A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Sajjad, Uzair; Hussain, Imtiyaz; Hamid, Khalid; Bhat, Showkat Ahmad; Ali, Hafız Muhammad; Wang, Chi-Chuan

doi:10.1007/s10973-021-10606-8

Cited by 41 publications

(11 citation statements)

References 42 publications

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“…The details regarding the training process of the DL method are available in our previous study [22]. The experimental data were divided into 80/20 for the training and testing.…”

Section: Developing and Training The DL Modelmentioning

confidence: 99%

“…It is therefore crucial to evaluate the boiling performance of various nanoporous surfaces produced by different fabrication methods. However, some efforts have been made recently for the prediction of the critical heat flux or heat transfer coefficient by artificial intelligence techniques for nanofluids [21], sintered coated microporous surfaces [22], nanorefrigerants [23], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

et al. 2021

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The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.

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“…The details regarding the training process of the DL method are available in our previous study [22]. The experimental data were divided into 80/20 for the training and testing.…”

Section: Developing and Training The DL Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

et al. 2021

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“…The predictability of the empirical correlations is usually hampered by these parameters, because these correlations were developed on a limited database. To cover a wide range of data, an artificial intelligence (AI) model, based on advance algorithms and new libraries, can be developed with high accuracy [1,15]. Keeping this in view, the objective of this study is to develop Bayesian optimized deep neural network models to perform a sensitivity analysis for finding the most influential parameters in the boiling heat transfer of sintered coated porous surfaces fabricated on copper substrate, subjected to a variety of high-and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface.…”

Section: Aim and Motivation Of The Studymentioning

confidence: 99%

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

et al. 2021

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The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.

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“… 11 Several laboratory tests and field trials have shown that surfactant-enhanced imbibition can achieve promising results after hydraulic fracturing in unconventional resource exploration. 12 − 16 At present, a lot of research work related to the imbibition of surfactant solutions has been carried out by related scholars. 17 − 20 To name only a few, in the process of imbibition of anionic surfactant solution, the effects of capillary radius and surfactant solution properties on the position of the oil–water interface in oil wet horizontal capillary were studied.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors of Surfactant Stripping Crude Oil and Spontaneous Imbibition Mechanism of Surfactants in a Tight Reservoir

et al. 2022

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Surfactants play a vital role in the working fluid during the exploitation of tight reservoirs. The main goal is to clarify the mechanism of surfactant production enhancement in the reservoir. In this paper, starting from the interface properties and emulsifying properties of surfactants, the factors affecting the stripping of crude oil by different surfactants were described in detail. Meanwhile, the imbibition experiments of cores were used to clarify the two spontaneous imbibition mechanisms of the surfactant. Namely, they are the capillary force expulsion caused by the emulsion stripping thermal diffusion–convection and the wettability change. When the interfacial tension between the surfactant and oil is in the range of 10 –2 –10 –3 mN/m, the particle size of emulsion is less than 1 μm, and the oil stripping efficiency is greater than 58%. The imbibition is mainly caused by thermal diffusion–convection. The wetting angle of the surfactant mainly changing wettability is less than 15°, and the adhesion work is greater than 52 mN/m. Using X-ray computed tomography, the surfactant imbibition distance of different permeability types of cores was obtained. The results show that higher permeability cores have a deeper imbibition distance. The results of this paper enrich the mechanism of enhanced oil recovery by surfactants and have important implications for the exploitation of tight reservoirs.

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A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Cited by 41 publications

References 42 publications

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

Influencing Factors of Surfactant Stripping Crude Oil and Spontaneous Imbibition Mechanism of Surfactants in a Tight Reservoir

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