Model-based prediction of vessel response is valuable for planning and execution of marine operations. Response-based operation criteria are expected to give less downtime and cheaper and safer operations than criteria based on wave height and wave period. At least this holds if the response model has sufficient accuracy. The accuracy can in principle be improved by tuning the model using measured inputs and outputs. It is envisioned that an advisory system for planning and execution of marine o

Model-based prediction of vessel response is valuable for planning and execution of marine operations. Response-based operation criteria are expected to give less downtime and cheaper and safer operations than criteria based on wave height and wave period. At least this holds if the response model has sufficient accuracy. The accuracy can in principle be improved by tuning the model using measured inputs and outputs. It is envisioned that an advisory system for planning and execution of marine operations will contain a module for continuous model improvement based on on-site measurements of excitation and response. A premise for this is that the measurements be of sufficiently high quality. To test the potential of automatic model tuning an established numerical vessel model is subjected to tuning with high-precision data from a model test with wave disturbance only. This may give information of how well modelling can be achieved using tuning under favourable conditions and serve as a benchmark for tuning under noisy conditions. In addition the results may give suggestions for improvement of the mathematical model. A prototype tuning software is written in Matlab. The tuning principle is to minimize the output error, i.e. the difference between measured and simulated response, by adjusting the model's parameters. For the minimization a quasi-Newton method is used. The tuning software is tested with data from the model test and found to function as intended. Examples of tuning are given.