Introduction To and Comparison Of Deep Learning and Optimization Approaches to Analytical Wake Modeling of a Tilted Wind Turbine

Keywords

wind farm optimization, deep learning, neural nets, wind energy, tilted turbine, wakes

Abstract

This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model’s axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

Original Publication Citation

Cutler, J., Bay, C., and Ning, A., “Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine,” Wind Energy Science, Vol. 11, No. 37–49, Jan 2026. doi: 10.5194/wes-11-37-2026

Document Type

Peer-Reviewed Article

Publication Date

2026-1

Publisher

Wind Energy Science

Language

English

College

Ira A. Fulton College of Engineering

Department

Mechanical Engineering

University Standing at Time of Publication

Full Professor

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