A neural network model for free-falling condensation heat transfer in the presence of non-condensable gases

Cho, Eunho; Lee, Haeun; Kang, Min-Soo; Jung, Daewoong; Lee, Geonhee; Sael, Lee; Kharangate, Chirag R.; Ha, Huiun; Huh, Sun-Chul; Lee, Hyoungsoon

doi:10.1016/j.ijthermalsci.2021.107202

Cited by 34 publications

(8 citation statements)

References 49 publications

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“…Recently, there has been a steady rise in the use of machine learning (ML) techniques for prediction and characterization of complex two-phase heat transfer problems such as condensation and boiling. [279][280][281][282][283] These techniques have shown a promising pathway for enhancing the performance of predictive models in these complex physical phenomena and have also helped with better feature extraction and understanding of the underlying physics. Many studies have focused on using ML models to substitute the empirical or semi-empirical correlations developed for pressure drop and heat transfer estimation of internal condensation and boiling.…”

Section: Machine Learning Methods For Boiling and Condensationmentioning

confidence: 99%

Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review

et al. 2023

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Section: Machine Learning Methods For Boiling and Condensationmentioning

confidence: 99%

Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review

et al. 2023

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“…The Lennard-Jones (LJ) parameters of the graphene carbon atom are ε = 0.1049 kcal/mol and σ = 0.3851 nm. 38 The interaction between components was described using the Lennard-Jones 12−6 potential, E = 4ε[(σ/r) 12 − (σ/r) 6 ], where ε and σ represent the characteristic energy and the van der Waals radius, respectively. The heteroatomic Lennard-Jones parameters were determined using the arithmetic combining rule.…”

Section: ■ Simulation Detailmentioning

confidence: 99%

“…This finding indicates that the primary impediment to the overall efficiency of solar thermal desalination systems is the low efficiency of the condensation component. The utilization of various materials for freshwater collection in condensation components, particularly conventional transparent cover plate designs, is constrained by factors such as limited light transmission and the presence of noncondensing gas, which obstruct the crucial improvement of overall performance. − Compared with traditional glass cover plates in noncondensing situations, the utilization of gel materials for air freshwater capture in low relative humidity conditions − has demonstrated superior performance. Thermosensitive hydrogels , typically incorporate both hydrophobic and hydrophilic groups within their own network.…”

Section: Introductionmentioning

confidence: 99%

Deswelling Mechanisms of PNIPAM Grafted in Nanochannels: A Molecular Dynamics Simulation Study

Chen,

Niu,

Gao

et al. 2024

Langmuir

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Porous thermosensitive hydrogels exhibit a more flexible strategy for freshwater capture compared to conventional hydrogels. This study employs molecular dynamics (MD) simulation to investigate the deswelling behavior of poly(Nisopropylacrylamide) (PNIPAM) grafted within the nanochannel, aiming to elucidate the deswelling elimination process at various temperatures. Notably, a distinct phase separation is observed at specific temperatures above the lower solution temperature (LCST). Furthermore, this study takes the effect of heat flux into account, wherein distinct heat fluxes lead to varying levels of phase separation between water and the polymer. Specifically, the number of hydrogen bonds, volume of polymer chains, and density distribution of water molecules are statistically analyzed to reveal the mechanism of phase separation in a thermosensitive hydrogel. These findings provide insight into the accelerated deswelling kinetics of the PNIPAM polymer chain, which has guiding significance for the field of water harvesting by the enhancement of the water release capacity in thermosensitive hydrogels.

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“…Most of the methods for enhancing heat transfer in existing heat exchanger systems are inclined towards better fluid mixing, thereby improving heat transfer efficiency in different types of applications like conversion of liquid to vapour [1,2] and low droplet impact cooling [3][4][5]. The various passive heat transfer enhancement methods are ribs and impingement [6,7], vortex generators [8], the usage of numerous microchannels [9,10], small pin fins [11] and conventional twisted tapes [12]. These techniques cause rapid fluid mixing between cold and hot regions in the flow sections, further causing higher heat transfer.…”

Section: Introductionmentioning

confidence: 99%

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

Deshmukh,

Lahane,

Sumant

et al. 2023

Aerospace

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Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor R3 of 1.5 to 2.65 to the equivalent Reynolds number Rec value of 20,000. It is observed that most of the curved rib configurations show a performance ratio R3 maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement Nua/Nus, average heat transfer enhancement fa/fs, and performance ratio R3, i.e., Nua/Nuc.

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A neural network model for free-falling condensation heat transfer in the presence of non-condensable gases

Cited by 34 publications

References 49 publications

Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review

Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review

Deswelling Mechanisms of PNIPAM Grafted in Nanochannels: A Molecular Dynamics Simulation Study

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

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