arXiv:2610.07844 · v2
GPU-accelerated wind farm layout search with a distilled endogenous wake model (EndoWake)
摘要
We build on EndoWake, an endogenous wake model that links the wind speed at every grid cell to its upwind neighbors by linear constraints, so that wake field, siting decisions and project constraints share one mixed-integer model. Its four parameters are calibrated once on the single wake of a Reynolds-averaged Navier-Stokes (RANS) simulation, and a graphics processing unit (GPU) scores over a million layouts per second per wind direction. At Lillgrund, we compare searches guided by EndoWake and by PyWake under a RANS judge that no search calls: the fidelity of a wake model is not the quality of the layouts optimized under it. Two diagnostics separate a quadrature error between the sampled wind directions from a surrogate bias localized off each wake edge, not reported before to our knowledge; at random directions, five paired starts detect no difference between the pipelines. The main result puts the speed to use: EndoWake ranks the moves of a local search on the GPU, and PyWake confirms every accepted move. On twenty unseen starts, with one GPU and thirty CPU processes, screen-and-confirm reaches similar mean gains to a search on PyWake alone, 7.9 times faster at one wind speed and 14.9 times faster on the annual energy (3.0 and 4.7 with a proof of local optimality charged to the timed search); on a second site it keeps its speed, and its local optima fall slightly short of those of PyWake alone. Code and data are deposited on Zenodo.
作者备注：v2, revised (October 2026): formal terms replace the metaphors of v1; new comparisons with TOPFARM and on a second site (Horns Rev 1); sensitivity to the length of the candidate list; speedups with and without the final certificate. The replication package (doi:10.5281/zenodo.22017446) now includes the local searches, the sector quadrature and the fast evaluator. 39 pages, 5 figures