首页|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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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.
Machine learningWakeWind turbineWind farmSupervisory control and data acquisition
Coleman Moss、Romit Maulik、Giacomo Valerio Iungo
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Wind Fluids and Experiments(WindFluX)Laboratory,Mechanical Engineering Department,The University of Texas at Dallas,Richardson 75080,USA
College of Information Sciences and Technology,The Pennsylvania State University,State College 16801,USA
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