: 10.1088/1755-1315/12/1/012099.

7. F. Vakili-Tahami, M. Zehsaz, M. Mohammadpour, A. Vakili-Tahami, Analysis of the hydrodynamic torque effects on large size butterfly valves and comparing results with AWWA C504 standard recommendations, Journal of Mechanical Science and Technology, 26(9): 2799–2806, 2012, doi: 10.1007/s12206-012-0733-8.

8. R. Kasukurthy, P.S. Challa, R.R. Palanikumar, B.R. Manimaran, D. Agonafer, Flow analysis and linearization of rectangular butterfly valve flow control device for liquid cooling, [in:] 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (I Therm), 29 May – 1 June, 2018, San Diego, CA, USA, pp. 683–687, doi: 10.1109/ITHERM.2018.8419503.

9. X.G. Song, L. Wang, Y.C. Park, Analysis and optimization of a butterfly valve disc, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 223(2): 81–89, 2009, doi: 10.1243/09544089JPME236.

10. X.G. Song, L. Wang, S.H. Baek, Y.C. Park, Multidisciplinary optimization of a butterfly valve, ISA Transactions, 48(3): 370–377, 2009, doi: 10.1016/j.isatra.2009.01.009.

11. S. Corbera, J. Luis, J. Antonio, Multi-objective global optimization of a butter fly valve using genetic algorithms, ISA Transactions, 63: 401–412, 2016, doi: 10.1016/j.isatra.2016.03.008.

12. B. Durakovic, Design of experiments application, concepts, examples: State of the art, Periodicals of Engineering and Natural Sciences, 5(3): 421–439, 2017, doi: 10.21533/pen.v5i3.145.

13. Y. Mack, T. Goel, W. Shyy, R. Haftka, Surrogate model-based optimization framework: A case study in aerospace design, [in:] Evolutionary Computation in Dynamic and Uncertain Environments, S. Yang, Y.-S. Ong, Y. Jin [Eds], Springer, Berlin Heidelberg, 2007, pp. 323–342, doi: 10.1007/978-3-540-49774-5_14.

14. A.-B. Ryberg, R. Bäckryd, L. Nilsson, Metamodel-Based Multidisciplinary Design Optimization for Automotive Applications, Technical Report LIU-IEI-R-12/003, Linköping University, 2012.

15. R. Jin, W. Chen, T.W. Simpson, Comparative studies of metamodeling techniques under multiple modeling criteria, Structural and Multidisciplinary Optimization, 23: 1–13, 2001, doi: 10.1007/s00158-001-0160-4.

16. P. Sofotasiou, B. Hughes, S.A. Ghani, CFD optimisation of a stadium roof geometry: a qualitative study to improve the wind microenvironment, Sustainable Buildings, 2, Article No. 8, 2017, doi: 10.1051/sbuild/2017006.

17. DesignXplorer 19.2/DesignXplorer User’s Guide/DesignXplorer Theory/Response Surface Theory/Kriging Algorithms, ANSYS, Inc. (n.d.).

18. S. Dasari, A. Cheddad, P. Andersson, Predictive modelling to support sensitivity analysis for robust design in aerospace engineering, Structural and Multidisciplinary Optimization, 61: 2177–2192, 2019, doi: 10.1007/s00158-019-02467-5.

19. A.I. Khuri, S. Mukhopadhyay, Response surface methodology, WIREs Computational Statistics, 2(2): 128–149, 2010, doi: 10.1002/wics.73.

20. J.P. Roselyn, D. Devaraj, S.S. Dash, Multi-Objective Genetic Algorithm for voltage stability enhancement using rescheduling and FACTS devices, Ain Shams Engineering Journal, 5(3): 789–801, 2014, doi: 10.1016/j.asej.2014.04.004.

21. M. Tabassum, K. Mathew, A genetic algorithm analysis towards optimization solutions, International Journal of Digital Information and Wireless Communications, 4(1): 124–142, 2014, doi: 10.17781/P001091.

22. PN-EN 60534-2-1:2011/AC1, Industrial-process control valves – Part 2-1: Flow capacity – Sizing equations for fluid flow under installed conditions, Polski Komitet Normalizacyjny, Warszawa, 2015.
Published
Mar 29, 2021
How to Cite
PAŁYS, Laura; MRZYGŁÓD, Mirosław W.. Using Metamodeling and Fluid-Structure Interaction Analysis in Multi-Objective Optimization of a Butterfly Valve. Computer Assisted Methods in Engineering and Science, [S.l.], v. 28, n. 1, p. 17–38, mar. 2021. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/311>. Date accessed: 17 oct. 2021. doi: http://dx.doi.org/10.24423/cames.311.
Section
Articles