发表机构
University of Seville; University of Padova（塞维利亚大学; 帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于内源尾流模型EndoWake的GPU加速风电场布局搜索，通过筛选-确认流程，在保持优化质量的同时，速度提升7.9倍（年能量14.9倍）。
AI 中文摘要
风电场布局优化决定涡轮机的位置以最大化年发电量，而尾流效应决定了其中多少能量被生产。精确的尾流模型对于搜索而言过于缓慢。我们基于EndoWake构建，这是一种内源尾流模型，其中每个网格单元的风速是一个变量，通过线性约束与其上风邻居相连，从而使尾流场、选址决策和项目约束共享一个混合整数模型。其四个参数仅针对雷诺平均纳维-斯托克斯（RANS）模拟的单尾流场校准一次，GPU每秒可对每个风向评估超过一百万个布局。在Lillgrund，将EndoWake引导的搜索与基于PyWake的流程进行比较，在无搜索调用的RANS评判下，揭示了尾流模型的保真度并非其优化下布局的质量。搜索被吸引到采样风向之间的间隙，即“海妖”；对场的诊断发现了第二个伪影，即每个尾流边缘外的乐观带，称为“美人鱼单元”，据我们所知此前未见报道。在随机方向评判下，两种流程在统计上无显著差异。我们的主要结果利用了这种速度：EndoWake在GPU上筛选局部搜索的候选移动，而PyWake确认每个接受的移动。在二十个未见过的起始点上，使用一个GPU和三十个CPU进程，这种筛选-确认搜索匹配了仅用PyWake搜索的价值，速度快7.9倍，年能量上快14.9倍。
英文摘要
Wind farm layout optimization decides where to place turbines to maximize annual energy production, and wake effects decide how much of that energy is produced. Accurate wake models are too slow for a search. We build on EndoWake, an endogenous wake model in which the wind speed at every grid cell is a variable linked 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 against the single-wake field of a Reynolds-averaged Navier-Stokes (RANS) simulation, and a GPU scores over a million layouts per second per wind direction. Comparing EndoWake-guided searches with a PyWake-based pipeline at Lillgrund, under a RANS judge that no search calls, revealed that the fidelity of a wake model is not the quality of the layouts optimized under it. A search is drawn to the gaps between sampled wind directions, the sirens; a diagnostic of the field finds a second artifact, an optimistic band off each wake edge, the mermaid cells, not reported before to our knowledge. Judged at random directions, the two pipelines are statistically indistinguishable. Our main result puts this speed to use: EndoWake screens the candidate 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, this screen-and-confirm search matches the PyWake value of a search on PyWake alone 7.9 times faster, and 14.9 times faster on the annual energy.
CommentsRevised version of the preprint circulated in September 2026 (ResearchGate): the RANS reference is recomputed with the published constants of the closure (C_R = 4.5); the main conclusions are unchanged. 42 pages, 7 figures
