Data-driven optimization of nose profiles for water entry impact load reduction

The prediction and reduction of impact loads during water entry are critical in various engineering applications, such as ship slamming and seaplane landings. Traditional methods, including theoretical models, computational fluid dynamics (CFD), and experimental approaches, often suffer from limited applicability, lengthy processing times, and high costs. To address these challenges, this study explores the use of Deep Neural Networks (DNNs) for predicting peak impact loads during the oblique water entry of cylinders with different nose profiles. Optimized Latin Hypercube Sampling (OLHS) and Sobol sequences are employed to generate dataset inputs from a 7-dimensional parameter space that governs impact dynamics. These inputs are then fed into OpenFOAM solvers to compute the peak impact accelerations, serving as dataset outputs for training and testing the DNN. Experiments are conducted to validate the numerical method, confirming that the resulting dataset outputs can be considered as ground truth. With Bayesian hyperparameter optimization, the well-trained DNN model demonstrates reliable accuracy in predicting peak axial and normal accelerations. Further, this study integrates the DNN model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the nose profiles, resulting in a maximum weighted reduction of 48% in the combined axial and normal accelerations. •Deep Neural Networks (DNNs) are utilized for the adaptive, real-time, and cost-effective prediction of impact loads during cylinder water entry.•The DNN model demonstrates reliable accuracy in predicting the impact loads within high-dimensional parameter spaces.•Significant reductions in impact loads are achieved by integrating the DNN model into multi-objective optimization algorithms.