RESEARCH ON RAPID RESPONSE PREDICTION AND PARAMETER INVERSION OF SEMI-SUBMERSIBLE WIND TURBINES BASED ON PINN

Yang Xinmeng; He Lun; Zhang Ruixing; Huang Zenghao; An Liqiang

doi:10.19912/j.0254-0096.tynxb.2025-0401

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 181-190. DOI: 10.19912/j.0254-0096.tynxb.2025-0401

RESEARCH ON RAPID RESPONSE PREDICTION AND PARAMETER INVERSION OF SEMI-SUBMERSIBLE WIND TURBINES BASED ON PINN

Yang Xinmeng¹, He Lun¹, Zhang Ruixing¹, Huang Zenghao^1,2, An Liqiang¹

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Abstract

Aiming to address the demands for rapid response prediction and parameter inversion of floating wind turbines, this study proposes a mesh-free solution method based on physics-informed neural network（PINN） for floating wind turbine dynamics. By embedding the six-degree-of-freedom dynamic equations of the floating wind turbines into the loss function and optimizing the spatial domain, the method achieves efficient prediction of dynamic responses and parameter inversion. The results demonstrate that, compared to traditional numerical methods, PINN significantly enhances computational efficiency while maintaining high accuracy, effectively overcoming error accumulation under large time-step conditions. Furthermore, the inverse PINN （IPINN） architecture achieves system parameter inversion with errors controlled within 1%, demonstrating precise identification capabilities.

Key words

offshore wind / physics-informed neural network / floating wind turbine / parameter inversion / response prediction / dynamics / mesh-free method

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Yang Xinmeng, He Lun, Zhang Ruixing, Huang Zenghao, An Liqiang. RESEARCH ON RAPID RESPONSE PREDICTION AND PARAMETER INVERSION OF SEMI-SUBMERSIBLE WIND TURBINES BASED ON PINN[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 181-190 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0401

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