Sea state estimation from full-scale FPSO motions using neural networks: Assessment based on in-service records and metocean reanalysis - Gustavo A. Bisinotto; João V. Sparano; Alexandre N. Simos; Fabio G. Cozman; Marcos D. Ferreira; Eduardo A. Tannuri (Ocean Engineering, 2026)

Numerical modeling of wave basin experiments of a floating wind turbine with active thrust emulation: A discussion of important aspects - Lucas Carmo; Pedro C. de Mello; Renato M. Monaro; Jordi Mas-Soler; Alexandre N. Simos; Daniel Fonseca de Carvalho e Silva (Ocean Engineering, 2024)

A second-order slender-body approach for computing wave induced forces with application to a simplified semi-submersible FOWT model - Lucas H.S. Carmo; Alexandre N. Simos; Pedro C. de Mello (Ocean Engineering, 2023)

Sea state estimation based on the motion data of a moored FPSO using neural networks: An evaluation with multiple draft conditions - Gustavo A. Bisinotto; João V. Sparano; Alexandre N. Simos; Fabio G. Cozman; Marcos D. Ferreira; Eduardo A. Tannuri (Ocean Engineering, 2023)

Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference - Jordi Mas-Soler; Antonio Souto-Iglesias; Alexandre N. Simos (Journal of Marine Science and Engineering, 2020)
An innovative Bayesian motion-based wave inference method is derived and assessed in this work. The evaluation of the accuracy of the proposed prior distribution has been carried out using the results obtained during a dedicated experimental campaign with a scale model an Oil and Gas (O&G) semisubmersible platform. As for the Bayesian statistical inference approaches, the features of the proposed novel prior distribution, as well as the hypotheses adopted, are discussed. It has been found that significant improvements can be obtained if the new approach is adopted to estimate the sea conditions from measured vessel motions. Finally, it is possible to highlight a substantial reduction of the computing time when the sea conditions are estimated by means of the improved Bayesian method, if compared with the conventional approaches for motion-based wave inference.