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Comparative Analysis of Surrogate Models for Organic Rankine Cycle Turbine Optimization.

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Title: Comparative Analysis of Surrogate Models for Organic Rankine Cycle Turbine Optimization.
Authors: Kim, Yeun-Seop1 (AUTHOR), Seo, Jong-Beom2 (AUTHOR), Lee, Ho-Saeng1,2 (AUTHOR), Han, Sang-Jo1,2 (AUTHOR) sjhan@seoultech.ac.kr
Source: Energies (19961073). Mar2026, Vol. 19 Issue 5, p1372. 20p.
Subject Terms: *Kriging, *Radial basis functions, *Rankine cycle, *Computational fluid dynamics, *Turbine aerodynamics, *Aerodynamics, *Statistical models
Abstract: To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192641097
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Comparative Analysis of Surrogate Models for Organic Rankine Cycle Turbine Optimization.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Kim%2C+Yeun-Seop%22">Kim, Yeun-Seop</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Seo%2C+Jong-Beom%22">Seo, Jong-Beom</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Ho-Saeng%22">Lee, Ho-Saeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Sang-Jo%22">Han, Sang-Jo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sjhan@seoultech.ac.kr</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 5, p1372. 20p.
– Name: Subject
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br />*<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Rankine+cycle%22">Rankine cycle</searchLink><br />*<searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Turbine+aerodynamics%22">Turbine aerodynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Aerodynamics%22">Aerodynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19051372
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 1372
    Subjects:
      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Rankine cycle
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
      – SubjectFull: Turbine aerodynamics
        Type: general
      – SubjectFull: Aerodynamics
        Type: general
      – SubjectFull: Statistical models
        Type: general
    Titles:
      – TitleFull: Comparative Analysis of Surrogate Models for Organic Rankine Cycle Turbine Optimization.
        Type: main
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            NameFull: Kim, Yeun-Seop
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            NameFull: Seo, Jong-Beom
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            NameFull: Lee, Ho-Saeng
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            NameFull: Han, Sang-Jo
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          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 19
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              Value: 5
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            – TitleFull: Energies (19961073)
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