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首页> 外文期刊>International Journal of Refrigeration >Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system
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Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system

机译：自适应神经模糊推理系统方法预测R-134A / LPG制冷剂的质量流量，用于蒸汽压缩制冷系统中的直螺旋卷材绝热毛细管

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This study deals with predicting the mass flow rate of R-134a/LPG as refrigerant inside a straight and helical coiled adiabatic capillary tube of vapor compression refrigeration system by combining dimensionless analysis and Adaptive Neuro-Fuzzy Inference System techniques. For this purpose the experimental system was designed and tested under steady state conditions, by changing the length of the capillary tube, the inner diameter of the capillary tube, the coil diameter and the degree of subcooling of the refrigerant at the capillary tube inlet. Dimensional analysis was utilized to provide generalized dimensionless parameters and to reduce the number of input parameters, while Adaptive Neuro-Fuzzy Inference System was applied as a generalized approximator of the nonlinear multi-input and single-output function. The comparison of the absolute fraction of variance (R-2) (0.998 and 0.961), the root mean square error (RMSE) (0.105 kg/h and 0.489 kg/h) and the mean absolute percentage error (MAPE) (0.954% and 4.75%) demonstrated the result for combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System and dimensionless correlation model predictions respectively. The results indicated that the combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System gave the best statistical prediction efficiency (C) 2017 Elsevier Ltd and IIR. All rights reserved.
机译：该研究通过组合无量纲分析和自适应神经模糊推理系统技术，预测R-134a / LPG作为制冷剂的r-134a / lpg作为制冷剂的质量流速。为此目的，通过改变毛细管的长度，毛细管的内径，线圈直径和制冷剂在毛细管入口处的制冷剂的过冷度下设计和测试实验系统。尺寸分析用于提供广义无量纲参数并减少输入参数的数量，而自适应神经模糊推理系统被应用为非线性多输入和单输出功能的广义近似器。方差绝对分数（R-2）（0.998和0.961）的比较，根均方误差（RMSE）（0.105kg / h和0.489 kg / h）和平均绝对百分比误差（mape）（0.954％ 4.75％）证明了尺寸分析和自适应神经模糊推理系统的组合和自适应神经模糊推理系统的结合和无量纲相关模型预测。结果表明，尺寸分析和自适应神经模糊推理系统的组合给出了最佳统计预测效率（C）2017 Elsevier Ltd和IIR。版权所有。

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