Heat transfer and pressure drop of refrigerant R404A at near-critical and supercritical pressures

Jiang, Yirong

doi:10.31274/rtd-180813-8811

Cited by 2 publications

(2 citation statements)

References 51 publications

(169 reference statements)

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“…Friction factor also increases for an increase in mass flux and temperature [38,135,39,86]. For higher pressures, the pressure drop decreases (but this effect is less pronounced in the liquid-like region and due to the temperature dependence of the thermophysical properties as viscosity and density increase for rising pressure) [82,38,39,86]. The influence of the diameter is not clear, as contradictory results are reported [15].…”

Section: Conclusion On Influencing Parametersmentioning

confidence: 97%

“…An overview is listed in Table 3. In 2004, the PhD dissertation of Jiang [82] was published about the heat transfer and pressure drop of R404A (a blend of R125, R134a and R143a) at near-critical and supercritical pressures for its application in vapor compression cycles (VCCs). A flow regime-based heat transfer model was developed which could predict 73% of the data within ±15%.…”

Section: Cooling Of Horizontal Flowmentioning

confidence: 99%

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Current status of the thermohydraulic behavior of supercritical refrigerants: A review

Nieuwenhuyse

Lecompte

Paepe

2023

Applied Thermal Engineering

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Section: Conclusion On Influencing Parametersmentioning

confidence: 97%

Section: Cooling Of Horizontal Flowmentioning

confidence: 99%

Current status of the thermohydraulic behavior of supercritical refrigerants: A review

Nieuwenhuyse

Lecompte

Paepe

2023

Applied Thermal Engineering

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Machine learning-based models for frictional pressure drop prediction of condensing and adiabatic flow in micro, mini and macro channels utilizing universal data

Shourehdeli

Mobini

2023

Int. J. Air-Cond. Ref.

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This study proposes a universal machine learning-based model to predict the adiabatic and condensing frictional pressure drop. For developing the proposed model, 11,411 data points of adiabatic and condensing flow inside micro, mini and macro channels are collected from 80 sources. The database consists of 24 working fluids, hydraulic diameters from 0.07 to 18 mm, mass velocities from 6.3 to 2000 Kg/m2s, and reduced pressures from 0.001 to 0.95. Using this database, four machine learning regression models, including “artificial neural network”, “support vector regression”, “gradient boosted regression”, and “random forest regression”, are developed and compared with each other. A wide range of dimensionless parameters as features, “two-phase friction factor” and “Chisholm parameter” are each considered separately as targets. Using search methods, the optimal values of important hyperparameters in each model are determined. The results showed that the “gradient boosted regression” model performs better than other models and predicts the frictional pressure drop with a mean absolute relative deviation of 3.24%. Examining the effectiveness of the new model showed that it predicts data with uniform accuracy over a vast range of variations of each flow parameter.

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Heat transfer and pressure drop of refrigerant R404A at near-critical and supercritical pressures

Cited by 2 publications

References 51 publications

Current status of the thermohydraulic behavior of supercritical refrigerants: A review

Current status of the thermohydraulic behavior of supercritical refrigerants: A review

Machine learning-based models for frictional pressure drop prediction of condensing and adiabatic flow in micro, mini and macro channels utilizing universal data

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