Effect of temperature difference on the thermal mixing phenomenon in a T-junction under inflow pulsation

Su, Bo; Zhu, Ziliang; Wang, Xiaoyu; Huang, Ke; Mei, Lin; Wang, Qiuwang

doi:10.1016/j.nucengdes.2020.110611

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…In the nuclear power industry, the branch pipeline is usually used for the thermal mixing of fluids at different temperatures. A high-frequency temperature fluctuation can cause thermal fatigue of the pipeline materials; thus, many scholars carried out a series of research on thermal mixing using a branch pipeline [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Guo

et al. 2022

Energies

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It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study, the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV, a deep neural network (DNN) model was constructed based on CFD simulation data, and the main influencing factors of the COV including flow velocity, hydrogen blending ratio, gas temperature, flow distance, and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process, the effects of various parameters on the prediction accuracy of the DNN model were studied, and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.

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Section: Introductionmentioning

confidence: 99%

Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Guo

et al. 2022

Energies

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“…An empirical correlation was also provided to calculate the condensation rate. Su et al (2020) evaluated numerically using ANSYS FLUENT 17.1 the thermal mixing in a T-shaped channel in the presence of inflow pulsation. Various temperature differences between the two inlet streams (hot and cold) were analyzed.…”

Section: Introductionmentioning

confidence: 99%

Thermal mixing in T-shaped micromixers with a porous block by the lattice Boltzmann method: Influence of the mixing channel configuration

Ajarostaghi

Poncet

2022

Front. Therm. Eng.

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The present paper investigates the thermal mixing and cooling processes in a passive micromixer, which is applicable for the cooling of electronic devices. Employing a porous block and testing different configurations for the mixing channel is considered to enhance the mixing process and cooling performance. A 2D lattice Boltzmann thermal model is utilized to investigate the thermal performance of a T-micromixer with a porous block. Two different types of mixing channel configurations, including a step-shaped and a zigzag-shaped channel, are considered, and the obtained results are compared with those of the simple mixing channel. The thermal mixing and cooling of two miscible fluids, at 50 and 25°C entering the micromixer, are investigated. The results show that changing the mixing channel configuration may create a chaotic laminar flow, which enhances the heat transfer rate between the mixed flow and the channel wall. Whatever the Reynolds number, the step-shaped mixing channel exhibits better mixing performance than the zigzag-shaped one. For the T-micromixer with a zigzag-shaped and step-shaped mixing channel, the cases with h/H = 0.5 and h/H = 0, respectively, exhibit better thermal mixing and cooling performance.

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“…The dominant structures of the velocity and concentration fields were extracted by proper orthogonal decomposition (POD). Su et al [5,6]…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mode Decomposition of Impinging Jet in a T-Junction

Anqi¹,

Li²,

Li³

et al. 2022

Preprint

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Thermal fatigue in a T-junction is of crucial importance issue for the coolant system of nuclear energy plants. The dynamic mode decomposition (DMD) is employed to analyze the snapshot data from simulation results with applying large eddy simulation (LES). The thermal mixing flow in a square T-junction is simulated at the impinging jet (MR = 0.2). The temperature difference of hot and cold fluids is 15 K. The corresponding Reynolds number is about 20000. The results show that the frequency of the velocity modes is not equal to that of the temperature mode. The frequency of the temperature mode 1 is more than 40% higher than that of the velocity mode 1. The main spatial structures of the temperature field and the velocity field are alternately arranged along the trajectory of the branch fluid entering the main duct. The main coherent structure of the velocity field arrives at the bottom wall of x/Dm = 1, whereas for the temperature field, it basically appears in the region of x/Dm = 0.6 -0.8. The negative structures of the velocity mode 1 induces the positive structures of the temperature mode 1.Also, the coherent structures of Modes 1 and 2 grow along the normal direction during downstream propagation.

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Effect of temperature difference on the thermal mixing phenomenon in a T-junction under inflow pulsation

Cited by 13 publications

References 23 publications

Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Thermal mixing in T-shaped micromixers with a porous block by the lattice Boltzmann method: Influence of the mixing channel configuration

Dynamic Mode Decomposition of Impinging Jet in a T-Junction

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