A CFD-based adjoint solver tool is presented to modify the hullform of a trimaran ship. Cost function of optimization process is considered total for resistance of trimaran at cruise speed. Mesh sensitivity gives sensitivity of the cost function associated with mesh deformation. Mesh morphing tool is implemented to alter the geometry without the need to revisit the CAD environment. The position of mesh morphing's control points are the design variables used in the optimization process. The aim of this optimization is to demonstrate the limitations and capabilities of the mesh morphing adjoint-based method for hydrodynamic complex geometries. Based on the numerical findings, a 6.67% reduction in total resistance of the trimaran is observed in a limited number of iterations. Meanwhile, major modification is observed on the side hull. Development of a CFD-based optimization platform with less computational time and effort (five optimization loops) is the result of the present study.

The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in an expedited manner. Regression Trees (RTs), Support Vector Machines (SVMs), and Artificial Neural Network (ANN) regression models are applied for dataset training. A hullform optimization was implemented for various catamarans, including dimensional and hull coefficient parameters based on resistance, structural weight reduction, and battery performance improvement. Design distribution based on Lackenby transformation fulfills all of the design space, and sequentially, a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on the generated data of the case study. This study shows that the ANN algorithm correlates well with the measured resistance. Accordingly, by choosing any new design based on owner requirements, GA optimization obtained the final optimum design by using an ML fast resistance calculator. The optimization process was conducted on a 40 m passenger catamaran case study that achieved a 9.5% cost function improvement. Results show that incorporating the ML tool into the GA optimization process accelerates the ship design process.