Aquatic–aerial propeller optimization based on a multi-fidelity neural network and transfer learning

The propeller design for multi-rotor hybrid aquatic–aerial vehicles is faced with a critical trade-off between aerial and aquatic efficiency. To address this challenge, a surrogate model based on multi-fidelity neural networks and transfer learning is proposed to predict aerodynamic and hydrodynamic performance (thrust and torque coefficients) of propellers. The chord length and the pitch angle distribution functions, each governed by three planform variables, are defined to determine blade geometry. Low-fidelity samples and high-fidelity samples derive from blade element momentum theory and computational fluid dynamics simulations, respectively. The non-dominated sorting genetic algorithm II seeks Pareto-optimal solutions through multi-objective optimization. During optimization, thrust and torque constraints are imposed. The optimization seeks to maximize aquatic efficiency without compromising aerial efficiency. The Quantitative analysis compares the aerodynamic and hydrodynamic performance of baseline vs optimized propellers. The optimized propeller achieves substantially higher aquatic thrust and efficiency while maintaining modest aerial thrust and efficiency improvements compared to the baseline propeller, t. Specifically, aerial thrust increases from 0.1198 to 0.1205, with efficiency rising from 17.08% to 17.18%. Aquatic thrust coefficient increases from 0.0372 to 0.0424, with efficiency rising from 63.25% to 69.43%.