Thermal zone archetypes in ships: a swift energy characterization method for thermal load and energy demand assessment

Heating, ventilation, and air conditioning systems onboard cruise ships account for a significant portion of the overall energy balance. These systems are designed to ensure optimal hygrothermal comfort for passengers and crew members. Achieving this requires the use of suitable tools, as well as careful sizing and design to minimize energy consumption throughout the lifecycle of the ship. A well-established methodology, based on dynamic simulation, is available in scientific and technical literature to pursue this objective. However, this methodology requires detailed 3D modelling of the whole ship, including all spaces requiring indoor air temperature and relative humidity control. Consequently, the thermal zoning process within the 3D modelling of cruise ships to estimate thermal loads and heating/cooling requirements is time-consuming and challenging. In this paper, a novel approach for swiftly defining energy models for thermal loads and energy needs assessment is presented. This approach is based on the dynamic simulation and the energy characterization of thermal zone archetypes mostly available onboard ships. The process starts with the selection and the development of the 3D detailed model for a sample ship. This step is crucial for the success of the methodology because the sample ship must include as much thermal zone typology as possible. After that, different simulations are conducted to define a dataset of results on which the model will be trained. Thus, the definition of thermal zone archetypes is conducted by analysing all thermal zones that have been simulated in the 3D modelled sample ship. After that, the parametrization procedure with the selection of thermophysical and geometrical parameters, as well as setpoints temperature, humidity, and external fresh air ratio to be supplied to the thermal zones is conducted. In this way, a set of design parameters is defined and suitable simulations are conducted just for the archetypes to assess the effects of parameter variation. Finally, the results obtained for the archetypes by varying the parameters are compared with the simulation results obtained from the simulation of the whole ship. From this comparison, suitable mathematical regressions are conducted to obtain appropriate mathematical equations capable of expressing thermal loads and hourly energy needs of archetypes as a function of the parameters previously selected. In this way, it is possible to define a grey-box model and build a dataset of archetypes that can be swiftly adopted for building any kind of ship with a modular and flexible approach.