Multi-Field Coupling and Data-Driven Based Optimization of Elliptical Nozzle Cooling

During the three-roll planetary rolling process, the cooling efficiency of conventional nozzle structures is insufficient, which can easily lead to copper adhesion on the roll surface, product quality degradation, and shortened roll lifespan, thereby limiting both the quality of copper tubes and overall production efficiency. To enhance the performance of the cooling system, this study proposes a novel elliptical nozzle structure and develops a multiphysics coupled model to reveal the effects of nozzle inclination angle and gas–liquid pressure ratio on cooling behavior. An independently constructed experimental platform was used to measure jet flow patterns and the surface temperature of alloy steel plates under various parameter conditions, thereby validating the accuracy and reliability of the numerical model. The results indicate that, under the same effective outlet area, the elliptical nozzle significantly increases jet exit velocity and overall cooling efficiency. To address the issues of high computational cost and low efficiency during optimization using finite element simulations, a high-accuracy surrogate model based on a Random Forest (RF) algorithm was introduced, and the geometric parameters of the nozzle were globally optimized using a Particle Swarm Optimization (PSO) algorithm. Ultimately, the combined RF-PSO strategy improved the average heat transfer coefficient by 55.57%, markedly enhancing the roll cooling performance and providing a solid theoretical basis and methodological reference for high-performance cooling system design and precision copper tube manufacturing.