Development of an environmental monitoring system from on-board motions of vessel movements with machine learning techniques

• Grant number: 21/00409-2
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: November 01, 2021
• End date: October 31, 2024
• Field of knowledge: Engineering - Mechanical Engineering
• Principal Investigator: Eduardo Aoun Tannuri
• Grantee: Gustavo Alencar Bisinotto
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

This research project comprises the investigation of model-based and data driven methodologies involved in the estimation of environmental loads acting on ships and oceanic systems, in order to design a monitoring system for ocean wave and current. The proposed system relies on the on-board measurements obtained from the vessel movements, its mathematical model and the information from other commonly available sensors. Machine learning theory combined with state observation techniques will be addressed in the project to deal with the complexity of the estimation task and to increase the robustness of the results. Among the applications of an environmental monitoring system are the support for operational decisions or advice on marine activities, including personnel and environmental safety, fuel and route consumption and others. In addition, Dynamic Positioning (DP) systems are benefited, since improvements on the system overall efficiency can be achieved by feed forward controllers based on the pre-compensation of the environmental loads. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BISINOTTO, GUSTAVO A.; DE MELLO, PEDRO C.; COZMAN, FABIO G.; TANNURI, EDUARDO A.. MOTION-BASED WAVE INFERENCE WITH NEURAL NETWORKS: TRANSFER LEARNING FROM NUMERICAL SIMULATION TO EXPERIMENTAL DATA. PROCEEDINGS OF ASME 2023 42ND INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2023, VOL 1, v. N/A, p. 10-pg., 2023-01-01. (19/07665-4, 21/00409-2)

BISINOTTO, GUSTAVO A.; DE MELLO, PEDRO C.; COZMAN, FABIO G.; TANNURI, EDUARDO A.. Motion-Based Wave Inference With Neural Networks: Transfer Learning From Numerical Simulation to Experimental Data. JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME, v. 146, n. 5, p. 9-pg., 2024-10-01. (19/07665-4, 21/00409-2)

BISINOTTO, GUSTAVO A.; DE MELLO, PEDRO C.; QUEIROZ FILHO, ASDRUBAL N.; IANAGUI, ANDRE S. S.; SIMOS, ALEXANDRE N.; TANNURI, EDUARDO A.. Estimating wave spectra from the motions of dynamically positioned vessels: An assessment based on model tests. APPLIED OCEAN RESEARCH, v. 121, p. 14-pg., 2022-03-01. (21/00409-2)

BISINOTTO, GUSTAVO A.; COTRIM, LUCAS P.; COZMAN, FABIO G.; TANNURI, EDUARDO A.. ASSESSMENT OF SEA STATE ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THE MOTION OF A MOORED FPSO SUBJECTED TO HIGH-FREQUENCY WAVE EXCITATION. PROCEEDINGS OF ASME 2022 41ST INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2022, VOL 5B, v. N/A, p. 10-pg., 2022-01-01. (21/00409-2)

LAVRA DIAS, PEDRO FELIPE; BISINOTTO, GUSTAVO ALENCAR; DE PAULA CAURIN, GLAUCO AUGUSTO; COSTA, ANNA HELENA REALI; TANNURI, EDUARDO AOUN. ESTIMATION OF OCEAN'S CURRENTS ACTING ON A TURRET-MOORED FPSO USING MACHINE LEARNING. PROCEEDINGS OF ASME 2022 41ST INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2022, VOL 5B, v. N/A, p. 10-pg., 2022-01-01. (21/00409-2)

COTRIM, LUCAS P.; HUANG, ALEX S.; BISINOTTO, GUSTAVO A.; CUNHA, RODRIGO S.; BARREIRA, RODRIGO A.; COSTA, ANNA H. REALI; GOMI, EDSON S.; TANNURI, EDUARDO A.. COMBINING MODEL-BASED AND DATA-DRIVEN METHODS TO ESTIMATE THE ROLL MOTION OF A SPREAD-MOORED FPSO. PROCEEDINGS OF ASME 2023 42ND INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2023, VOL 1, v. N/A, p. 8-pg., 2023-01-01. (21/00409-2)