Numerical analysis and artificial neural network-based prediction of two-phase flow pressure drop of refrigerants in T-junction

Zhi, Changshuang; Zhang, Yixiao; Zhu, Chao; Liu, Yingwen

doi:10.1016/j.ijrefrig.2022.02.005

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Recent advancements in soft computing methods motivated many scholars to use this approach as a promising tool to handle highly complex, non-linear and dynamic problems for numerous critical industrial applications (Yan et al, 2018;Zhi et al, 2022). They are also known as artificial intelligence (AI), Machine Learning (ML) or datadriven techniques.…”

Section: Soft Computing Approachmentioning

confidence: 99%

Two-Phase Flow Pressure Drop Modelling in Horizontal Pipes with Different Diameters

Faraji¹,

Santim²,

Chong³

et al. 2022

SSRN Journal

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Section: Soft Computing Approachmentioning

confidence: 99%

Two-Phase Flow Pressure Drop Modelling in Horizontal Pipes with Different Diameters

Faraji¹,

Santim²,

Chong³

et al. 2022

SSRN Journal

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“…Subsequent work has found that the pressure drops in bends change with the fow motion state and the geometry of the bend. A new T-junction pressure drop prediction model using a BP neural network (BPNN) was developed by Zhi et al [15], which difers from the traditional prediction model based on theoretical analysis. Furthermore, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm were combined with BPNN to optimize the weights and biases of BPNN.…”

Section: Introductionmentioning

confidence: 99%

Local Resistances of Gas–Liquid Two-Phase Flows in Vertical L-Shaped and Z-Shaped Pipes

Wang

Liao

2022

International Journal of Chemical Engineering

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In this paper, a systematic numerical study of the local resistance coefficients of vertical L-shaped and Z-shaped pipes for gas‒liquid two-phase flows under vertical conditions was carried out using a realizable k-ε turbulence model combined with a mixture model in Fluent software. Specifically, the influence of the Reynolds number Rel, the gas-phase volume rate α, the radius–diameter ratio R/D, the height–diameter ratio H/D, and the two-phase flow direction on the local resistance coefficient ξ were discussed in detail. ξ of the vertical Z-shaped pipe decreases with increasing Rel, while ξ of the vertical L-shaped pipe does not change significantly. In a specific range, ξ of vertical L-shaped and Z-shaped pipes increases with increasing α and decreases with increasing R/D. In Z-shaped pipes, under the upward flow condition, ξ increases with increasing H/D, and under the downward flow and horizontal flow conditions, ξ first decreases and then increases with increasing H/D. Overall, upward and downward flow conditions have a larger ξ than the horizontal flow condition. When H/D is larger than 14, ξ tends to be stable under all three flow conditions. Finally, the relationship equations between ξ and Rel, α, R/D, and H/D were fitted, which agreed with the numerical results.

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Separation mechanism of T-junction in thermodynamic systems: A review and case verification

Zhao²,

Zhao³

et al. 2022

Applied Thermal Engineering

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Numerical analysis and artificial neural network-based prediction of two-phase flow pressure drop of refrigerants in T-junction

Cited by 8 publications

References 22 publications

Two-Phase Flow Pressure Drop Modelling in Horizontal Pipes with Different Diameters

Two-Phase Flow Pressure Drop Modelling in Horizontal Pipes with Different Diameters

Local Resistances of Gas–Liquid Two-Phase Flows in Vertical L-Shaped and Z-Shaped Pipes

Separation mechanism of T-junction in thermodynamic systems: A review and case verification

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