.png){kind=icon}
.png){kind=icon}
Return
A transformer-based architecture for wave height forecasting within rectangular moonpool and its generalization performance study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.oceaneng.2025.120313.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Moonpools are widely used in offshore oil and gas drilling and marine energy development. A calm wave environment is required at an operational site for effective functioning. However, affected by incident waves or harmonic ship motions, the fluid inside the moonpool may undergo significant resonant motions that can significantly disrupt operations and compromise offshore safety. Therefore, dependable moonpool wave height forecasting is important for guiding offshore operations. Our study aimed to develop a generalizable model for moonpool wave height forecasting. Initially, we conducted a model test to obtain the wave height within the moonpool and hull motion data. Subsequently, a self-supervised training model called Generalizable Wave height Transformer (GenWT) based on the Transformer framework is proposed. This model utilizes moonpool resonance information to enhance predictive accuracy. Experiments demonstrate the superior performance of GenWT compared to other benchmark models such as Informer, long short-term memory (LSTM) models, achieving 26.15% and 27.98% MSE reduction in two sea conditions, respectively. Owing to the pretrain + finetune strategy, the time and space complexity of the GenWT model can be reduced quadratically. Furthermore, the same pre-model can be applied to different ocean engineering forecast tasks while maintaining optimal performance, highlighting GenWT's robustness and potential in ocean engineering applications.
Keywords:
Moonpool
Resonant frequencies
Wave height forecasting
Machine learning
Transformer
Journal
IF:
5.5
Papers:
5.8K
Citations:
7.6W
Organization
No organization information available