Robust and general predictive models for condensation heat transfer inside conventional and mini/micro channel heat exchangers

Moradkhani, M.A.; Hosseini, Seyyed Hossein; Song, Mengjie

doi:10.1016/j.applthermaleng.2021.117737

Cited by 46 publications

(10 citation statements)

References 127 publications

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“…Considering mini-channels to be those affected by surface tension, channels with We GT < 100 are mini-channels, and those with We GT > 100 are macro or conventional channels. While Shah (2022b) [10] was found to give the least MAD for both macro and mini channels, other correlations that had a reasonable agreement with macro channel data were those of Moradkhani et al (2021) [14], Dorao and Fernandino (2018) [12], Ananiev et al (1961) [42], and Marinheiro et al ( 2023) [5]. The correlation of Cavallini et al (2006) [11] has been reported to be accurate by these researchers as well as by many other researchers.…”

Section: Evaluation Of Model 2 Using Various Correlationsmentioning

confidence: 70%

“…The final version is Shah (2022) [10] which differs from that in Shah (2022) [3] only for quality ≥ 0.99. Among other correlations that have agreed with a wide range of test data are Cavallini et al (2006) [11], Dorao and Fernandini (2018) [12], Hosseini et al (2020) [13], and Moradkhani et al (2022) [14]. The aforementioned correlations are intended for both mini and macro channels.…”

Section: Previous Work 21 Condensation Of Saturated Vaporsmentioning

confidence: 81%

“…In calculations with all Shah correlations, D HP was used to calculate the single-phase heat transfer coefficient and the Reynolds number. The same was also carried out with other correlations, except for those of Kim and Mudawar (2013) [15], Dorao and Fernandino (2018) [12], Hosseini et al (2020) [13], Moradkhani et al (2021) [14], and Marinheiro et al ( 2023) [5]. For these correlations, D HYD was used as the equivalent diameter in all calculations because that was specified by these authors.…”

Section: Parameter Data Rangementioning

confidence: 99%

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An Evaluation of Correlations for Predicting the Heat Transfer Coefficient during the Condensation of Saturated and Superheated Vapors Inside Channels

Shah

2024

Thermo

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Condensation heat transfer is involved in many industrial applications. Therefore, it is important to know the relative accuracy of the available methods for predicting heat transfer. Condensation can occur with saturated as well as superheated vapors. Predictive methods for both conditions were evaluated using a wide range of data. Twelve well-known correlations for the condensation of saturated vapor, including the most recent ones, were compared with data for 51 pure fluids and mixtures from 132 sources in horizontal and vertical channels of many shapes. Channel hydraulic diameters were 0.08–49 mm, the mass flux was 1.1–1400 kg/m2s, and the reduced pressure range was 0.0006–0.949. The fluids included water, CO2, ammonia, hydrocarbons, halocarbon refrigerants, various chemicals, and heat transfer fluids. The best predictive technique was identified. The three most commonly used models for heat transfer during the condensation of superheated vapors were studied. They were first compared with test data using measured saturated condensation and forced convection heat transfer coefficients to select the best model. The selected model was then compared with test data using various correlations for heat transfer coefficients needed in the model. The best correlations to use in the model were identified. The results of this research are presented, as are recommendations for use in design.

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Section: Evaluation Of Model 2 Using Various Correlationsmentioning

confidence: 70%

Section: Previous Work 21 Condensation Of Saturated Vaporsmentioning

confidence: 81%

Section: Parameter Data Rangementioning

confidence: 99%

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An Evaluation of Correlations for Predicting the Heat Transfer Coefficient during the Condensation of Saturated and Superheated Vapors Inside Channels

Shah

2024

Thermo

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“…The study shows that machine learning algorithms can be used to develop a robust new tool for predicting heat transfer coefficients in flow condensation in mini/micro channels. Moradkhani et al [4] presented a study which focused on the creation of accurate models for estimating the condensation heat transfer coefficient (HTC) inside conventional and mini/micro channel heat exchangers using machine learning methods. The study evaluated the performance of three different models: gaussian process regression (GPR), hybrid radial basis function (HRBF), and interpolating-based radial basis function (RBF).…”

Section: Prediction Methods That Utilize Neural Networkmentioning

confidence: 99%

Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur

Gluch,

Niksa-Rynkiewicz,

Mikielewicz

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Most of the literature models for condensation heat transfer prediction are based on specific experimental parameters and are not general in nature for applications to fluids and non-experimental thermodynamic conditions. Nearly all correlations are created to predict data in normal HVAC conditions below 40°C. High temperature heat pumps operate at much higher parameters. This paper aims to create a general model for the calculation of heat transfer coefficients during flow condensation which could be applied to a wide range of fluids and thermodynamical parameters up to the vicinity of the critical point. To achieve this goal authors present a model based on Feed Forward Neural Network. The designed neural network consists of 5 hidden layers and utilizes ReLu and linear activation functions. The first four layers consist of 50 neurons, and the last layer consists of 1 neuron. The network was trained on a consolidated database which consists of 4659 data points for 25 fluids and covers a range of reduced pressure from 0.1 to 0.9 for various mass velocities and diameters. Two input variants were considered. For randomly selected test data Mean Square Root achieved 0.1093 and Mean Absolute Error MAE achieved 0.2243 for the first configuration which consist of 4 parameters. For the second variant, which consists of 17 parameters, MSE achieved 0.0452 MAE achieved 0.1028.

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“…Since there are many factors affecting the bed-to-tube HTC in a gas–solid fluidized bed, it is necessary to clarify the magnitude of the influence of different factors on the heat transfer process. Correlation analysis , is helpful to characterize the relationship between the HTC and various factors, thereby guiding the establishment of an accurate local heat transfer model between a fluidized bed and horizontally immersed tubes. The degree of correlation between variables can be evaluated quantitatively by the correlation coefficient.…”

Section: Correlation Analysismentioning

confidence: 99%

Novel Segmental Model for Predicting Bed-to-Tube Heat Transfer Coefficient in Gas–Solid Fluidized Beds

Zheng,

Chen,

Liu

et al. 2024

Ind. Eng. Chem. Res.

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Accurate prediction of the bed-to-tube heat transfer coefficient (HTC) is the foundation of developing efficient fluidized bed heat exchangers, but there is still a lack of an effective prediction method. In this paper, the heat transfer mechanism around the immersed tube is revealed through the correlation analysis method. A computational fluid dynamics simulation was conducted to provide various flow field data for a gas–solid fluidized bed with an immersed tube. A correlation analysis was conducted to characterize the relationship between the local HTC and various factors. The results show that Kendall’s rank correlation coefficient (r k) between the solid phase volume fraction and HTC is less than 0.5 on the top of the tube, while the r k on the bottom side exceeds 0.9. According to the distribution characteristics of r k and the emulsion phase contacting time fraction (δe) around the tube, a theoretical model containing three different heat transfer mechanisms was developed. The threshold value for defining the dynamically changing boundaries of each heat transfer mechanism is obtained (δe = 0.88 and r k = 0.82). A comparison analysis demonstrates that the proposed model can predict the average HTC with a deviation of less than ±25% within a wide range of particle sizes (100–1000 μm).

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Robust and general predictive models for condensation heat transfer inside conventional and mini/micro channel heat exchangers

Cited by 46 publications

References 127 publications

An Evaluation of Correlations for Predicting the Heat Transfer Coefficient during the Condensation of Saturated and Superheated Vapors Inside Channels

An Evaluation of Correlations for Predicting the Heat Transfer Coefficient during the Condensation of Saturated and Superheated Vapors Inside Channels

Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur

Novel Segmental Model for Predicting Bed-to-Tube Heat Transfer Coefficient in Gas–Solid Fluidized Beds

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