A Neural Network approach to optimize flow rate for Micro-Bubble Drag Reduction

To reduce the resistance of ships, many scholars have focused on micro-bubble drag reduction (MBDR) technology. However, The implementation of MBDR on ships is challenging due to the numerous factors and complex coupling relationships involved in the gas–liquid two-phase flow at the ship’s bottom. This paper aims to apply neural network algorithm on MBDR technology to real ships to achieve energy savings and emission reductions. A test platform was constructed, and the Micro-Bubble Flow Rate of the test model at the lowest drag resistance was collected to form a sample database. Using the database as a sample, the mathematical model is trained by the neural network algorithm, and optimized by the genetic algorithm through a number of iterations, the Optimal Micro-Bubbles Flow Rate (OMFR) is predicted for each status. This paper is of great significance to the application of MBDR technology on real ships. Using neural network algorithm to form the mathematics model from the data samples of unconstrained self-propulsion tests. The OMFR obtained through the iterative process of the genetic algorithm, can reduce the ship resistance, thereby achieving energy savings and emission reductions. •Test platform identifies optimal flow rates for minimal drag and builds a database.•A MBDR model is developed using a neural network to minimize ship resistance.•The model is optimized using a genetic algorithm, closely matching the test results.