近日，美国德克萨斯大学达拉斯分校的Giacomo Valerio Iungo及其研究小组与美国宾夕法尼亚州立大学的Romit Maulik合作发表文章，呼吁加强数据驱动的风能流物理洞察研究。相关研究成果已于2023年12月15日在国际知名学术期刊《理论与应用力学快报》上发表。

在这份快报中，研究人员讨论了风力涡轮机尾迹和运行的机器学习（ML）建模领域的最新成就，以及新的有前途的研究策略。

随着风力资源和风力涡轮机运行的实验测量数据不断增加，机器学习（ML）模型在推进人们对大气边界层和风力涡轮机阵列之间相互作用的物理基础、产生的尾迹及其相互作用以及风能收集方面的理解上发挥着越来越重要的作用。然而，目前大多数用于预测风力涡轮机尾迹的ML模型只是以类似的精度重建CFD模拟数据，虽然降低了计算成本，但并没有提供比传统模型更深入的物理见解。尽管基于ML的替代模型有助于克服当前CFD模型的高计算成本限制，但利用ML从实验数据中揭示过程或增强建模能力被认为是一个潜在的研究方向。

附：英文原文

Title: A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics

Author: Coleman Moss, Romit Maulik, Giacomo Valerio Iungo

Issue&Volume: 2023-12-15

Abstract: With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations, machine learning (ML) models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays, the generated wakes and their interactions, and wind energy harvesting. However, the majority of the existing ML models for predicting wind turbine wakes merely recreate CFD-simulated data with analogous accuracy but reduced computational costs, thus providing surrogate models rather than enhanced data-enabled physics insights. Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models, using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue. In this letter, we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations, along with new promising research strategies.

DOI: 10.1016/j.taml.2023.100488

Source: http://taml.cstam.org.cn/article/doi/10.1016/j.taml.2023.100488?pageType=en