Physics‐Informed Bayesian Optimisation of Utility‐Scale Wind Farms in Synthetically Generated Complex Terrain Using LES.
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| Title: | Physics‐Informed Bayesian Optimisation of Utility‐Scale Wind Farms in Synthetically Generated Complex Terrain Using LES. |
|---|---|
| Authors: | Jané‐Ippel, Christian1 (AUTHOR) christian.jane-ippel19@imperial.ac.uk, Palacios, Rafael1 (AUTHOR), Laizet, Sylvain1 (AUTHOR) |
| Source: | Wind Energy. Jun2026, Vol. 29 Issue 6, p1-19. 19p. |
| Subjects: | Large eddy simulation models, Wind power plants, Experimental design, Surrogate-based optimization, Wind turbine aerodynamics, Atmospheric boundary layer |
| Abstract: | Optimising turbine layouts to maximise power output is crucial for wind farm development, particularly in complex terrain where analytical wake models fail to capture key flow physics. We present the first application of Bayesian optimisation (BO) combined with large eddy simulations (LES) for utility‐scale wind farm layout optimisation in synthetically generated, realistic and reproducible terrain. LES were performed using WInc3D, a fast, high‐fidelity simulator that resolves atmospheric boundary layers, terrain effects and turbine wake interactions. Initial tests show that standard black‐box BO achieves only marginal improvements in complex terrain, with limited capacity to identify high‐performing layouts. To overcome this, we developed physics‐informed sampling strategies that use terrain‐induced flow patterns from base‐flow LES that result in high‐performing initial layouts. We tested four 16‐turbine optimisation cases with different spacing constraints and wind directionality (single and multiple directions), evaluating 512 layouts with LES for each optimisation. Results show that optimal layouts exploit terrain features through clustering on elevated ridges for single‐direction cases, whereas multi‐directional optimisation requires balanced distributions to maintain performance. LES reveal complex flow phenomena, including terrain‐dependent wake behaviour, bidirectional turbine‐terrain interactions and non‐linear wake effects that necessitate high‐fidelity resolution. Analysis of the optimisation evolution reveals that BO serves primarily as a refinement tool, effectively exploiting physics‐informed initial designs but struggling to discover new optimal regions independently. These findings demonstrate that incorporating physical insight is essential for WFLO in complex terrain and that high‐fidelity simulations are necessary for reliable wind farm layout optimisation under such conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of Wind Energy is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194014852 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Physics‐Informed Bayesian Optimisation of Utility‐Scale Wind Farms in Synthetically Generated Complex Terrain Using LES. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jané‐Ippel%2C+Christian%22">Jané‐Ippel, Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.jane-ippel19@imperial.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Palacios%2C+Rafael%22">Palacios, Rafael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Laizet%2C+Sylvain%22">Laizet, Sylvain</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wind+Energy%22">Wind Energy</searchLink>. Jun2026, Vol. 29 Issue 6, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Large+eddy+simulation+models%22">Large eddy simulation models</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+turbine+aerodynamics%22">Wind turbine aerodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Optimising turbine layouts to maximise power output is crucial for wind farm development, particularly in complex terrain where analytical wake models fail to capture key flow physics. We present the first application of Bayesian optimisation (BO) combined with large eddy simulations (LES) for utility‐scale wind farm layout optimisation in synthetically generated, realistic and reproducible terrain. LES were performed using WInc3D, a fast, high‐fidelity simulator that resolves atmospheric boundary layers, terrain effects and turbine wake interactions. Initial tests show that standard black‐box BO achieves only marginal improvements in complex terrain, with limited capacity to identify high‐performing layouts. To overcome this, we developed physics‐informed sampling strategies that use terrain‐induced flow patterns from base‐flow LES that result in high‐performing initial layouts. We tested four 16‐turbine optimisation cases with different spacing constraints and wind directionality (single and multiple directions), evaluating 512 layouts with LES for each optimisation. Results show that optimal layouts exploit terrain features through clustering on elevated ridges for single‐direction cases, whereas multi‐directional optimisation requires balanced distributions to maintain performance. LES reveal complex flow phenomena, including terrain‐dependent wake behaviour, bidirectional turbine‐terrain interactions and non‐linear wake effects that necessitate high‐fidelity resolution. Analysis of the optimisation evolution reveals that BO serves primarily as a refinement tool, effectively exploiting physics‐informed initial designs but struggling to discover new optimal regions independently. These findings demonstrate that incorporating physical insight is essential for WFLO in complex terrain and that high‐fidelity simulations are necessary for reliable wind farm layout optimisation under such conditions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wind Energy is the property of Wiley-Blackwell 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/we.70122 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Large eddy simulation models Type: general – SubjectFull: Wind power plants Type: general – SubjectFull: Experimental design Type: general – SubjectFull: Surrogate-based optimization Type: general – SubjectFull: Wind turbine aerodynamics Type: general – SubjectFull: Atmospheric boundary layer Type: general Titles: – TitleFull: Physics‐Informed Bayesian Optimisation of Utility‐Scale Wind Farms in Synthetically Generated Complex Terrain Using LES. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jané‐Ippel, Christian – PersonEntity: Name: NameFull: Palacios, Rafael – PersonEntity: Name: NameFull: Laizet, Sylvain IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10954244 Numbering: – Type: volume Value: 29 – Type: issue Value: 6 Titles: – TitleFull: Wind Energy Type: main |
| ResultId | 1 |
