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

Sergio Corbera1, José Luis Olazagoitia2, José Antonio Lozano1

  • 1Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.

ISA Transactions
|April 9, 2016
PubMed
Summary
This summary is machine-generated.

This study optimized butterfly valve design using genetic algorithms, achieving significant stress, mass, and flow improvements. The novel multi-objective approach enhances performance for this critical flow-control device.

Keywords:
Butterfly valveCFDDOEFEMGenetic algorithmsGlobal optimizationMultidisciplinary optimizationNSGA-IIPareto

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Area of Science:

  • Mechanical Engineering
  • Fluid Dynamics
  • Computational Mechanics

Background:

  • Butterfly valves are crucial for flow control, with ongoing research in structural (FEM) and computational fluid dynamics (CFD) analysis to enhance performance.
  • Previous studies focused on individual analyses, lacking a comprehensive optimization strategy for butterfly valve design.

Purpose of the Study:

  • To introduce a novel multi-objective optimization approach for butterfly valve design.
  • To leverage advanced genetic algorithms based on Pareto dominance for improved valve performance.

Main Methods:

  • Defined design variables and created a parameterization model for the butterfly valve.
  • Employed Design of Experiments (DOE) to generate initial candidates.
  • Utilized genetic algorithms for simultaneous structural (FEM) and CFD analysis, integrating various software with Python scripts.

Main Results:

  • Achieved a 65.4% reduction in stress.
  • Secured a 5% reduction in mass.
  • Increased flow rate by 11.3%.

Conclusions:

  • The proposed multi-objective optimization methodology effectively enhances butterfly valve design.
  • Validated results through experimental comparison, demonstrating the approach's capability and potential for practical application.