A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics
A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics
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DOI：
10.1016/j.taml.2023.100488
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发表时间：
2023-12
影响因子：
3.4
通讯作者：
C. Moss;R. Maulik;G. Iungo
中科院分区：
文献类型：
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作者：
C. Moss;R. Maulik;G. Iungo
随着旨在探测风资源和风力涡轮机运行的实验测量的增加，机器学习(ML)模型有望促进我们对支撑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的理解。然而，现有的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计算流体动力学(CFD)模拟数据，但降低了计算成本，因此提供的是替代模型，而不是增强的基于数据的物理洞察。虽然基于ML的代理模型有助于克服目前CFD模型计算成本高的局限性，但使用ML来揭示实验数据的过程或增强建模能力被认为是一个潜在的研究方向。在这封信中，我们讨论了在风力涡轮机尾迹和操作的最大似然建模领域的最新成就，以及新的有前途的研究策略。
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 Computational fluid dynamics (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.