Pool Boiling on Modified Surfaces Using R-123

Ahmad, Syed Waqas; Lewis, John S.; McGlen, Ryan J.; Karayiannis, Tassos G.

doi:10.1080/01457632.2014.889493

Cited by 25 publications

(7 citation statements)

References 19 publications

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“…This work proposes, for the first time, the use of machine learning classifiers to identify flow patterns and flow pattern transition in a single-loop PHP system with two different working fluids and in varying gravity conditions using data from the European Space Agency Parabolic Flight Campaigns [11,25]. Since the single-loop PHP allows the visualization of flow patterns, this makes the present analysis unique in understanding if ML can be successfully trained to recognize PHP flow patterns.…”

Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

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Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.

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Section: Machine Learning Algorithms For Two-phase Flow Heat Transfermentioning

confidence: 99%

“…There are a wide variety of available cooling processes for electronics. The most common methods based on two-phase flow are flow boiling [4][5][6][7][8][9][10], pool boiling [11][12][13][14], and impinging jets [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Loyola-Fuentes¹,

Pietrasanta

Marengo

et al. 2022

Energies

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“…In the present investigation, 380 data points have been collected from the saturated pool boiling experiments of microporous coated surfaces for different high-and low-surface tension working fluids including water, dielectric liquids, and refrigerants [11,12,14,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Most of the tested porous surfaces are manufactured by sintering techniques.…”

Section: Experimental Data Collectionmentioning

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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“…Ahmad et al. 14 studied the saturated pool boiling performance of copper surfaces modified with different treatments, such as emery-polished surface, a finely sandblasted surface, a rough sandblasted surface, an electron beam-enhanced surface, and a sintered surface. Above studies 8–14 clearly indicates that the micro/nanoporous coatings enhance the boiling heat transfer significantly when compared to the ordinary surfaces and the need for improving the surface for heat transfer.…”

Section: Introductionmentioning

confidence: 99%

Heat transfer characteristics and flow visualization of anodized flat thermosiphon

Varughese

Raj

Sharifpur

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Use of environmentally safe refrigerants is the need of the hour to minimize the carbon footprints and to reduce the ozone depletion. The application of such environmentally safe refrigerants in the thermosyphon with the micro/nanoporous coating is expected to operate efficiently. In this study, a simple cost-effective anodizing technique to form a uniform thin micro/nanoporous coating at the inner wall of the thermosiphon is undertaken, and the resulting performance enhancement is presented. Also, the effect of fill ratio, heat input and the porous coating on the heat transfer characteristics of flat thermosiphon are studied. The heat transfer coefficient in the evaporator of anodized and nonanodized thermosiphon is enhanced to a maximum of 3587 and 2742 W/m2 K, respectively. The number of pores present in the anodized evaporator surface is estimated as 3.4 × 109. The width (pore size) and depth of pore are measured as 0.25 µm and 16 nm respectively. The porosity of the coating is around 55%, and the contact angle is ≤10°. As a result, the heat transfer coefficient at the evaporator and condenser of anodized thermosyphon is enhanced to a maximum of 24% and 13% respectively, when compared to that of the nonanodized thermosiphon.

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Pool Boiling on Modified Surfaces Using R-123

Cited by 25 publications

References 19 publications

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

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

Heat transfer characteristics and flow visualization of anodized flat thermosiphon

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