计量学报2025，Vol.46Issue(4)：555-564,10.DOI:10.3969/j.issn.1000-1158.2025.04.13
预测性混合体系跨接比容平移SRK状态方程与计算性能评价
Predictive Mixture Crossover Volume-Translation SRK Equation of State and Computational Evaluation
摘要
Abstract
Due to the severe density fluctuations and singularity of properties in near-critical region,classical equations of state fail in critical description.The crossover method based on renormalization group theory can make the equations of state effectively describe near-critical thermodynamic properties.However,existing researches often rely on experimental data fitting,and the data of mixtures is much rarer than that of pure fluids.The applicability and predictive performance of these equations need to be evaluated.This work adopts Kiselev crossover method to establish a crossover volume-translation(VT)Soave-Redlich-Kwong(SRK)equation of state for pure fluid to improve the performance of classical equations in near-critical regions.The new equation does not rely on the correlation of experimental data and can achieve reliable prediction of natural fluid CO2 and refrigerant HFOs from the normal region to the near-critical region.On the basis of the equation of state for pure fluid,a crossover VTSRK equation of state with predictive performance was constructed for mixture systems.The computational performance of the crossover equation of state in critical parameters and pvTx properties was evaluated for CO2 and HFO mixtures.The average relative deviation of the predicted critical temperature was only 0.53％,and the predicted pvTx properties were within 1％deviation for most experimental points.The crossover VTSRK equation of state for mixtures shows good consistency with existing data and has predictive performance for non-associative fluid mixtures.关键词
热学计量/SRK状态方程/跨接方法/混合体系/临界轨迹/pvTx性质Key words
thermometrics/SRK equation of state/crossover method/mixture/critical locus/pvTx properties引用本文复制引用
汪尔奇,卿康,杨震,段远源..预测性混合体系跨接比容平移SRK状态方程与计算性能评价[J].计量学报,2025,46(4):555-564,10.基金项目
国家自然科学基金(U21B2056) （U21B2056）
云南省西南联合研究生院科技专项(202302AO370018) （202302AO370018）
