In this study, the performance pattern of air-driven two parallel-connected Ranque-Hilsch vortex tubes (RHVT) by using artificial neural network (ANN) is considered. Different parameters such as vortex tube inlet parameters, type of working fluid, nozzle material, and nozzle number affect the temperature separation in vortex tubes. In this context, overall temperature difference (ΔT), which is also known as the effectivity indicator of vortex tubes, was modeled according to the aforementioned parameters which were obtained from experiments. A novel framework is presented to make the ANN model generalizable and robust. The ΔT quantity was selected as an output parameter and obtained with the well-trained ANN structure according to nozzle material (thermal conductivity), nozzle number, and inlet pressure. The coefficient of determination (R²), post error ratio (C), and the mean absolute percentage error (MAPE) of the proposed ANN model have been calculated as 0.9878, 0.19, and 0.0671, respectively. To model an experimental process, shorten the time, and save costs, a decision-support system was designed with three types of input parameters that are heat transfer coefficient of nozzle material, inlet pressure, and nozzle number. Thus, the system easily calculating the ΔT value by the generalizable and robust ANN model, which is the first trial for a parallel-connected system allowing the decision-maker to use different parameter values and different materials, is constituted.

中文翻译：

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广义鲁棒人工神经网络对并联的RANCH-HILSCH涡管的性能建模

在这项研究中，考虑了使用人工神经网络（ANN）的空气驱动两个并联Ranque-Hilsch涡流管（RHVT）的性能模式。诸如涡流管入口参数，工作流体的类型，喷嘴材料和喷嘴数量之类的不同参数会影响涡流管中的温度分离。在这种情况下，总温差（ΔT），也称为涡流管的效率指标，是根据从实验中获得的上述参数进行建模的。提出了一种新颖的框架来使ANN模型具有通用性和鲁棒性。该ΔT选择数量作为输出参数，并根据喷嘴材料（导热率），喷嘴数量和入口压力采用训练有素的ANN结构获得。所提出的ANN模型的确定系数（R ²），误差后比率（C）和平均绝对百分比误差（MAPE）分别计算为0.9878、0.19和0.0671。为了模拟实验过程，缩短时间并节省成本，设计了决策支持系统，该系统具有三种输入参数，即喷嘴材料的传热系数，入口压力和喷嘴数量。因此，系统很容易计算出ΔT 通过可扩展且健壮的ANN模型构建值，该模型是允许决策者使用不同参数值和不同材料的并行连接系统的第一个试验。