Estimation of Directional Wave Spectra with Motion Data of Floating Structure Using Complex-Valued Neural Networks
Year: 2025
Type: article
Abstract: This study presents the development and validation of a complex-valued neural network model to predict wave conditions—such as wave direction, height, and period—surrounding floating structures. Accurate wave predictions are crucial for optimizing the design, operation, and maintenance of offshore platforms and floating offshore wind turbines, particularly in the context of digital twins. The proposed methodology leverages motion data obtained through numerical simulations of a floating structure to train the prediction model, enabling it to predict both the amplitude and phase information of the surrounding waves. The model successfully addresses the challenges of representing wave direction data in polar coordinates and capturing phase differences between motion components, which are difficult for traditional real-valued neural networks. The performance of the model was validated through various test cases, with the maximum prediction error found to be less than 10% and most predictions showing an error of less than 5%. Wave direction predictions demonstrated high accuracy, with errors consistently below 2%. While the model was trained using pseudo-measurement data, the results suggest that high-accuracy predictions can be achieved using real-world measurement data. This work contributes to enhancing wave prediction models for floating structures and is expected to improve the safety, performance, and long-term stability of such systems.
Source: Applied Sciences
DOI: 10.3390/app15031603
Authors: Taehyun Yoon, Young-IL Park, Jeong-Hwan Kim
Institution: Dong-A University
Language: English
Topic: Wave and Wind Energy Systems
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4407153353