Experimental study on the influence of inlet pressure on the flow parameters at the reverse flow boundary of vortex tube and its relation with energy separation performance

Guangming, Chen; Guannan, Jiang; Liming, Tang; Nian, Li

doi:10.1016/j.expthermflusci.2024.111211

Experimental Thermal and Fluid Science

2024

DOI: 10.1016/j.expthermflusci.2024.111211

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Experimental study on the influence of inlet pressure on the flow parameters at the reverse flow boundary of vortex tube and its relation with energy separation performance

Chen Guangming,

Jiang Guannan,

Tang Liming

et al.

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Cited by 2 publications

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Investigation of flow boiling phenomenon in micro-pin-fin heat sinks adapted to actively direction-changing moving systems

Markal,

Evcimen

2024

International Communications in Heat and Mass Transfer

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Investigation of flow boiling phenomenon in micro-pin-fin heat sinks adapted to actively direction-changing moving systems

Markal,

Evcimen

2024

International Communications in Heat and Mass Transfer

View full text Add to dashboard Cite

Research on temperature performance prediction of vortex tubes based on artificial neural networks

Han,

Li,

Liu

et al. 2024

Eng. Res. Express

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This study constructs a hybrid neural network model by integrating the physical constraints of the Bernoulli equation and Nikolaev's formula. The model is designed to explore and predict the variation pattern of the cold end temperature in a vortex tube. The input parameters include inlet pressure, inlet temperature, and cold mass fraction, with the cold end temperature as the output parameter. The network employs a multilayer feedforward model and the Levenberg-Marquardt learning algorithm, using a hyperbolic tangent function as the activation function. To evaluate the statistical validity of the developed model, the coefficient of determination (R²) and root mean square error (RMSE) are utilized, along with an analysis of the model's uncertainty and robustness. The hybrid model achieves an R² of 0.9936 and an RMSE of 0.3392, demonstrating strong performance in terms of uncertainty and robustness. These results indicate that the model accurately predicts the cold end temperature variation in the vortex tube. Furthermore, the findings reveal an optimal pressure range (0.49 MPa to 0.76 MPa) and cold mass fraction range (0.1 to 0.2) that minimize the cold end temperature.

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Experimental study on the influence of inlet pressure on the flow parameters at the reverse flow boundary of vortex tube and its relation with energy separation performance

Cited by 2 publications

References 52 publications

Investigation of flow boiling phenomenon in micro-pin-fin heat sinks adapted to actively direction-changing moving systems

Investigation of flow boiling phenomenon in micro-pin-fin heat sinks adapted to actively direction-changing moving systems

Research on temperature performance prediction of vortex tubes based on artificial neural networks

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