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Optimal Design of a Cold Spray Nozzle for Inner Wall Coating Fabrication by Combining CFD Simulation and Neural Networks
Journal of Thermal Spray Technology ( IF 3.1 ) Pub Date : 2024-01-22 , DOI: 10.1007/s11666-024-01716-4
Yuxian Meng , Hiroki Saito , Chrystelle Bernard , Yuji Ichikawa , Kazuhiro Ogawa

Recently, the low-pressure cold spray (LPCS) technique has been used to fabricate superhydrophobic polymer coatings on metallic substrates, suggesting a significant potential in engineering applications. This study aims to design a spiral LPCS nozzle to coat the pipe’s inner wall with superhydrophobic polymer. The design goal is to achieve the maximum particle velocity in a confined (limited) space, assuming that the powder can enter the feeding tube through the Venturi effect. Achieving these two goals simultaneously using only computational fluid dynamics (CFD) simulation is challenging. Therefore, the CFD simulation was combined with the neural network (NN) method to design the new spiral nozzle. During training, the effects of the NN models and algorithms were investigated. The results showed that the feedforwardnet model combined with the trainbr or trainlm algorithm (from MATLAB 2016b software), presented a minimal error for particle velocity or gas flux prediction, respectively. The trained NN correlates the nozzle parameters (i.e., mean coil diameter, spring lift angle, and expansion ratio) and its performances (i.e., particle velocity and gas flux in the powder feeding tube). As a result, the optimal spiral nozzle was determined based on the design goal of maximum particle velocity and suitable gas flux in the powder feeding tube. Furthermore, the effect of each nozzle parameter on the particle velocity and gas flux in the powder feeding tube was analyzed. The cold spray experiment confirmed that the designed spiral nozzle could fabricate Perfluoroalkoxy alkane (PFA) coatings.



中文翻译:

CFD 仿真与神经网络相结合的内壁涂层冷喷涂喷嘴优化设计

最近,低压冷喷涂(LPCS)技术已被用于在金属基材上制备超疏水聚合物涂层,这表明其在工程应用中具有巨大潜力。本研究旨在设计一种螺旋LPCS喷嘴,在管道内壁涂覆超疏水聚合物。设计目标是在受限(有限)空间内实现最大颗粒速度,假设粉末可以通过文丘里效应进入进料管。仅使用计算流体动力学 (CFD) 模拟同时实现这两个目标具有挑战性。因此,将CFD模拟与神经网络(NN)方法相结合来设计新型螺旋喷嘴。在训练过程中,研究了神经网络模型和算法的效果。结果表明,前馈网络模型与 trainbr 或 trainlm 算法(来自 MATLAB 2016b 软件)相结合,分别为粒子速度或气体通量预测提供了最小的误差。经过训练的神经网络将喷嘴参数(即平均线圈直径、弹簧升程角和膨胀比)与其性能(即送粉管中的颗粒速度和气体通量)相关联。根据送粉管内最大颗粒速度和合适气体流量的设计目标,确定了最佳螺旋喷嘴。进一步分析了各喷嘴参数对送粉管内颗粒速度和气体流量的影响。冷喷涂实验证实所设计的螺旋喷嘴可以制备全氟烷氧基烷烃(PFA)涂层。

更新日期:2024-01-24
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