Multi-objective global optimization of a butterfly valve using genetic algorithms.


A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.

DOI: 10.1016/j.isatra.2016.03.008

Cite this paper

@article{Corbera2016MultiobjectiveGO, title={Multi-objective global optimization of a butterfly valve using genetic algorithms.}, author={Sergio Corbera and Jos{\'e} Luis Olazagoitia and Jos{\'e} Lozano}, journal={ISA transactions}, year={2016}, volume={63}, pages={401-412} }