The effects of online-training artificial neural network mechanism and multi-stage parametric modeling method on simulation-based design system for ship optimization

To dynamically re-calibrate the weights of efficiency and precision in high-dimensional issues of ship resistance optimization, a hybrid framework containing both Computational Fluid Dynamics (CFD) solver and Artificial Neural Network (ANN) surrogate model, and driven by an online training Self-Adaptive Mechanism is proposed in this investigation. Initially, a classic Simulation-Based Design (SBD) system including an optimizer of particle swarm optimization (PSO) algorithm, a multi-level Free-Form-Deformation modeler and a Star CCM + solver was established; secondly, a fully-connected neural network surrogate model, whose input is a 4108 geometric feature tensor and output is total resistance coefficient C T , was constructed; finally, a self-adaptive mechanism switching between CFD solver and surrogate model by the dynamic error of inspected samples was plugged into the framework. According to the result, the hybrid framework optimized the C T of a scaled 5415 DTBM model with 6.683% at Fr = 0.28. The adaptive mechanism reduced computational cost from 1638 min to 456 min, which improved 72.16% optimization efficiency by activating the surrogate model instead CFD solver while the dynamic errors are below the criteria. The proposed self-adaptive mechanism could dynamically re-balance the efficiency and precision of the framework, and provide theoretical and technical support for ship design optimization. • Propose a ship design optimization technique based on online-trained adaptive neural network surrogate model for Simulation-Based Design (SBD). • Extracting a tensor of 4108 ship feature attributes as input to enhance the dimensionality of the neural network surrogate model. • Introduce a neural network adaptive training mechanism to enhance the predictive accuracy and optimization efficiency of the neural network surrogate model.