Data assimilation (DA) is useful to improve simulation accuracy and tune model parameters. The improvement of the accuracy of the estimation of flows around a ship hull based on the DA method contributes to design ship hull form or energy-saving device, and we construct a novel framework for DA-based parameter tuning of RANS turbulence models carrying out the experiment at the towing tank. Particle filter (PF) is employed to estimate the likelihood of two parameters ( \(C_{2}\) , \(C_{4}\) ) of the explicit algebraic stress model (EASM) with measured data from 40 fiber Bragg grating (FBG) pressure sensors near the ship stern. This study presents the first application of data assimilation for parameter tuning of the EASM using measured data of the pressure distribution on a three-dimensional ship model. In the analysis using a 749 gross-ton general cargo ship model, it is confirmed that the parameter tuning using measured pressure distribution significantly affects the flow field near the stern. Moreover, the reconstructed pressure and wake distributions show good agreement with observations, and pressure recovery after flow separation is particularly improved. These findings imply that turbulence model parameters can be effectively tuned, and unobserved pressure and wake can be estimated by the present DA method combining RANS simulation and FBG measurements. Also, present work has the possibility to contribute to the improvement of the accuracy at a full-scale sea trials in the future.
