Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis

The draft tube is one of the main components that integrate a turbine, since it has the function of recovering the residual kinetic energy after the runner by the pressure energy. The search for a draft tube design that increases the efficiency of the turbine is always an engineering challenge. The...

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Autores principales: A. C. Barkett Botan, R. G. Ramirez Gustavo, G. L. Tiago Filho, E. R. da Silva
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Lenguaje:EN
Publicado: Isfahan University of Technology 2021
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spelling oai:doaj.org-article:d9cce8851561419a99ce90875d684d132021-11-13T07:03:04ZOptimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis1735-3572https://doaj.org/article/d9cce8851561419a99ce90875d684d132021-01-01T00:00:00Zhttp://jafmonline.net/JournalArchive/download?file_ID=56645&issue_ID=1015https://doaj.org/toc/1735-3572The draft tube is one of the main components that integrate a turbine, since it has the function of recovering the residual kinetic energy after the runner by the pressure energy. The search for a draft tube design that increases the efficiency of the turbine is always an engineering challenge. The hydromechanics components geometry optimization can be accomplished through the integration of optimization methods and CFD tools. In this work, the geometric optimization of a double diffuser draft tube of a Bulb turbine applied to ultra-low heads is presented, with the objectives of maximizing the pressure recovery coefficient, Cp, and increasing the hydraulic efficiency of the turbine, ηh. These improvements would make it possible to reduce the longitudinal length of the draft tube, thereby, making an easier insertion of this kind of turbines in water transport systems, with pressures around 3 [mH2O]. The optimization methodology was performed in the meridional plane, using twelve geometric variables in the draft tube through the integration of optimization methods and computational fluid dynamics. The optimized geometry obtained showed an increase in the Cp value of 0.71516, from the original geometry, to 0.83080. The results were extended to the 3D flow analysis, where the optimized turbine showed efficiency gains of 82% to 84%, when compared to the original turbine considering that its total length was reduced and its geometry simplified, resulting in a more compact and versatile equipment. The study also concluded that the applied methodology can be extended to other similar optimization problems in the design of hydraulic machines‎.A. C. Barkett BotanR. G. Ramirez GustavoG. L. Tiago FilhoE. R. da SilvaIsfahan University of Technology articlecfd analysis; design of experiments; draft tube; bulb turbine; mosa; optimization‎.Mechanical engineering and machineryTJ1-1570ENJournal of Applied Fluid Mechanics, Vol 14, Iss 6, Pp 1617-1633 (2021)
institution DOAJ
collection DOAJ
language EN
topic cfd analysis; design of experiments; draft tube; bulb turbine; mosa; optimization‎.
Mechanical engineering and machinery
TJ1-1570
spellingShingle cfd analysis; design of experiments; draft tube; bulb turbine; mosa; optimization‎.
Mechanical engineering and machinery
TJ1-1570
A. C. Barkett Botan
R. G. Ramirez Gustavo
G. L. Tiago Filho
E. R. da Silva
Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
description The draft tube is one of the main components that integrate a turbine, since it has the function of recovering the residual kinetic energy after the runner by the pressure energy. The search for a draft tube design that increases the efficiency of the turbine is always an engineering challenge. The hydromechanics components geometry optimization can be accomplished through the integration of optimization methods and CFD tools. In this work, the geometric optimization of a double diffuser draft tube of a Bulb turbine applied to ultra-low heads is presented, with the objectives of maximizing the pressure recovery coefficient, Cp, and increasing the hydraulic efficiency of the turbine, ηh. These improvements would make it possible to reduce the longitudinal length of the draft tube, thereby, making an easier insertion of this kind of turbines in water transport systems, with pressures around 3 [mH2O]. The optimization methodology was performed in the meridional plane, using twelve geometric variables in the draft tube through the integration of optimization methods and computational fluid dynamics. The optimized geometry obtained showed an increase in the Cp value of 0.71516, from the original geometry, to 0.83080. The results were extended to the 3D flow analysis, where the optimized turbine showed efficiency gains of 82% to 84%, when compared to the original turbine considering that its total length was reduced and its geometry simplified, resulting in a more compact and versatile equipment. The study also concluded that the applied methodology can be extended to other similar optimization problems in the design of hydraulic machines‎.
format article
author A. C. Barkett Botan
R. G. Ramirez Gustavo
G. L. Tiago Filho
E. R. da Silva
author_facet A. C. Barkett Botan
R. G. Ramirez Gustavo
G. L. Tiago Filho
E. R. da Silva
author_sort A. C. Barkett Botan
title Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
title_short Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
title_full Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
title_fullStr Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
title_full_unstemmed Optimization of a Draft Tube using Statistical Techniques- DOE and 2D Computational Fluid Dynamic Analysis
title_sort optimization of a draft tube using statistical techniques- doe and 2d computational fluid dynamic analysis
publisher Isfahan University of Technology
publishDate 2021
url https://doaj.org/article/d9cce8851561419a99ce90875d684d13
work_keys_str_mv AT acbarkettbotan optimizationofadrafttubeusingstatisticaltechniquesdoeand2dcomputationalfluiddynamicanalysis
AT rgramirezgustavo optimizationofadrafttubeusingstatisticaltechniquesdoeand2dcomputationalfluiddynamicanalysis
AT gltiagofilho optimizationofadrafttubeusingstatisticaltechniquesdoeand2dcomputationalfluiddynamicanalysis
AT erdasilva optimizationofadrafttubeusingstatisticaltechniquesdoeand2dcomputationalfluiddynamicanalysis
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