Hybrid Artificial Intelligence-based Computational Fluid Dynamics model for optimizing Offshore Wind Farm aerodynamics under variable marine climate conditions
DOI:
https://doi.org/10.18686/cest634Keywords:
Offshore Wind Farm optimization , Artificial Intelligence , Computational Fluid Dynamics , wake interaction modelling , surrogate modelling , marine climate variability , multi-objective evolutionary optimizationAbstract
Aerodynamic optimization of Offshore Wind Farms (OWF) is challenged by nonlinear wake interactions, turbulence transport, and stochastic Marine Climate Conditions (MCC). High-fidelity Computational Fluid Dynamics (CFD) models capture these dynamics accurately but impose prohibitive computational costs for large-scale optimization. Analytical wake models offer computational efficiency but oversimplify complex turbulence interactions. This study presents a hybrid Artificial Intelligence (AI)-CFD that integrates Deep Neural Network (DNN) surrogates with selective CFD validation to enable efficient, robust optimization under MCC. The model employs unsteady Reynolds-averaged Navier-Stokes simulations with actuator-line turbine representation to generate training data, which are used to train an ensemble surrogate model incorporating dimensionally reduced climate states. Uncertainty-driven adaptive sampling triggers CFD validation for high-uncertainty configurations, maintaining physical fidelity while accelerating optimization. A multi-objective evolutionary algorithm (NSGA-II) optimizes turbine layout, yaw angles, and pitch controls to balance power generation, wake losses, and structural loading. Validation on an 80-turbine, 320 MW wind farm in Dhanushkodi, Tamil Nadu, sea proves 7.8% power improvement, 23.5% wake-loss reduction, and 11.2% network load decrease compared to the baseline, with 7.2× computational speedup vs. CFD-only optimization. Sensitivity analyses confirm robustness across wind speeds (6–12 m·s−1), turbulence intensities (5–15%), and inflow directions (0–30°). The model establishes a scalable methodology for optimizing OWF under realistic MCC.
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Copyright (c) 2026 Hayder M. Ali, Rachel Nallathamby, Saravanan Ramaiah, Radhika Rani Chintala, Aseel Smerat, Temur Eshchanov, Bekzod Madaminov, Sudhakar Sengan
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