The Reynolds-averaged Navier–Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their accuracy. To address this issue, recent research has explored data-driven techniques such as data assimilation and machine learning.

中文翻译：

---

通过校正强迫项在粗 RANS 模拟中稀疏速度参考数据的变分数据同化方法

雷诺平均纳维斯托克斯 (RANS) 方程为解决工程应用中的流体流动问题提供了一种计算高效的方法。然而，使用闭合模型来表示湍流效应可能会降低其准确性。为了解决这个问题，最近的研究探索了数据驱动技术，例如数据同化和机器学习。