From 7712fd96fb38c8b54384f95ce4447304eeadd27d Mon Sep 17 00:00:00 2001 From: Trailblazer Date: Fri, 17 Apr 2026 12:06:05 +0200 Subject: [PATCH 1/3] Add new paper on yawed wind turbines to papers.yml Added a new paper entry with details including title, authors, affiliations, link, abstract, image, and date. --- docs/papers.yml | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/papers.yml b/docs/papers.yml index 3044ecb6d..977aa65de 100644 --- a/docs/papers.yml +++ b/docs/papers.yml @@ -2,6 +2,17 @@ # information to generate the "Research Showcase" papers: + - title: An Engineering Model for Static Yawed Wind Turbines Based on Actuator Line Simulations and Symbolic Regression + authors: + - Haoyuan Sun (1) + - Andrea Sciacchitano (1) + - Wei Yu (1) + affiliations: + 1: Faculty of Aerospace Engineering, Delft University of Technology, + link: http://dx.doi.org/10.1002/we.70118 + abstract: "Yaw engineering models are commonly used as add-ons to the industrial Blade Element Momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds-Averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5 MW wind turbine, covering a range of yaw angles ($\gamma$) and thrust coefficients ($C_T$) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade-off between accuracy and complexity, with complexity constrained to remain comparable to Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared to the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the non-restoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce." + image: https://raw.githubusercontent.com/MilesCranmer/PySR_Docs/ + date: 2026-04-16 - title: Discovering parametrizations of implied volatility with symbolic regression authors: - Martin Keller-Ressel (1,2) From 702f293190e6decd3e1f44995937a840feb8e42c Mon Sep 17 00:00:00 2001 From: Trailblazer Date: Fri, 17 Apr 2026 13:46:12 +0200 Subject: [PATCH 2/3] Update papers.yml --- docs/papers.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/papers.yml b/docs/papers.yml index 977aa65de..0dcdff91d 100644 --- a/docs/papers.yml +++ b/docs/papers.yml @@ -8,10 +8,10 @@ papers: - Andrea Sciacchitano (1) - Wei Yu (1) affiliations: - 1: Faculty of Aerospace Engineering, Delft University of Technology, + 1: Faculty of Aerospace Engineering, Delft University of Technology link: http://dx.doi.org/10.1002/we.70118 abstract: "Yaw engineering models are commonly used as add-ons to the industrial Blade Element Momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds-Averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5 MW wind turbine, covering a range of yaw angles ($\gamma$) and thrust coefficients ($C_T$) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade-off between accuracy and complexity, with complexity constrained to remain comparable to Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared to the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the non-restoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce." - image: https://raw.githubusercontent.com/MilesCranmer/PySR_Docs/ + image: https://github.com/MilesCranmer/PySR/blob/c435527ccddb89a7a4e2835fb064679b3ed3e537/docs/papers.yml date: 2026-04-16 - title: Discovering parametrizations of implied volatility with symbolic regression authors: From e86be482ed5686995d7093bf1f61b095e205e9e2 Mon Sep 17 00:00:00 2001 From: Trailblazer Date: Fri, 17 Apr 2026 13:46:57 +0200 Subject: [PATCH 3/3] Change image link in papers.yml Updated the image link for the yaw engineering models paper. --- docs/papers.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/papers.yml b/docs/papers.yml index 0dcdff91d..f72d87423 100644 --- a/docs/papers.yml +++ b/docs/papers.yml @@ -11,7 +11,7 @@ papers: 1: Faculty of Aerospace Engineering, Delft University of Technology link: http://dx.doi.org/10.1002/we.70118 abstract: "Yaw engineering models are commonly used as add-ons to the industrial Blade Element Momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds-Averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5 MW wind turbine, covering a range of yaw angles ($\gamma$) and thrust coefficients ($C_T$) over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade-off between accuracy and complexity, with complexity constrained to remain comparable to Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared to the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the non-restoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce." - image: https://github.com/MilesCranmer/PySR/blob/c435527ccddb89a7a4e2835fb064679b3ed3e537/docs/papers.yml + image: https://github.com/TrailblazerH/PySR_Docs/blob/c91caa5a6b34593164f092d8899fc126c6ede8e9/images/Example.png date: 2026-04-16 - title: Discovering parametrizations of implied volatility with symbolic regression authors: