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

Autor:	Banafsheh Nematollahi, Mohammad Bananmah, Mohammad Reza Nikoo, Mojtaba Sadegh
Rok vydání:	2020
Předmět:	Optimal design Computer science Control theory Cavitation 0103 physical sciences 0208 environmental biotechnology 02 engineering and technology Dissipation 01 natural sciences 010305 fluids & plasmas 020801 environmental engineering General Environmental Science Civil and Structural Engineering
Zdroj:	Canadian Journal of Civil Engineering. 47:584-595
ISSN:	1208-6029 0315-1468
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::188bcefc47b1020212e419f7e5d2c560 https://doi.org/10.1139/cjce-2018-0534 Zobrazit plný text záznamu Plný text ve formátu PDF