Data-driven Reynolds-averaged Navier–Stokes (RANS) turbulence closures are increasing seen as a viable alternative to general-purpose RANS closures, when LES reference data is available—also in wind-energy. Parsimonious closures with few, simple terms have advantages in terms of stability, interpret-ability, and execution speed. However experience suggests that closure model corrections need be made only in limited regions—e.g. in the near-wake of wind turbines and not in the majority of the flow. A parsimonious model therefore must find a middle ground between precise corrections in the wake, and zero corrections elsewhere. We attempt to resolve this impasse by introducing a classifier to identify regions needing correction, and only fit and apply our model correction there. We observe that such classifier-based models are significantly simpler (with fewer terms) than models without a classifier, and have similar accuracy, but are more prone to instability. We apply our framework to three flows consisting of multiple wind-turbines in neutral conditions with interacting wakes.

当 LES 参考数据可用时，数据驱动的雷诺平均 Navier-Stokes (RANS) 湍流闭合越来越被视为通用 RANS 闭合的可行替代方案 - 也适用于风能。具有少量简单术语的简约闭包在稳定性、可解释性和执行速度方面具有优势。然而经验表明，闭合模型修正只需要在有限的区域进行——例如在风力涡轮机的近尾流中，而不是在大部分流动中。因此，简约模型必须在尾流的精确修正和其他地方的零修正之间找到一个中间地带。我们试图通过引入分类器来识别需要校正的区域来解决这一僵局，并且只在那里拟合和应用我们的模型校正。我们观察到这种基于分类器的模型比没有分类器的模型更简单（术语更少），并且具有相似的准确性，但更容易出现不稳定。我们将我们的框架应用于由多个风力涡轮机在中性条件下与相互作用的尾流组成的三个流。