Because interfacing with real world contraptions is both expensive and time-consuming, the first phase focuses on interfacing with the digital twins in simulated environment. This allows cost-effective and efficient interfacing with emulated hardware and software output, and the possibility to run simulations faster than perceived human real time and gives the possibility to train various controlled weather and wave conditions. The benchmark will be the simulated ship itself, hereby providing a 100% guarantee that the algorithm is fit for purpose. Model environment and emulate sensors: wave, wind and current based on sensor data. Create or improve realistic sensor data from simulator: wind sensor, Radar, Lidar, Wave Radar, MRUs, wave buoys, Acoustic Doppler Current Profiler, thruster command and actual output, crane cargo specific sensor, Ballast information, model various cargo and ballast loads and ballast loads, as well as mooring forces in a simulator. Focus here is to model exact current, wind, and wave environment, which is key to precisely predict ship motions, possible applications are on-board real-time augmented tools like energy optimization tools, and collision prevention and other prediction systems. Parallel to sensing and modelling environment and ships, a standard method for testing digital ship models in virtual sea trials has to be agreed upon, both theoretical and programmatically. Once the environment and benchmarking are in place, data-driven machine learning algorithms can be developed and tested in a fast and effective manner (third step). For this purpose, develop an API to interface with simulation framework for algorithms to learn, analyse and predict the simulated ship motions, hereby making a data-driven ship model. When algorithms are stable enough and trustable against the benchmark, the next step is to switch from the realistic simulated data to the offline and online real data (step 4).
Project leader: Joel Alexander Mills
Institution: OFFSHORE SIMULATOR CENTRE AS