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Geometry to Parameter Mapping Based on Neural Networks

Geometry to Parameter Mapping Based on Neural Networks

In the process of geometry-based optimization, the problem of having to model a resulting geometry without knowing the exact values of characteristic parameters can arise. In order to open and further edit the optimized geometry inside the CAESES environment, the parameter set belonging to the new shape needs to be determined. The use of neural networks offers a great opportunity to solve this kind of problem since they are a powerful tool for constructing a predictive model based on data.

Designing Wind Assisted Commercial Cargo Vessels

Designing Wind Assisted Commercial Cargo Vessels

Within the framework of the project “Transitioning to Low Carbon Sea Transport” (TLCSeaT), focusing on the new-build of a sail assisted general cargo and island supply vessel for the Republic of Marshall Islands (RMI), CAESES has been used to create a parametric design and calculation environment that can be used for the evaluation of different design options during the vessel’s concept and early design phases.