scholarly journals Deep learning predicts boiling heat transfer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngjoon Suh ◽  
Ramin Bostanabad ◽  
Yoonjin Won
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Boiling Heat Transfer ◽  
Bubble Dynamics ◽  
Advective Transport ◽  
Detection Algorithms ◽  
Learning Framework ◽  
Automated Learning ◽  
Daunting Challenge

AbstractBoiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.

Thermal Bubble Dynamics Under the Effect of Acoustic Vibration

2009 ◽  
Author(s):  
Xiaopeng Qu ◽  
Huihe Qiu
Keyword(s):  
Heat Transfer ◽  
Acoustic Field ◽  
Vapor Bubble ◽  
Boiling Heat Transfer ◽  
High Speed ◽  
Bubble Dynamics ◽  
Acoustic Vibration ◽  
Micro Electro Mechanical System ◽  
Mems Technology ◽  
Enhanced Boiling

The effect of acoustic field on the dynamics of micro thermal bubble is investigated in this paper. The micro thermal bubbles were generated by a micro heater which was fabricated by standard Micro-Electro-Mechanical-System (MEMS) technology and integrated into a mini chamber. The acoustic field formed in the mini chamber was generated by a piezoelectric plate which was adhered on the top side of the chamber’s wall. The dynamics and related heat transfer induced by the micro heater generated vapor bubble with and without the existing of acoustic field were characterized by a high speed photograph system and a micro temperature sensor. Through the experiments, it was found that in two different conditions, the temperature changing induced by the micro heater generated vapor bubble was significantly different. From the analysis of the high speed photograph results, the acoustic force induced micro thermal bubble movements, such as forcibly removing, collapsing and sweeping, were the main effects of acoustic enhanced boiling heat transfer. The experimental results and theoretical analysis were helpful for understanding of the mechanisms of acoustic enhanced boiling heat transfer and development of novel micro cooling devices.

scholarly journals Prediction for Flow Boiling Heat Transfer in Small Diameter Tube Using Deep Learning

2017 ◽  
Vol 31 (4) ◽  
pp. 412-421 ◽  
Author(s):  
Koji ENOKI ◽  
Yuichi SEI ◽  
Tomio OKAWA ◽  
Kiyoshi SAITO
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Small Diameter ◽  
Flow Boiling Heat Transfer

scholarly journals Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3383
Author(s):  
Uzair Sajjad ◽  
Imtiyaz Hussain ◽  
Muhammad Imran ◽  
Muhammad Sultan ◽  
Chi-Chuan Wang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Deep Learning ◽  
Thermophysical Properties ◽  
Boiling Heat Transfer ◽  
Parameters Estimation ◽  
Learning Method ◽  
Working Fluids ◽  
Coated Surfaces ◽  
Nanoporous Surfaces

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.

Pore-Scale Simulation on Pool Boiling Heat Transfer and Bubble Dynamics in Open-Cell Metal Foam by Lattice Boltzmann Method

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jie Qin ◽  
Zhiguo Xu ◽  
Xiaofei Ma
Keyword(s):  
Heat Transfer ◽  
Lattice Boltzmann ◽  
Boiling Heat Transfer ◽  
Bubble Dynamics ◽  
Metal Foam ◽  
Pool Boiling ◽  
Open Cell ◽  
Pool Boiling Heat Transfer ◽  
Boltzmann Method ◽  
Foam Thickness

Abstract Based on the newly developed geometrical model of open-cell metal foam, pool boiling heat transfer in open-cell metal foam, considering thermal responses of foam skeletons, is investigated by the phase-change lattice Boltzmann method (LBM). Pool boiling patterns are obtained at different heat fluxes. The effects of pore density and foam thickness on bubble dynamics and pool boiling heat transfer are revealed. The results show that “bubble entrainment” promotes fluid mixing and bubble sliding inside metal foam. Based on force analysis, the sliding bubble is pinned on the heating surface and cannot lift off completely at high heat flux due to the increasing surface tension force. Pool boiling heat transfer coefficient decreases with increasing pore density and foam thickness due to high bubble escaping resistance.

Boiling Heat Transfer Using Spatially-Variant and Uniform Microporous Coatings

2019 ◽  
Author(s):  
Quang N. Pham ◽  
Youngjoon Suh ◽  
Bowen Shao ◽  
Yoonjin Won
Keyword(s):  
Heat Transfer ◽  
Phase Change ◽  
Thermal Management ◽  
Boiling Heat Transfer ◽  
Bubble Dynamics ◽  
Bubble Nucleation ◽  
Vapor Flow ◽  
Inverse Opal ◽  
Two Phase ◽  
Spatially Variant

Abstract Two-phase thermal management offers cooling performance enhancement by an order of magnitude higher than single-phase flow due to the latent heat associated with phase change. Among the modes of phase-change, boiling can effectively remove massive amounts of heat flux from the surface by employing structured or 3D microporous coatings to significantly enlarge the interfacial surface area for improved heat transfer rate as well as increase the number of potential sites for bubble nucleation and departure. The bubble dynamics during pool boiling are often considered to be essential in predicting heat transfer performance, causing it to be a field of significant interest. While prior investigations seek to modulate the bubble dynamics through either active (e.g., surfactants, electricity) or passive means (e.g., surface wettability, microstructures), the utilization of an ordered microporous architecture to instigate desirable liquid and vapor flow field has been limited. Here, we investigate the bubble dynamics using various spatial patterns of inverse opal channels to induce preferential heat and mass flow site in highly-interconnected microporous media. A fully-coated inverse opal surface demonstrates the intrinsic boiling effects of a uniform microporous coating, which exhibits 156% enhancement in heat transfer coefficient in comparison to the polished silicon surface. The boiling heat transfer performances of spatially-variant inverse opal channels significantly differ based on the pitch spacings between the microporous channels, which dictate the bubble coalescent behaviors and bubble departure characteristics. The elucidated boiling heat transfer performances will provide engineering guidance toward designing optimal two-phase thermal management devices.

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