A methodology for the holistic, simulation driven ship design optimization under uncertainty

The change of scenery in shipping has been evident over the past 20 years. The ever changing oil and fuel price, tough and cyclical market conditions, the constant societal pressure for a «green» environmental footprint combined with ever demanding international safety regulations create the new framework in which commercial ship designs are subject to. As a result of this current status of shipping commercial a change of attitude in the philosophy and process of ship design is required in order to shift towards new approaches where holistic approaches are deemed necessary. Apart from considering all the interrelationships between the subsystems that consist the vessel lifecycle and supply chain considerations are the key in successful and «operator oriented» designs. The methodology herein presented is built and fully integrated within the computer aided engineering (CAE) software CAESES that integrates in the design process CFD codes. It can be successfully used for the optimization of either of the basic design of a vessel or the operation of an existing vessel with regards to the maximization of the efficiency, safety and competitiveness of the final design. Stability, strength, powering and propulsion, safety, economics, operational and maintenance and in service management considerations are tightly integrated within a fully parametric model. This tight integration enables the user to simulate the response of the model in variations of the geometrical, design variables of the vessel (including its propeller) under conditions of simulation and uncertainty. For each of the potential design candidates, its operation is simulated based and assessed on a lifecycle basis and under conditions of uncertainty. The uncertainty modelling is extensive and in several levels including but not limited to Economic, Environmental, and Operational uncertainty as well an accuracy modelling of the methodology itself. The methodology is applied on the iron and coal seaborne trade and more specifically the case of large bulk carriers. The uncertainty models are based on Big Data statistical analysis, from the on-board real time monitoring systems of a fleet of 15 vessels for a period of more than 18 months on the examined trad