Trimaran hull forms have been popular recently in both commercial and military usage due to reduction in resistance at high speeds, better stability, and greater deck area compared to conventional monohull vessels. Determination of the location of the side hulls is most critical to get higher hydrodynamic performance. Therefore, many studies in the literature are related to defining the location of the side hulls for trimaran vessels. Most of the studies have been carried out experimentally or numerically. In this study, an artificial neural network (ANN) model was used to predict the residual resistance coefficient of a trimaran model. The model uses four parameters which are the transverse and longitudinal positions of the side hulls, the longitudinal centre of buoyancy and the Froude number to predict the residual resistance of the trimaran model. The experimental data of the trimaran model were used to train the neural network model in order to develop a more reliable model. Several neural network models were developed and tested to find the one with minimum error. The study showed that the residual resistance coefficients of the trimaran model were predicted with high accuracy levels compared to the model experimental data. It was also shown that an ANN is a useful alternative method to model tests and numerical simulations. The developed model can be used to reduce the number of model tests or numerical simulations as well as to obtain the optimum location of the side hulls in terms of resistance.

/pdf/prediction-of-residual-resistance-of-a-trimaran-vessel-by-1pccboiu.pdf

Prediction of residual resistance of a trimaran vessel by using an artificial neural network

Study on the surf-riding and broaching of trimaran in oblique stern waves

Predicting ship surf-riding, wave blocking, and broaching in oblique stern waves is essential for the deterministic motion prediction and control of marine structures. In this paper, the effectiveness of the dataset is ensured by using simulation data from the numerical method for the numerical simulation of the autopilot trimaran. The effect of the database obtained by classifying initial conditions on the prediction accuracy is studied first. Then, multi-condition learning and state vectors are used to make the neural network better predict the occurrence of surf-riding and broaching in various wave conditions. Finally, different input features are used to establish the database to train the neural network, and the influence of the database by different input features on the accuracy of the surf-riding, wave blocking, and broaching prediction is discussed and analyzed. The study ultimately finds that combining multi-condition learning with LSTM, optimizing input features, and introducing state vectors in the time series can effectively enhance the accuracy of the model's predictions. 

Surf-riding and broaching prediction of ship sailing in regular waves by LSTM based on the data of ship motion and encounter wave

Research on the influence of trimaran side hull layout on its manoeuvrability

Trimarans, as high-performance ships, the complex flow field of which generated by the interaction between the main hull and the outrigger plays a crucial role in its hydrodynamic performance compared with conventional mono-hulls. In this paper, a new framework is proposed for calm water resistance prediction and optimization of a novel Trimaran, Tri-SWACH with particular consideration of its outrigger layouts. Firstly, Through the comparison of calm water resistances for the novel Tri-SWACH hull in semi-constrained state using CFD simulations and model tests, the CFD simulation is verified to be a valid approach to build the database for surrogate model. PRS, BPNN and KRG methods are used to build the hybrid surrogate models. The effect of using different weight calculation strategies are examined. It is found that the hybrid surrogate model established by using the heuristic method shows higher prediction accuracy and generalization ability compared with single surrogate models and other weight calculation strategies. Based on the proposed hybrid surrogate model, the particle swarm algorithm is used to optimize the solution in the design space. The effectiveness of the optimal outrigger layout is verified by comparing with the CFD method. Compared to the worst outrigger layout, the calm water resistance of the optimal outrigger layout can be reduced by 5.12%. Finally, the hydrodynamic mechanism behind the difference of resistance under the initial, optimal, and worst outrigger layouts are revealed by comparing and analyzing the wave-making interference of the main hull and outriggers. The methods and findings of this study can provide theoretical guidance and support for the design and operation of Tri-SWACH. 

Resistance prediction and optimization of Tri-SWACH using hybrid surrogate model with particular consideration of outrigger layout

The effect of side-hull position on the seakeeping performance of a trimaran at various headings

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The effect of side-hull position on the seakeeping performance of a trimaran at various headings