A general correlation for the frictional pressure drop during condensation in mini/micro and macro channels

Moradkhani, M.A.; Hosseini, Seyyed Hossein; Valizadeh, Mohammadreza; Zendehboudi, Alireza; Ahmadi, Goodarz

doi:10.1016/j.ijheatmasstransfer.2020.120475

Cited by 28 publications

(21 citation statements)

References 40 publications

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“…Therefore, they cannot be considered as general and accurate correlations. The Moradkhani et al 41 , Muller-Steinhagen and Heck 35 , and Kim and Mudawar 69 correlations which were developed for two-phase FPD in straight tubes, show roughly similar results for helically coiled tubes with the AARE of 35.90%, 40.32%, and 42.29%, respectively. In addition, their AAE values show that the straight tubes models underestimate FPD for helically coiled tubes because these tubes have higher pressure drops than those of straight tubes due to centrifugal force and secondary flow effects 20 .…”

Section: Resultsmentioning

confidence: 68%

“…These correlations were selected because they showed better results than the others models for estimating the experimental data based on Table 4 . Figure 4 shows that Ferraris and Marcel 52 , Santini et al 29 and Wongwises and Polsongkram 48 correlation results are more reasonable than those of straight tubes’ models, and most data points predicted by Moradkhani et al 41 , Muller-Steinhagen and Heck 35 and Kim and Mudawar 69 models, are beyond the 30% error bounds. In addition, as discussed previously, the models for straight tubes significantly underestimate the coiled tubes’ FPD.…”

Section: Resultsmentioning

confidence: 86%

“…For visualizing the previous models’ accuracy, Fig. 4 compares the predictions of Ferraris and Marcel 52 , Santini et al 29 and Wongwises and Polsongkram 48 correlations for coiled tubes as well as the Moradkhani et al 41 , Muller-Steinhagen and Heck 35 and Kim and Mudawar 69 models for straight tubes with the corresponding experimental data. These correlations were selected because they showed better results than the others models for estimating the experimental data based on Table 4 .…”

Section: Resultsmentioning

confidence: 99%

“…The other coiled tubes’ correlations do not provide good results in different orientations. In addition, the straight tubes’ models developed by Muller-Steinhagen and Heck 35 , Kim and Mudawar 69 , and Moradkhani et al 41 exhibit high deviations in all orientations due to the secondary flow effects caused by the centrifugal force in coiled tubes. Overall, none of the previous models provide reasonable predictions for horizontal orientation, and all of them show the AARE of higher than 25%.…”

Section: Resultsmentioning

confidence: 99%

“…In our previous works, the GP technique was used for estimating the two-phase FPD in straight channels 41 , heat transfer coefficient in straight tubes 60 , and helically coiled tubes 10 . Additional detailed information about the GP technique was reported in the earlier studies 61 , 62 .…”

Section: Methodsmentioning

confidence: 99%

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Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Moradkhani

Hosseini

Mansouri

et al. 2021

Sci Rep

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There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.

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Section: Resultsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Moradkhani

Hosseini

Mansouri

et al. 2021

Sci Rep

Self Cite

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Prediction of steady-state two-phase flow of nitrogen + extinguishant in the pipeline and a correlation for mass flow rate

Liu

Xie

Chen

et al. 2021

J Therm Anal Calorim

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Nitrogen used for pressurization of a fire extinguisher could be partially dissolved in the fire extinguishing agent, forming a binary mixture accompanied by a phase change while flowing inside the pipeline. Notwithstanding the widespread use of fire extinguishing system, an effective method has never been considered to predict two-phase flow performance of nitrogen + extinguishant in the pipeline. This paper presents investigation of the steady-state two-phase flow of extinguishant in the pipeline, including C3HF7 (HFC227ea), CF3I, and C2HF5 (HFC125). The average viscosity of mixture was calculated using six quoted methods (VM-1 to VM-6).Subsequently, inspired by one-dimensional adiabatic isenthalpic flow of refrigerant in a capillary tube, the corresponding prediction models (STFM-1 to STFM-6) for large mass flux nitrogen + extinguishant in a fire extinguishing pipeline were developed based on the VM-1 to VM-6. In comparison with previous experimental and theoretical data, the applicability and accuracy of the proposed mathematical models was examined from two different aspects, mass flow rate and pressure drop. The results indicated that both models, STFM-2 and STFM-3, predicted accurately for mass flow rate, and STFM-2 model predicted accurately for pressure drop. Finally, new correlations for mass flow rate and pressure drop have been established accurately based on summarizing the relevant predicted data, respectively. This work contributes to a good theoretical approach on the analysis of two-phase flow of nitrogen + extinguishant. Keywords: steady-state two-phase flow; adiabatic isenthalpic expansion; average viscosity; mass flow rate; pressure drop Nomenclature T' presumptive temperature r' parameter of the PRμ model defined in Eq. (5) p pressure, Pa μ dynamic viscosity, 10 -7 Pa•s

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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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A general correlation for the frictional pressure drop during condensation in mini/micro and macro channels

Cited by 28 publications

References 40 publications

Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Prediction of steady-state two-phase flow of nitrogen + extinguishant in the pipeline and a correlation for mass flow rate

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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