Optimizing chute-flip bucket system based on meta-modelling approach

Mohammad Bananmah, Mohammad Reza Nikoo*, Banafsheh Nematollahi, Mojtaba Sadegh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Optimal design of chute-flip bucket (CFB) system depends on various parameters, among which energy dissipation and cavitation prevention are the most important. This study develops a simulation-optimization model based on a calibrated Flow-3D numerical model, multi-layer perceptron artificial neural network (MLP-ANN), and genetic algorithm (GA) optimization approach for determining the optimal geometry of the CFB system. To alleviate the computational time burden of the Flow-3D numerical model, a MLP-ANN meta-model is developed based on some limited simulations of Flow-3D. The meta-model framework is then coupled with GA to provide an efficient design framework for the CFB system. The proposed framework is employed to design optimal geometry of the CFB system of the Jareh dam in Ahvaz, Iran. The results show that the obtained optimal design increases the cavitation index up to 30% and energy dissipation up to 32% compared to the old engineering design already in place.

Original languageEnglish
Pages (from-to)584-595
Number of pages12
JournalCanadian Journal of Civil Engineering
Volume47
Issue number5
DOIs
Publication statusPublished - 2020

Keywords

  • Artificial neural network
  • Chute-flip bucket system
  • Flow-3D numerical model
  • Genetic algorithm

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Environmental Science(all)

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