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Thermodynamic analysis of subcooling and superheating effects of alternative refrigerants for vapour compression refrigeration cycles

机译:蒸气压缩式制冷循环的替代制冷剂过冷和过热效应的热力学分析

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摘要

This paper presents a computer-based first law and exergy analysis applied to vapour compression refrigeration systems for determining subcooling and superheating effects of environmentally safe new refrigerants. Three refrigerants are considered: R134a, R407c and R410a. It is found that subcooling and superheating temperatures directly influence the system performance as both condenser and evaporator temperatures are affected. The thermodynamic properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Six ANNs were trained to predict various properties of the three refrigerants. The training and validation of the ANNs were performed with good accuracy. The correlation coefficient obtained when unknown data were used to the networks were found to be equal or very near to 1 which is very satisfactory. Additionally, the present methodology proved to be much better than the linear multiple regression analysis. From the analysis of the results it is found that condenser and evaporator temperatures have strong effects on coefficient of performance (COP) and system irreversibility. Also both subcooling and superheating affect the system performance. This effect is similar for R134a and R407c, and different for R410a.
机译:本文介绍了一种基于计算机的第一定律和火用分析,该分析应用于蒸气压缩制冷系统,用于确定环境安全的新制冷剂的过冷和过热效应。考虑了三种制冷剂:R134a,R407c和R410a。发现冷凝器和蒸发器温度都受到影响,过冷和过热温度直接影响系统性能。制冷剂的热力学性质是使用人工神经网络(ANN)方法制定的。对六种人工神经网络进行了训练,以预测三种制冷剂的各种特性。人工神经网络的训练和验证均具有良好的准确性。发现将未知数据用于网络时获得的相关系数等于或非常接近1,这非常令人满意。另外,本方法论证明比线性多元回归分析好得多。从结果分析中发现,冷凝器和蒸发器的温度对性能系数(COP)和系统不可逆性有很大影响。过冷和过热也会影响系统性能。 R134a和R407c的效果相似,R410a的效果不同。

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