| 12 Jan 2026
A Computational Fluid Dynamics surrogate model for wind turbine interaction including atmospheric stability
Abstract. Wind turbine wake and blockage effects can reduce the energy yield in wind farms and fast models are required to mitigate these effects by wind farm layout optimization. However, most fast models do not account for important physics that impact wake and blockage effects, as for example atmospheric stability. In this work, we propose a surrogate model of a Reynolds-averaged Navier-Stokes (RANS) wind farm model including atmospheric surface layer stability that is about five orders of magnitude faster than the original model. The surrogate model is based on a single wake database of stream-wise velocity deficit and wake-added turbulence intensity, generated by a RANS model. The surrogate model is evaluated against the RANS model for different inflow conditions and wind farms. The errors of the surrogate model are reduced by a factor two to four when taking into account wake-added turbulence intensity and the use of a rotor-averaging model in combination with a momentum-based wake superposition method. However, the computational effort of the surrogate model is still an order of magnitude larger compared to traditional engineering wake models and more research is required to reduce it.
Overall evaluation
This article presents a new wake and blockage model for wind farm flow simulation, based on the Look Up Table (LUT) constructed from single wake simulations using RANS CFD model with an actuator disk. The proposed methodology aims to fill a current gap in RANS-based models by modeling the effects of atmospheric stability and providing estimates for the wake-added turbulence. Both effects are known to have a strong influence on the build-up of wakes in wind farms and limit the accuracy of many existing models. From that perspective the work presented in this paper is relevant quite novel.
The benefits of the proposed approach are clearly demonstrated by the results of the two tests-cases presented in the article . The authors are also transparent about the remaining limitations of their approach - particularly with regard to computation time - while proposing clear tracks for potential improvement to be explored by future research.
Comparing the results of the RANS LUT using the RANS-AD shows that the model's performances are comparable to that of the full CFD model, while offering significant gains in computational time. Such a validation strategy makes sense, since the accuracy of the LUT depends on that of the underlying CFD model, as the conclusions rightly point out.
The paper is also well structured. The section on methodology provides detailed information that allows the relevance of the approach to be assessed and ensures the reproducibility of the results - at least in theory. The results are presented in a clear way and provide sufficient evidence to support the authors' claims. The paragraph discussing the performances of the various superposition models repeats some elements already covered in the methodology section, making it a bit long and harder to read than the remainder of the paper. It could probably be shortened, but this is a minor issue.
Overall, the quality of the scientific content is high and results demonstrate an appropriate technical depth. I would only point out that a lot of emphasis is put on the ability of the RANS-LUT to reconstruct the wake flow while the authors comment very little about the models performance regarding blockage and speed ups regions. Focusing on the wake makes sense, given the preeminence of the phenomena on the wind farm losses. However, the ability to accurately reproduce the entire flow field around the wind turbine is one of the main advantages of the RANS-LUT approach over engineering models. For instance, when discussing the results of the 8x8 wind farm, it would have been a great addition to address the behavior of the first row of turbines - especially for non-row aligned wind direction where some turbines are expected to benefit from local flow acceleration of the flow caused by their neighbors. Demonstrating the ability of the RANS-LUT to accurately reproduce these complex patterns - which many engineering models fail to capture - would have strengthened the overall argument.
Overall, this paper is very strong, relevant for the community and that introduce a novel approach to fill some of the current modeling gaps. Therefore, my recommendation would be to accept it for publication provided that the authors address the few minor comment listed below.
Technical questions
Minor comments