Chamber shape optimization for ultra-high-pressure water-jet nozzle based on computational fluid dynamics method and a data-driven surrogate model

The ultra-high-pressure (UHP) water-jet nozzle acts as one of the key units for the whole rotary sprayers, and its chamber shape plays a decisive role in improving the hydrodynamic performance of water jetting. Unfortunately, comprehensive and effective methods to optimize the nozzles’ chamber shape are still lacking. By coupling a data-driven surrogate model with metaheuristic optimizer, a computational fluid dynamics (CFD)-based optimization scheme aiming at fully enhancing the hydrodynamic performance of UHP nozzles was proposed in this paper. Firstly, to ensure optimization accuracy, an improved whale optimization algorithm (IWOA) was developed. It combines the chaotic opposition-based learning (COBL) and the Cauchy-Gaussian mutation simulated annealing algorithms, incorporating a nonlinear convergence and adaptive inertia weight mechanism. Then, to reduce CFD computational costs and accelerate calculation speed, a data-driven surrogate model based on IWOA-support vector machine (SVM) was proposed. Herein, the parameter gamma and the penalty coefficient in SVM model, are trained using IWOA. The IWOA-SVM model was constructed using optimal Latin hypercube design (Opt. LHD) to sample nozzle structures, with peak wall shear stress (as objective function) calculated via CFD method. Finally, this optimization scheme was applied to optimize the chamber shape of the original nozzle commonly used in ship rust removal. The results reveal that the chamber shape of the optimized nozzle can greatly enhance the water-jet hydrodynamic performance by raising the peak wall shear stress by 13.88%. Additionally, the IWOA-SVM model demonstrates the capability to evaluate hydrodynamic performance with a maximum error of 2.853%. Therefore, the proposed optimization scheme provides novel insights for the overall design and optimization of the UHP water-jet nozzle.