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

Coleman Moss, Romit Maulik, Giacomo Valerio Iungo

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number100488
JournalTheoretical and Applied Mechanics Letters
Volume14
Issue number1
DOIs
StatePublished - Jan 2024

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Environmental Engineering
  • Civil and Structural Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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