Transient flows on an evenly heated wall with a fin

Ma, Jia; Nie, Bingchuan; Xu, Feng

doi:10.1016/j.ijheatmasstransfer.2017.10.117

Cited by 15 publications

(4 citation statements)

References 46 publications

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“…Lin & Armfield 2012; Ma et al. 2018), the first term at the right-hand side of (3.3), (3.14), (3.19) and (3.27) is the corresponding thickness of the TBL adjacent to a flat vertical wall; that is, the first term represents the basic solution of the thickness of the TBL adjacent to the flat vertical wall, but the second term describes the curvature effect.…”

Section: Discussion Of Curvature Effectmentioning

confidence: 99%

“…For comparison, the scales of the TBL of the fluid with the fixed Prandtl number adjacent to a flat vertical wall of height H and width 2 R flattened from the pipe are shown in the non-dimensional form with subscript p in table 1 based on the studies by Lin & Armfield (2012) and Ma et al. (2018). Further, δ T , δ Ts , δ q and δ qs are written as the product of the basic solution for the flat vertical wall and the corresponding curvature coefficients η T , η q , η Ts and η qs obtained by solving (3.3), (3.14), (3.19) and (3.27), respectively…”

Section: Discussion Of Curvature Effectmentioning

confidence: 99%

“…Further, we have a scaling relation between the heat flux q and temperature difference Δ T for the isoflux TBL (also see Ma, Nie & Xu 2018), …”

Section: Analysis Of Tblmentioning

confidence: 99%

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Effect of curvature on transient natural convection in a vertical circular pipe

Nie

2022

J. Fluid Mech.

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Natural convection adjacent to a curved vertical wall is widely present. Unfortunately, the effect of curvature on the transient thermal boundary layer (TBL) adjacent to the concave vertical wall has been neglected. In this study, dynamical evolution and thermal process of transient natural convection in a vertical circular pipe are discussed using scaling analysis, a boundary flow regime for the thin TBL without merging and a duct flow regime for the TBL with merging at the axis of the pipe are distinguished. The scaling laws quantifying the dependence of thickness, velocity and flow rate of the TBL of the fluid with the fixed Prandtl number in the vertical pipe on the Rayleigh number (RaT and Raq) and the ratio of height to radius of the pipe (A) are first reported for the isothermal and isoflux conditions. The curvature effect becomes stronger with the increase of the thickness of the TBL. Under the duct flow regime, the non-dimensional flow rate is scaled with $Ra_T^{1/2}{A^{ - 1}}$ for the isothermal condition and with $Ra_q^{1/2}{A^{ - 3/2}}$ for the isoflux condition. The scaling laws of the thickness, velocity and the flow rate of the TBL in the vertical pipe are validated based on the numerical results from direct numerical simulation (DNS) with good precision. The scaling coefficient is also presented under different regimes and conditions, which can serve as a design guide to determine natural convection in the vertical circular pipe.

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Section: Discussion Of Curvature Effectmentioning

confidence: 99%

Section: Discussion Of Curvature Effectmentioning

confidence: 99%

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Effect of curvature on transient natural convection in a vertical circular pipe

Nie

2022

J. Fluid Mech.

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“…The density of applied nanofluids ρ n is calculated as: where φ denotes the volume fraction of nanofluids, ρ b and ρ p represent the density of base fluid water and nanoparticles Al 2 O 3 , respectively. The specific heat capacity 54 of applied nanofluids Cp n is calculated as: where and represent the specific heat capacity of base fluid water and nanoparticles Al 2 O 3 , respectively. The thermal conductivity 55 of nanofluids λ n is defined as: where λ b and λ p represent the specific heat capacity of base fluid water and nanoparticles Al 2 O 3 , and is shown as below: …”

Section: Methodsmentioning

confidence: 99%

Thermal fluid fields reconstruction for nanofluids convection based on physics-informed deep learning

Liu

Xie

2022

Sci Rep

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Based on physics-informed deep learning method, the deep learning model is proposed for thermal fluid fields reconstruction. This method applied fully-connected layers to establish the mapping function from design variables and space coordinates to physical fields of interest, and then the performance characteristics Nusselt number Nu and Fanning friction factor f can be calculated from the reconstructed fields. Compared with reconstruction model based on convolutional neural network, the improved model shows no constrains on mesh generation and it improves the physical interpretability by introducing conservation laws in loss functions. To validate this method, the forced convection of the water-Al2O3 nanofluids is utilized to construct training dataset. As shown in this paper, this deep neural network can reconstruct the physical fields and consequently the performance characteristics accurately. In the comparisons with other classical machine learning methods, our reconstruction model is superior for predicting performance characteristics. In addition to the effect of training size on prediction power, the extrapolation performance (an important but rarely investigated issue) for important design parameters are also explored on unseen testing datasets.

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Scaling laws of buoyant flows on a suddenly heated horizontal plate

Jiang

Nie

2019

International Communications in Heat and Mass Transfer

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Transient flows on an evenly heated wall with a fin

Cited by 15 publications

References 46 publications

Effect of curvature on transient natural convection in a vertical circular pipe

Effect of curvature on transient natural convection in a vertical circular pipe

Thermal fluid fields reconstruction for nanofluids convection based on physics-informed deep learning

Scaling laws of buoyant flows on a suddenly heated horizontal plate

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