Densities and isothermal compressibilities of several nanofluids were modelled using a perturbed hard-chain equation of state (EoS) by an attractive term from Yukawa tail in 273–363 K range and pressure up to 45 MPa. The nanofluids of interest comprise TiO2-Anatase (-A), TiO2-Rutile (-R), SnO2, Co3O4, CuO, ZnO, and Al2O3 as nanoparticles dispersed in ethylene glycol, water, poly ethylene glycol, ethylene glycol + water, and poly ethylene glycol + water as base fluids. The EoS was capable of estimating 1397 density data of 9 nanofluids with the overall average absolute deviations (AAD) of 0.90%. The coefficients of isothermal compressibility of 6 selected nanofluids were also predicted using the EoS with the AAD of 5.74% for 1095 data points examined. The PHDC EoS was not capable of estimating the excess volumes of 3 selected EG-, PEG-, and water-based nanofluids accurately as the relative deviations from the literature data were greater than 34%, even though the trend of results against the nanoparticle concentration was in accord with the literature. To further investigate the density prediction, we have trained a neural network with a single hidden layer and 17 neurons which was able to predict the densities of nanofluids accurately.

中文翻译：

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扰动的硬二聚体链状态方程的密度和等温压缩率：在纳米流体中的应用

在 273–363 K 范围和高达 45 MPa 的压力下，使用来自 Yukawa 尾巴的一个有吸引力的项，使用扰动的硬链状态方程 (EoS) 对几种纳米流体的密度和等温压缩率进行建模。感兴趣的纳米流体包括 TiO2个-锐钛矿 (-A), TiO2个-金红石 (-R)、SnO2、Co3个欧4个, CuO, ZnO, 和 Al2个欧3个作为分散在乙二醇、水、聚乙二醇、乙二醇+水和聚乙二醇+水作为基液中的纳米粒子。EoS 能够估计 9 种纳米流体的 1397 个密度数据，总体平均绝对偏差 (AAD) 为 0.90%。还使用 EoS 预测了 6 种选定纳米流体的等温压缩系数，其中 AAD 为 5.74%，检查了 1095 个数据点。PHDC EoS 无法准确估计 3 种选定的 EG-、PEG- 和水基纳米流体的过量体积，因为与文献数据的相对偏差大于 34%，即使结果趋势与纳米粒子浓度相反与文献一致。为了进一步研究密度预测，