This article reports the effect of double vortex-chambers with multiple inlet snail entries of N = 1, 4 and 6 nozzles on the energy separation referred to cold gas exit temperature difference (ΔT_c) in a counter-flow vortex tube type. The experimental work focused on ascertaining the effects of entry air pressure (P_i = 2, 3 and 4 bar), distance ratios between the two vortex-chamber to the vortex tube diameter (l/D = 0.875–1.125) and the cold gas mass ratio (μ_c) in a vortex tube. It was found that cold gas exit temperature difference (ΔT_c) increased with increasing inlet air pressure (P_i) and number of inlet nozzles (N), and decreasing l/D. Among the studied conditions, the double vortex-chamber operated at the highest P_i of 4 bar, N = 6, the smallest l/D = 0.875 and μ_c = 0.38 gave the highest cold gas exit temperature difference (ΔT_c) of 31.5 °C. In addition, the deep learning optimization technique was also developed to predict the temperatures for different combination of parameters used in this study. It was found that the optimal models provide maximum R² value of 0.99317.

本文报告了具有N  = 1、4 和 6 个喷嘴的多个入口蜗牛入口的双涡流室对逆流涡流管类型中冷气出口温度差 (Δ T _c )的能量分离的影响。实验工作的重点是确定进气压力（P _i  = 2、3 和 4 bar）、两个涡流室与涡流管直径之间的距离比（l/D  = 0.875–1.125）和冷气体的影响涡流管中的质量比 ( μ _c )。发现冷气出口温差（Δ T _c）随着进气压力（P _i）的增加而增加) 和入口喷嘴的数量 ( N )，并降低l/D。在研究的条件中，双涡室在4 bar的最高P _i下运行，N  = 6，最小的l/D  = 0.875 和μ _c  = 0.38 给出了最高的冷气体出口温差 (Δ T _c ) 31.5°C。此外，还开发了深度学习优化技术来预测本研究中使用的不同参数组合的温度。发现最优模型提供了0.99317的最大R ²值。