| 13 Feb 2026
Fast blockage models for wind-farm power prediction
Abstract. Large offshore wind farms can trigger atmospheric gravity waves, with the associated hydrostatic blockage effect impacting their energy yield. Unfortunately, to date no tools exist that can model this wind-farm gravity-wave interaction and blockage at a computational cost that is not drastically higher than conventional engineering wake models. To address this, this paper applies insights from two-scale momentum (2SM) theory to an atmospheric perturbation model (APM), thereby significantly speeding up the latter. This leads to two different models, with one using pre-computed farm-level coefficients to compute the turbine forces and power output, while the other relies on repeated wake model evaluations. The core 2SM hypothesis and both developed models are validated using a prior LES dataset of a large wind farm. Both fast 2SM—APMs perform well, predicting blockage-corrected farm power at a computational cost that is only a factor 40 slower than a standalone wake model.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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The paper describes how the recently proposed two-scale momentum (2SM) theory can be coupled to a atmospheric perturbation model (APM) developed by the authors. The approach is novel and the paper well written, mathematically sound and mostly easy to follow, however the structure, validation and “fast” element of the paper need improvement.
The only part that seems to be “fast” is hidden in subsection 4.3.1, so either the title needs to be adapted, as it is currently misleading, or more work needs to be spend on timing the three different flavours of the coupling that are presented and show how the currently not so “fast” approaches can be sped up. Whilst going through section 4 and 5 the motivation of using one approach over the other remains somewhat obscure and currently just seems like a list of possible methods.
While the paper is mathematically rigorous, some decisions and sensitivities need to be explored further, especially how to choose the size of the control volume in the 2SM approach such that the error in determining the average wind farm wind speed, Uf, does not depend on the wind farm layout. The authors currently are able to avoid addressing this issue, as they use idealised square wind farms, the same idealised conditions also the 2SM theory is based on. At least one non-rectangular wind farm (or at least run one different wind direction) test case need to be added to showcase that the proposed approach has more general applicability and provides similar results as for an idealised setup. Additionally, one case above rated wind speed could be added, such that the farm has some CT, CP variation (potentially already the case but not explicitly discussed in the paper.
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