Two-Dimensional Flow Boiling Characteristics with Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

2021 ◽  
Author(s):  
Anwarul Karim ◽  
Yoon Jo Kim ◽  
Jong-Hoon Kim
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Heat Sink ◽  
Flow Boiling ◽  
Two Dimensional ◽  
Two Phase ◽  
Hydrophilic Surfaces

Abstract As technology becomes increasingly miniaturized, thermal management becomes challenging to keep devices away from overheating due to extremely localized heat dissipation. Two-phase cooling or flow-boiling in micro-spaces utilizes the highly efficient thermal energy transport of phase change from liquid to vapor. However, the excessive consumption of liquid-phase by highly localized heat source causes the two-phase flow maldistribution, leading to a significantly reduced heat transfer coefficient, high-pressure loss, and limited flow rate. In this study, flow-boiling in a two-dimensional microgap heat sink with a hydrophilic coating is investigated with bubble morphology, heat transfer, and pressure drop for conventional (non-hydrophilic) and hydrophilic heat sinks. The experiments are carried out on a stainless steel plate, having a micro gap depth of 170 µm using deionized water at room temperature. Two different hydrophilic surfaces (partial and full channel shape) are fabricated on the heated surface to compare the thermal performance with the conventional surface. Vapor films and slugs are flushed quickly on the hydrophilic surfaces, resulting in heat transfer enhancement on the hydrophilic heat sink compared to the conventional heat sink. The channel hydrophilic heat sink shows better cooling performance and pressure stability as it provides a smooth route for the incoming water to cool the hot spot. Moreover, the artificial neural network prediction of heat transfer coefficient shows a good agreement with the experimental results as data fits within ±5% average error.

scholarly journals Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

2020 ◽  
pp. 238-238
Author(s):  
Adel Bouali ◽  
Salah Hanini ◽  
Brahim Mohammedi ◽  
Mouloud Boumahdi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Flow Boiling ◽  
Low Flow ◽  
Inclined Channel ◽  
Artificial Neural

The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient (HTC) for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. orientation ,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron (MLP) neural network, whereas the analogous HTC was selected to be its output. Via the method of trial-and-error, MLP network with 30 neurons in the hidden layer was attained as optimal ANN structure. The fact that is was enabled to predict accurately the HTC. For the training set, the mean relative absolute error (MRAE) is about 0.68 % and the correlation coefficient (R) is about 0.9997. As for the testing and validation set they are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The comparison of the developed ANN model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

Flow Boiling Heat Transfer in a Silicon Microchannel Heat Sink Using Liquid Crystal Thermography

2008 ◽  
Author(s):  
Ayman Megahed ◽  
Ibrahim Hassan ◽  
Tariq Ahmad
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Heat Sink ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Heat Fluxes ◽  
Working Fluid ◽  
Microchannel Heat Sink

The present study focuses on the experimental investigation of boiling heat transfer characteristics and pressure drop in a silicon microchannel heat sink. The microchannel heat sink consists of a rectangular silicon chip in which 45 rectangular microchannels were chemically etched with a depth of 295 μm, width of 254 μm, and a length of 16 mm. Un-encapsulated Thermochromic liquid Crystals (TLC) are used in the present work to enable nonintrusive and high spatial resolution temperature measurements. This measuring technique is used to provide accurate full and local surface-temperature and heat transfer coefficient measurements. Experiments are carried out for mass velocities ranging between 290 to 457 kg/m2.s and heat fluxes from 6.04 to 13.06 W/cm2 using FC-72 as the working fluid. Experimental results show that the pressure drop increases as the exit quality and the flow rate increase. High values of heat transfer coefficient can be obtained at low exit quality (xe < 0.2). However, the heat transfer coefficient decreases sharply and remains almost constant as the quality increases for an exit quality higher than 0.2.

scholarly journals Experimental Investigation of Flow Condensation in Microgravity

2013 ◽  
Author(s):  
Hyoungsoon Lee ◽  
Ilchung Park ◽  
Christopher Konishi ◽  
Issam Mudawar ◽  
Rochelle I. May ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Heat Dissipation ◽  
Flow Boiling ◽  
Condensation Heat Transfer ◽  
Interfacial Structure ◽  
Sensible Heat ◽  
Two Phase ◽  
The Heat Transfer Coefficient

Future manned missions to Mars are expected to greatly increase the space vehicle’s size, weight, and heat dissipation requirements. An effective means to reducing both size and weight is to replace single-phase thermal management systems with two-phase counterparts that capitalize upon both latent and sensible heat of the coolant rather than sensible heat alone. This shift is expected to yield orders of magnitude enhancements in flow boiling and condensation heat transfer coefficients. A major challenge to this shift is a lack of reliable tools for accurate prediction of two-phase pressure drop and heat transfer coefficient in reduced gravity. Developing such tools will require a sophisticated experimental facility to enable investigators to perform both flow boiling and condensation experiments in microgravity in pursuit of reliable databases. This study will discuss the development of the Flow Boiling and Condensation Experiment (FBCE) for the International Space Station (ISS), which was initiated in 2012 in collaboration between Purdue University and NASA Glenn Research Center. This facility was recently tested in parabolic flight to acquire condensation data for FC-72 in microgravity, aided by high-speed video analysis of interfacial structure of the condensation film. The condensation is achieved by rejecting heat to a counter flow of water, and experiments were performed at different mass velocities of FC-72 and water and different FC-72 inlet qualities. It is shown that the film flow varies from smooth-laminar to wavy-laminar and ultimately turbulent with increasing FC-72 mass velocity. The heat transfer coefficient is highest near the inlet of the condensation tube, where the film is thinnest, and decreases monotonically along the tube, except for high FC-72 mass velocities, where the heat transfer coefficient is enhanced downstream. This enhancement is attributed to both turbulence and increased interfacial waviness. One-ge correlations are shown to predict the average condensation heat transfer coefficient with varying degrees of success, and a recent correlation is identified for its superior predictive capability, evidenced by a mean absolute error of 21.7%.

Phase-Change Heat Transfer of Ethanol-Water Mixtures: Towards Development of a Distributed Hydrogen Generator

2013 ◽  
Author(s):  
Manoj Kumar Moharana ◽  
Rohan M. Nemade ◽  
Sameer Khandekar
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Phase Change ◽  
Transfer Coefficient ◽  
Single Phase ◽  
Flow Boiling ◽  
Phase Region ◽  
Design Flow ◽  
Two Phase ◽  
Vapor Region

Hydrogen fuel from renewable bio-ethanol is a potentially strong contender as an energy carrier. Its distributed production by steam reforming of ethanol on microscale platforms is an efficient upcoming method. Such systems require (a) a pre-heater for liquid to vapor conversion of ethanol water mixtures (b) a gas-phase catalytic reactor. We focus on the fundamental experimental heat transfer studies (pool and flow boiling of ethanol-water mixtures) required for the primary pre-heater boiler design. Flow boiling results (in a 256 μm square channel) clearly show the influence of mixture composition. Heat transfer coefficient remains almost constant in the single-phase region and rapidly increases as the two-phase region starts. On further increasing the wall superheat, heat transfer starts to decrease. At higher applied heat flux, the channel is subjected to axial back conduction from the single-phase vapor region to the two-phase liquid-vapor region, thus raising local wall temperatures. Simultaneously, to gain understanding of phase-change mechanisms in binary mixtures and to generate data for the modeling of flow boiling process, pool-boiling of ethanol-water mixtures has also been initiated. After benchmarking the setup against pure fluids, variation of heat transfer coefficient, bubble growth, contact angles, are compared at different operating conditions. Results show strong degradation in heat transfer in mixtures, which increases with operating temperature.

Microchannel Size Effects on Two-Phase Local Heat Transfer and Pressure Drop in Silicon Microchannel Heat Sinks With a Dielectric Fluid

2007 ◽  
Author(s):  
Tannaz Harirchian ◽  
Suresh V. Garimella
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Flow Boiling ◽  
Heat Sinks ◽  
Dielectric Fluid ◽  
Two Phase ◽  
Microchannel Heat Sinks ◽  
The Heat Transfer Coefficient

Two-phase heat transfer in microchannels can support very high heat fluxes for use in high-performance electronics-cooling applications. However, the effects of microchannel cross-sectional dimensions on the heat transfer coefficient and pressure drop have not been investigated extensively. In the present work, experiments are conducted to investigate the local flow boiling heat transfer in microchannel heat sinks. The effect of channel size on the heat transfer coefficient and pressure drop is studied for mass fluxes ranging from 250 to 1600 kg/m2s. The test sections consist of parallel microchannels with nominal widths of 100, 250, 400, 700, and 1000 μm, all with a depth of 400 μm, cut into 12.7 mm × 12.7 mm silicon substrates. Twenty-five microheaters embedded in the substrate allow local control of the imposed heat flux, while twenty-five temperature microsensors integrated into the back of the substrates enable local measurements of temperature. The dielectric fluid Fluorinert FC-77 is used as the working fluid. The results of this study serve to quantify the effectiveness of microchannel heat transport while simultaneously assessing the pressure drop trade-offs.

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