View in EDS

Surrogate modeling method for multi-objective optimization of the inlet channel and the basin of a gravitational water vortex hydraulic turbine.

Saved in:
Bibliographic Details
Title: Surrogate modeling method for multi-objective optimization of the inlet channel and the basin of a gravitational water vortex hydraulic turbine.
Authors: Velásquez, Laura1 (AUTHOR) lisabel.velasquez@udea.edu.co, Posada, Alejandro2 (AUTHOR), Chica, Edwin1 (AUTHOR)
Source: Applied Energy. Jan2023:Part B, Vol. 330, pN.PAG-N.PAG. 1p.
Subjects: Hydraulic turbines, Genetic algorithms, Interpolation algorithms, Response surfaces (Statistics), Inlets, Watersheds
Abstract: This work presents a high-fidelity surrogate model for generating a multi-objective genetic algorithm to allow the search for the optimal geometry of the inlet channel and the basin of a gravitational water vortex hydraulic turbine. Six parameters were considered for the optimization: the relations between the basin diameter (D) and the basin height (H), H / D ; the wrap-around angle (γ), the outlet diameter (d) and D , d / D ; the inlet channel width (w) and D , w / D ; the inlet channel height (h) and D , h / D ; and the inlet channel length (L) and D , L / D. Two conflicting objectives were studied: maximizing the vortex strength (Γ) and minimizing the volume flow rate (Q). The multi-objective optimization problem was resolved by applying the gamultiobj function in Matlab R2018b software. The optimization combines the genetic algorithm with Kriging interpolation to obtain the Pareto front. To train the gamultiobj function, an initial population of 40 individuals was used. As a stopping criterion, the maximum generation of 500 individuals was established. To improve the Pareto front, 20 optimization cycles (60 new samples) were required, reaching a final population of 100 individuals. It was found from the Pareto front that the values of the six variables providing the compromise solution, between Γ and Q , were H / D = 1. 572 , L / D = 1. 518 , h / D = 0. 565 , w / D = 0. 361 , d / D = 0. 108 , and γ =92.141°. This solution reaches a Q of 0.00305 m 3 /s and a Γ of 1.699 m 2 /s. The results of this study were compared with the results reported by other authors, who optimized this type of turbine by applying the response surface methodology. The difference between these results was less than 9.61%. [Display omitted] • The gravitational water vortex hydraulic turbine (GWVHT) takes advantage of the potential and kinetic energy of an artificial vortex in a circulation chamber. • GWVHT was optimized through a multi-objective genetic algorithm. • Transition-state, volume of fluid (VoF), and k − ϵ RNG turbulence model were chosen to perform the simulations in CFD. • The circulation and the volume flow rate for the compromise solution were 1.699 m 2 /s and 0.00305 m 3 /s, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 160584643
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Surrogate modeling method for multi-objective optimization of the inlet channel and the basin of a gravitational water vortex hydraulic turbine.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Velásquez%2C+Laura%22">Velásquez, Laura</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lisabel.velasquez@udea.edu.co</i><br /><searchLink fieldCode="AR" term="%22Posada%2C+Alejandro%22">Posada, Alejandro</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chica%2C+Edwin%22">Chica, Edwin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Jan2023:Part B, Vol. 330, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Hydraulic+turbines%22">Hydraulic turbines</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Interpolation+algorithms%22">Interpolation algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Response+surfaces+%28Statistics%29%22">Response surfaces (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Inlets%22">Inlets</searchLink><br /><searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This work presents a high-fidelity surrogate model for generating a multi-objective genetic algorithm to allow the search for the optimal geometry of the inlet channel and the basin of a gravitational water vortex hydraulic turbine. Six parameters were considered for the optimization: the relations between the basin diameter (D) and the basin height (H), H / D ; the wrap-around angle (γ), the outlet diameter (d) and D , d / D ; the inlet channel width (w) and D , w / D ; the inlet channel height (h) and D , h / D ; and the inlet channel length (L) and D , L / D. Two conflicting objectives were studied: maximizing the vortex strength (Γ) and minimizing the volume flow rate (Q). The multi-objective optimization problem was resolved by applying the gamultiobj function in Matlab R2018b software. The optimization combines the genetic algorithm with Kriging interpolation to obtain the Pareto front. To train the gamultiobj function, an initial population of 40 individuals was used. As a stopping criterion, the maximum generation of 500 individuals was established. To improve the Pareto front, 20 optimization cycles (60 new samples) were required, reaching a final population of 100 individuals. It was found from the Pareto front that the values of the six variables providing the compromise solution, between Γ and Q , were H / D = 1. 572 , L / D = 1. 518 , h / D = 0. 565 , w / D = 0. 361 , d / D = 0. 108 , and γ =92.141°. This solution reaches a Q of 0.00305 m 3 /s and a Γ of 1.699 m 2 /s. The results of this study were compared with the results reported by other authors, who optimized this type of turbine by applying the response surface methodology. The difference between these results was less than 9.61%. [Display omitted] • The gravitational water vortex hydraulic turbine (GWVHT) takes advantage of the potential and kinetic energy of an artificial vortex in a circulation chamber. • GWVHT was optimized through a multi-objective genetic algorithm. • Transition-state, volume of fluid (VoF), and k − ϵ RNG turbulence model were chosen to perform the simulations in CFD. • The circulation and the volume flow rate for the compromise solution were 1.699 m 2 /s and 0.00305 m 3 /s, respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=160584643
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.apenergy.2022.120357
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Hydraulic turbines
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Interpolation algorithms
        Type: general
      – SubjectFull: Response surfaces (Statistics)
        Type: general
      – SubjectFull: Inlets
        Type: general
      – SubjectFull: Watersheds
        Type: general
    Titles:
      – TitleFull: Surrogate modeling method for multi-objective optimization of the inlet channel and the basin of a gravitational water vortex hydraulic turbine.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Velásquez, Laura
      – PersonEntity:
          Name:
            NameFull: Posada, Alejandro
      – PersonEntity:
          Name:
            NameFull: Chica, Edwin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 01
              Text: Jan2023:Part B
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 03062619
          Numbering:
            – Type: volume
              Value: 330
          Titles:
            – TitleFull: Applied Energy
              Type: main
ResultId 1