Li Zhuo, Duan Yuanyuan, Yang Xiaoxian
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Beijing Key Laboratory for CO2 Utilization and Reduction Technology, Tsinghua University, Beijing 100084,People's Republic of China.
Southwest United Graduate School, Kunming 650092, People's Republic of China.
Ind Eng Chem Res. 2024 Oct 15;63(42):18160-18175. doi: 10.1021/acs.iecr.4c02946. eCollection 2024 Oct 23.
In recent years, the application of the residual entropy scaling (RES) method for modeling transport properties has become increasingly prominent. Based on Yang et al. (. , , 13052) in modeling the thermal conductivity of refrigerants, we present here an RES model that extends Yang et al.'s approach to a wider range of pure fluids and their mixtures. All fluids available in the REFPROP 10.0 software, i.e., those with reference equations of state (EoS), were studied. A total of 71,554 experimental data of 125 pure fluids and 16,702 experimental data of 164 mixtures were collected from approximately 647 references, mainly based on the NIST ThermoData Engine (TDE) database 10.1. As a result, over 68.2% (corresponding to the standard deviation of a normal distribution) of the well-screened experimental data agree with the developed RES model within 3.1% and 4.6% for pure fluids and mixtures, respectively. Comparative analysis against the various models implemented in the REFPROP 10.0 (one of the state-of-the-art software packages for thermophysical property calculations) reveals that our RES model demonstrates analogous statistical agreement with experimental data yet with much fewer parameters. Regarding the average absolute value of the relative deviation (AARD) from experimental values to model predictions, the developed RES model shows a smaller or equal AARD for 74 pure fluids out of 125 and 76 mixtures out of 164. Besides, a detailed examination of the impact of the critical enhancement term on mixture calculations was conducted. To use our model easily, a software package written in Python is provided in the Supporting Information.
近年来,残余熵标度(RES)方法在传输性质建模中的应用日益突出。基于Yang等人(......,13052)在制冷剂热导率建模方面的研究,我们在此提出一种RES模型,该模型将Yang等人的方法扩展到更广泛的纯流体及其混合物。研究了REFPROP 10.0软件中所有可用的流体,即那些具有状态方程(EoS)参考方程的流体。主要基于NIST热数据引擎(TDE)数据库10.1,从大约647篇参考文献中收集了125种纯流体的71554个实验数据和164种混合物的16702个实验数据。结果,经过精心筛选的实验数据中,超过68.2%(对应正态分布的标准差)与所开发的RES模型相符,纯流体和混合物的偏差分别在3.1%和4.6%以内。与REFPROP 10.0(热物理性质计算的最先进软件包之一)中实现的各种模型进行对比分析表明,我们的RES模型与实验数据具有类似的统计一致性,但参数要少得多。关于从实验值到模型预测的相对偏差平均绝对值(AARD),所开发的RES模型在125种纯流体中有74种以及164种混合物中有76种显示出较小或相等的AARD。此外,还详细研究了临界增强项对混合物计算的影响。为了便于使用我们的模型,支持信息中提供了一个用Python编写的软件包。