Corresponding state principle based correlation for the thermal conductivity of saturated refrigerants liquids from Ttr to 0.90Tc

Yang, Shangguo; Tian, Jianxiang; Jiang, Hua

doi:10.1016/j.fluid.2020.112459

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…The method proposed by Bridgman is based on the theoretical calculation considering molecular motion and interaction, but there is not both enough accuracy and simple expression. ,, Several methods use group contribution theory. ,− Moreover, some empirical and semiempirical methods such as corresponding-state principle and multiparameter correlation have also been used to predict the thermal conductivity. − …”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

2022

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In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to correlate and predict the thermal conductivity from the triple point temperature up to 0.98 times critical temperature (T c) for 23 refrigerants and 11 n-alkanes. It requires the temperature (T) as well as the molecular mass (M), acentric factor (ω), critical temperature, and critical pressure (P c) as input variables. The optimal ANN model is obtained by a trial-and-error procedure and consists of the input layer and the output layer together with one hidden layer with seven neurons. This ANN model can not only correlate the thermal conductivity but also accurately predict the thermal conductivity of refrigerants and n-alkanes. The correlation coefficients (R) in the training and testing phases are 0.9994 and 0.9993, respectively. Furthermore, the average absolute deviation (AAD) values are less than 1% for 14 out of 34 fluids, less than 2% for 28 fluids, and less than 4.5% for all the considered fluids.

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Section: Introductionmentioning

confidence: 99%

“…1 , 5 − 8 Moreover, some empirical and semiempirical methods such as corresponding-state principle and multiparameter correlation have also been used to predict the thermal conductivity. 9 − 18 …”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

2022

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“…Since the thermal conductivity of liquid refrigerants currently cannot be accurately estimated using rigorous theory [8], various semi-empirical and empirical models for calculating their liquid λ were developed. Some of the main models are as follows: extended corresponding states models used in REFPROP 10.0 [9]; models based on the entropy-scaling concept [10][11][12][13]; models based on equations of state [14,15]; semi-empirical correlations that describe the λ dependence on temperature [16][17][18]; and empirical equations that present specific fixed parameters [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Some literature models have been specifically developed to describe the liquid thermal conductivity of low-GWP refrigerants, such as HFOs and HCFOs [13,16,22,23,26]. For example, modified versions of the Di Nicola et al [19] correlation and the Latini and Sotte [18] correlation to calculate their liquid thermal conductivity dependence on temperature and pressure were proposed by Tomassetti et al [22]. The modified Di Nicola et al [21] correlation has the following expression:…”

Section: Introductionmentioning

confidence: 99%

Modeling Liquid Thermal Conductivity of Low-GWP Refrigerants Using Neural Networks

2022

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The thermal conductivity of refrigerants is needed to optimize and design the main components of HVAC&R systems. Consequently, it is crucial to have reliable models that are able to accurately calculate the temperature and pressure dependence of the thermal conductivity of refrigerants. For the first time, this study presents a neural network specifically developed to calculate the liquid thermal conductivity of various low-GWP-based refrigerants. In detail, a feed-forward network algorithm with 5 input parameters (i.e., the reduced temperature, the critical pressure, the acentric factor, the molecular weight, and the reduced pressure) and 1 hidden layer was applied to a large dataset of 3404 experimental points for 7 halogenated alkene refrigerants. The results provided by the neural network algorithm were very satisfactory, achieving an absolute average relative deviation of 0.389% with a maximum absolute relative deviation of 6.074% over the entire dataset. In addition, the neural network ensured lower deviations between the experimental and calculated data than that produced using different literature models, proving its accuracy for the liquid thermal conductivity of the studied refrigerants.

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Thermal Conductivity of Liquid Hydrofluorochlorine Olefins in the Range from Saturation Line to Criterial Pressure

Kudryavtseva,

Rykov,

Rykov

2024

J Eng Phys Thermophy

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Corresponding state principle based correlation for the thermal conductivity of saturated refrigerants liquids from Ttr to 0.90Tc

Cited by 15 publications

References 26 publications

Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

Modeling Liquid Thermal Conductivity of Low-GWP Refrigerants Using Neural Networks

Thermal Conductivity of Liquid Hydrofluorochlorine Olefins in the Range from Saturation Line to Criterial Pressure

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