To determine the optimal trimaran configuration for best calm-water transportation efficiency, a Deep Neural Network (DNN) is trained with sufficient computational results provided by, as an example, an in-house developed potential-flow code called Multi-hull Simple-source Panel Method (MSPM). Even though the computational method is extremely efficient in accurately establishing the mapping relation between the key design parameters governing the trimaran configuration problem and the resulting calm-water transportation cost, the modeling efforts are non-trivial since the number of geometric and configuration parameters in a typical situation is large. In this work, we demonstrate how the “Big Data” of computational results can be effectively utilized in training a DNN. An optimal trimaran configuration solution within a specified design space, subject to realistic range constraints, can be quickly determined in a minimal amount of time with the DNN. A demonstrative case study is provided for illustration.

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