Xiaoxian Yang

Accurate density measurements of strongly magnetic fluids using a single-sinker magnetic suspension densimeter (SSD) are often affected by force transmission errors (FTE). Based on the FTE correction assumptions of McLinden et al. (2007), this work introduces a multi-fluid simultaneous fitting method that eliminates linear parameter coupling in conventional sequential approaches. Using oxygen and nitrogen for calibration and argon and carbon dioxide for validation, the proposed method achieves expanded density uncertainty (k = 2) of ~0.1% for strongly magnetic fluids and 0.05% for weakly magnetic fluids. Across medium-to-high densities (> 30 kg·m–3), FTE correction reduces density deviations from 1.5% to < 0.05% for strongly magnetic fluids while preserving < 0.05% accuracy for weakly magnetic fluids. At low densities (< 30 kg·m–3), FTE correction suppresses deviations from 2% to 0.2%, which are subsequently constrained to < 0.1% via a dedicated low-density correction model. Compared with conventional approaches, the method suppresses parameter compensation and uncertainty inflation by decoupling balance calibration, apparatus, and fluid effects, thereby providing more stable correction across the experimental range and enabling reliable transfer of correction parameters between different fluids. This systematic correction and analysis provide a robust reference for magnetic-coupling calibrations.

The accurate measurements and modeling of thermophysical properties, including vapor pressure, viscosity, density, and thermal conductivity, of refrigerant–lubricant mixtures are crucial. This study presents measurements for binary and ternary mixtures involving the pure refrigerants R-290, RE-170, R-744, and R-152a as well as blends of R-290/RE-170 and R-744/R-152a combined with the lubricants POE VG68 and POE VG100 for vapor pressure, thermal conductivity, density, and viscosity and MO VG10 with R-290, RE-170 and their mixture just for thermal conductivity in a temperature range between 238 and 353 K and up to 6.0 MPa. The measured properties are compared against empirical and semiphysical modeling approaches. The semiphysical model of [Yang, X.; et al.Ind. Eng. Chem. Res.2023, 62(44), 18736–18749]. requires only 12 experimental data points for property description. Empirical models used are the Cavestri equation (vapor pressure), the Henderson equation (density/viscosity), and the power law/Filippov equations (thermal conductivity). Results indicate that predictions for data points above the critical point are inconclusive across all models. For other measurement points, the deviation magnitude is similar. However, for these measurement data, the semiphysical model of [ Yang, X.; et al.Ind. Eng. Chem. Res.2023, 62(44), 18736–18749].provides a better fit for vapor pressure, while the empirical models offer a superior fit for viscosity.

Dilute-gas viscosity is a fundamental transport property governed by binary molecular collisions and is widely used as a reference term in dense-fluid viscosity models. In this work, a physics-constrained symbolic regression framework is developed for predicting dilute-gas viscosity through the conventional Chapman–Enskog framework and a new reduced Boyle-scaled collision integral. Instead of correlating viscosity directly, the viscosity data are transformed into a dimensionless collision integral using the Boyle temperature and Boyle length derived from the second virial coefficient. A comprehensive database containing 87 fluids and 11,263 dilute-gas viscosity data points was constructed by combining high-accuracy ab initio data with carefully screened and zero-density-extrapolated experimental data. The collision-integral model was formulated as a decomposed correlation consisting of a spherical reference term, a nonpolar correction term, and a polar correction term, with additional empirical extensions for quantum and associating fluids. Symbolic regression was used to identify the analytical expression of each term considering accuracy, numerical stability, and physical consistency. The resulting model gives an overall AARD of 2.00 % for the complete database and maintains good accuracy across different fluid classes. Analysis of the final expressions shows that the dominant temperature dependence is captured by the spherical reference term, while molecular nonsphericity mainly acts through a coupled acentric factor-temperature effect. The polar correction provides a coarse but systematic empirical adjustment for polar fluids. To facilitate reproducible use, a Python implementation of the final correlation is provided in the Supporting Information.

A triple-port automatic high-pressure volumetric sorption analyzer was set up for sorption measurements over the temperature range of (273.15–368.15) K and pressures up to 10 MPa. The main function of this apparatus is to rapidly screen porous materials for the separation of a specific gas mixture by measuring adsorption isotherms. Measurements with empty sample containers were carried out to evaluate potential systematic errors. Validation measurements on commonly studied cases (CO₂ adsorption on Zeolite 4A, ZIF-8, and Zeolite 13X) were performed, and the excellent agreement with results of literature indicates the high reliability of the measurement system. A comprehensive uncertainty analysis was carried out to determine the uncertainty of measurement results and to propose future improvements of the system. Later, the measurement system was applied to screen suitable materials for the separation of refrigerant blends, which are composed of six pure refrigerants (R-32, R-125, R-1234yf, R-134a, R-1234ze(E), and R-290). The studied porous materials include six metal–organic framework samples, five zeolite samples, and one activated carbon sample. The results show that all zeolite samples investigated (Köstrolith 3AK, 3ABFK, 3ABFK(HSD), 4AK, and 4ABFK) exhibit a higher adsorption capacity for R-32 than for the other five refrigerants, with Köstrolith 4ABFK showing the highest selectivity. This makes it possible to use any of these zeolite samples to separate R-32 from the R-410A blend (a mixture of R-32 + R-125, to be phased down in the European Union) or even separate R-32 from the mixtures composed of any of these six refrigerants. The other studied porous samples do not yield any promising results for the refrigerant blend separation.

Thermal conductivity is a key thermophysical parameter in the energy transfer process and is important for the design and optimization of heat transfer equipment. This work monotonically linearizes the logarithm of the reduced thermal conductivity with respect to the reduced density of 95 pure fluids and 66 mixtures (including ternary and quintuple mixtures) based on hidden scale invariance. Quasiuniversality is used to make the images of different fluids as a function of the reduced thermal conductivity vs the reduced density coincide. A generalized predictive model for the liquid thermal conductivity is constructed. The average absolute relative deviation of the 26 327 pure fluid thermal conductivity experimental data is 3.6%, and the average absolute relative deviation of the 7033 mixtures experimental data points is 4.8%. In addition, the critical enhancement of thermal conductivity is considered. The new model contains only four global parameters, no specific fitting parameters for pure fluids and no interaction parameters for mixtures.