MOTION-BASED WAVE INFERENCE WITH NEURAL NETWORKS: TRANSFER LEARNING FROM NUMERICAL SIMULATION TO EXPERIMENTAL DATA

Author(s)

Bisinotto, Gustavo A. ; De Mello, Pedro C. ; Cozman, Fabio G. ; Tannuri, Eduardo A.

• Document type: Journal article
• Source: PROCEEDINGS OF ASME 2023 42ND INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2023, VOL 1; v. N/A, p. 10-pg., 2023-01-01.

Abstract

The directional wave spectrum, which describes the distribution of wave energy along frequencies and directions, can be estimated from the measured motions of a vessel subjected to a particular sea condition by resorting to the wave-buoy analogy. Several methods have been proposed to address the inverse estimation problem; recently, machine learning techniques have been assessed as further alternatives. However, it may be difficult to gather large datasets of in-service motion responses and the associated sea states to train effective data-driven models. In this work, an encoder-decoder neural network is trained with the synthetic responses of a station-keeping platform supply vessel (PSV) to estimate the directional wave spectrum. This estimation model is directly applied to perform wave inference from motion data of wave basin tests with a small-scale model of the same vessel. Furthermore, fine-tuning is also used to incorporate experimental data into the neural network model. Results show a satisfactory match between estimated and measured values, both with respect to the energy distribution and the integral spectrum parameters, indicating that the proposed approach can be employed to obtain data-driven wave inference models when there is little or no availability of measured motion records and the corresponding sea conditions. (AU)

• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 21/00409-2 - Development of an environmental monitoring system from on-board motions of vessel movements with machine learning techniques
• Grantee: Gustavo Alencar Bisinotto
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)