AN EXAMINATION OF THE WALL TEMPERATURE DROP PHENOMENON DURING APPROACH TO FLOW BOILING CRISIS

1986 ◽  
Author(s):  
D. R. H. Beattie ◽  
K.R. Lawther
Keyword(s):  
Wall Temperature ◽  
Flow Boiling ◽  
Temperature Drop ◽  
Boiling Crisis

FLOW BOILING CRISIS IN GROOVED BOILER-TUBES

1974 ◽  
Author(s):  
Kaneyasu Nishikawa ◽  
Tetsu Fujii ◽  
Suguru Yoshida ◽  
Masaki Ohno
Keyword(s):  
Flow Boiling ◽  
Boiling Crisis ◽  
Boiler Tubes

Highly Subcooled Flow Boiling: A Model for Estimating Heat Transfer Behind Sliding Bubbles in a Narrow Channel

2012 ◽  
Author(s):  
Arif B. Ozer ◽  
Donald K. Hollingsworth ◽  
Larry. C. Witte
Keyword(s):  
Heat Transfer ◽  
Flow Field ◽  
Heat Generation ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Narrow Channel ◽  
Heated Wall ◽  
Uniform Heat ◽  
Proposed Model ◽  
Sliding Bubbles

A quenching/diffusion analytical model has been developed for predicting the wall temperature and wall heat flux behind bubbles sliding in a confined narrow channel. The model is based on the concept of a well-mixed liquid region that enhances the heat transfer near the heated wall behind the bubble. Heat transfer in the liquid is treated as a one-dimensional transient conduction process until the flow field recovers back to its undisturbed level prior to bubble passage. The model is compared to experimental heat transfer results obtained in a high-aspect-ratio (1.2×23mm) rectangular, horizontal channel with one wide wall forming a uniform-heat-generation boundary and the other designed for optical access to the flow field. The working fluid was Novec™ 649. A thermochromic liquid crystal coating was applied to the outside of the uniform-heat-generation boundary, so that wall temperature variations could be obtained and heat transfer coefficients and Nusselt numbers could be obtained. The experiments were focused on high inlet subcooling, typically 15–50°C. The model is able to capture the elevated heat transfer rates measured in the channel without the need to consider nucleate boiling from the surface or microlayer evaporation from the sliding bubbles. Surface temperatures and wall heat fluxes were estimated for 17 different experimental conditions using the proposed model. Results agreed with the measured values within ±15% accuracy. The insight gathered from comparing the results of the proposed model to experimental results provides the basis for a better understanding of the physics of subcooled bubbly flow in narrow channels.

An Experimental Investigation of Flow Boiling Characteristics in a Single Transparent Heated Microtube

2009 ◽  
Author(s):  
Osamu Kawanami ◽  
Shih-Che Huang ◽  
Kazunari Kawakami ◽  
Itsuro Honda ◽  
Yousuke Kawashima ◽  
...  
Keyword(s):  
Heat Flux ◽  
Wall Temperature ◽  
Mass Velocity ◽  
Tube Wall ◽  
Flow Boiling ◽  
High Mass ◽  
Test Fluid ◽  
High Heat Flux ◽  
Gold Plating ◽  
Heated Tube

In the present study, flow boiling in a transparent heated microtube having a diameter of 1 mm was investigated in detail. The transparent heated tube was manufactured by the electroless gold plating method. The enclosed gas-liquid interface could be clearly recognized through the tube wall, and the inner wall temperature measurement and direct heating of the film were simultaneously conducted by using the tube. Deaerated and deionized water that was subcooled temperature of 15 K was used as a test fluid, and constant and stable mass velocities of 50, 100, and 200 kg/m2s were provided by using a twin plunger pump. Among our experimental results, a vapor bubble grew up in a direction opposite the flow at a low heat flux and low mass velocities; however, this flow pattern was not observed at a high mass velocity of 200 kg/m2s. Under the conditions of G = 50 kg/m2s and high heat flux, the liquid film surrounding an elongated bubble near the heated tube wall occasionally thickened partially. The inner wall temperature exhibited large random oscillations in this regime; however, the visual observation revealed that dry-patches did not occur. The mass velocity had a negligible effect on the boiling heat transfer except in the counter-growth bubble flow regime.

Infrared characteristics of wall temperature for single microtube during in-tube flow boiling

2012 ◽  
Vol 20 (3) ◽  
pp. 506-513 ◽  
Author(s):  
马虎根 MA Hu-gen ◽  
凃文静 TU Wen-jing ◽  
谢荣建 XIE Rong-jian ◽  
白健美 BAI Jian-mei
Keyword(s):  
Wall Temperature ◽  
Flow Boiling ◽  
Tube Flow

The Flow Boiling Crisis of Saturated Water in Pipes at High Pressures

2020 ◽  
Vol 67 (6) ◽  
pp. 349-354
Author(s):  
M. V. Minko ◽  
V. V. Yagov ◽  
S. S. Savekin
Keyword(s):  
High Pressures ◽  
Flow Boiling ◽  
Boiling Crisis ◽  
Saturated Water

Dryout During Flow Boiling in a Single Circular Minichannel: Experimentation and Modelling

2008 ◽  
Author(s):  
Davide Del Col ◽  
Alberto Cavallini ◽  
Stefano Bortolin ◽  
Marko Matkovic ◽  
Luisa Rossetto
Keyword(s):  
Heat Flux ◽  
Standard Deviation ◽  
Critical Heat Flux ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Temperature Fluctuations ◽  
Test Channel ◽  
Average Value ◽  
Drying Up ◽  
Experimental Values

This paper presents an experimental investigation on the dryout during flow boiling of R134a and R32 inside a 0.96 mm diameter single circular minichannel. In the present tests, the test channel is not electrically heated; instead, the flow boiling is achieved by means of a secondary fluid (water). Therefore, the heat flux is not uniform in the channel since the temperature of the water varies. The onset of dryout is detected by means of the standard deviation of the temperature readings in the wall. The wall temperature in fact displays larger fluctuations in the zone where dryout occurs, which are related to the presence of a liquid film drying up at the wall with some kind of an oscillating process. These temperature fluctuations are detected by means of the standard deviation in the wall temperature. These temperature fluctuations never appear during condensation tests, neither are present during flow boiling at low vapor qualities. The fluctuations also disappear in the postdryout zone. Experimental values of dryout quality measured with the above method are reported in this paper at mass velocity ranging between 100 and 700 kg m−2s−1 for R134a and between 200 and 900 kg m−2s−1 for R32. Since the heat flux is not uniform along the channel, each dryout point is characterized by its own boiling story. Nevertheless, an average value of heat flux can be defined in the channel, with the purpose of comparing it to critical heat flux values in uniformly heated channels. Present experimental data has been compared against some models available in the literature, which provide either the critical heat flux or the dryout quality in microchannels.

scholarly journals Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5987
Author(s):  
Jerol Soibam ◽  
Achref Rabhi ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis ◽  
Rebei Bel Fdhila
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Void Fraction ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Learning Model ◽  
Data Driven ◽  
Subcooled Boiling ◽  
Deep Learning Model ◽  
Quantities Of Interest

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

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