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Research Papers: Evaporation, Boiling, and Condensation

Computational Fluid Dynamics Analysis of the Transient Cooling of the Boiling Surface at Bubble Departure

[+] Author and Article Information
Giovanni Giustini

Department of Mechanical Engineering,
Imperial College London,
Exhibition Road,
London SW7 2AZ, UK
e-mail: g.giustini12@imperial.ac.uk

S. P. Walker

Department of Mechanical Engineering,
Imperial College London,
Exhibition Road,
London SW7 2AZ, UK

Yohei Sato, Bojan Niceno

Laboratory for Thermal Hydraulics,
Nuclear Energy and Safety Department,
Paul Scherrer Institute,
Villigen 5232, Switzerland

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 2, 2016; final manuscript received April 13, 2017; published online May 16, 2017. Assoc. Editor: Debjyoti Banerjee.

J. Heat Transfer 139(9), 091501 (May 16, 2017) (15 pages) Paper No: HT-16-1781; doi: 10.1115/1.4036572 History: Received December 02, 2016; Revised April 13, 2017

Component-scale computational fluid dynamics (CFD) modeling of boiling via heat flux partitioning relies upon empirical and semimechanistic representations of the modes of heat transfer believed to be important. One such mode, “quenching,” refers to the bringing of cool water to the vicinity of the heated wall to refill the volume occupied by a departing vapor bubble. This is modeled in classical heat flux partitioning approaches using a semimechanistic treatment based on idealized transient heat conduction into liquid from a perfectly conducting substrate. In this paper, we apply a modern interface tracking CFD approach to simulate steam bubble growth and departure, in an attempt to assess mechanistically (within the limitations of the CFD model) the single-phase heat transfer associated with bubble departure. This is in the spirit of one of the main motivations for such mechanistic modeling, the development of insight, and the provision of quantification, to improve the necessarily more empirical component scale modeling. The computations indicate that the long-standing “quench” model used in essentially all heat flux partitioning treatments embodies a significant overestimate of this part of the heat transfer, by a factor of perhaps ∼30. It is of course the case that the collection of individual models in heat flux partitioning treatments has been refined and tuned in aggregate, and it is not particularly surprising that an individual submodel is not numerically correct. In practice, there is much cancelation between inaccuracies in the various submodels, which in aggregate perform surprisingly well. We suggest ways in which this more soundly based quantification of “quenching heat transfer” might be taken into account in component scale modeling.

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Figures

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Fig. 1

Components of the RPI-partitioned heat flux

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Fig. 2

RPI quench heat flux component

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Fig. 3

Physical model of steam bubble release from a single nucleation site. The model includes conjugate heat transfer with the substrate.

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Fig. 4

The left panel shows the local heat flow due to transient conduction to fluid at the remote temperature, following bubble release, computed by applying the model of Han and Griffith. The right panel shows the energy transfer across the area where the transient conduction heat transfer mechanism takes place, also computed by applying the model of Han and Griffith.

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Fig. 5

CFD model of pool boiling: dimensions of computational domain. Also shown is a typical observed shape of the liquid–vapor interface, denoted by a red line, the fixed mesh used to track its motion, and the solid–fluid boundary, denoted by a black line (see figure online for color).

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Fig. 6

Showing values of the energy transfer to the liquid during quench of the solid surface, extracted 10 ms after bubble departure, for various levels of discretization. The finest mesh, using cubic cells of 15.65 μm in size in the fluid, and solid cells of thickness equal to 7.68 μm, consists of 36,860,928 cells.

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Fig. 7

Bubble shape, and temperature distribution in the solid, as predicted by the CFD method. These are shown together with the microlayer thickness, as computed by the dedicated algebraic submodel. Note the length scale; bubble size is a few millimeters, much larger than the microlayer thickness.

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Fig. 8

Time histories of bubble size for different thermal conductivities of the substrate

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Fig. 9

Bubble shape and temperature distribution in the solid and in the fluid at a vertical plane. From top left to bottom right, times are 0, 6, 12, 18, 22, and 36 ms after nucleation.

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Fig. 10

Velocity and temperature field during lift-off, preceded by the reduction in bubble base radius and quenching proper. The vapor–liquid interface, seen after 16 and 20 ms into the simulation, is represented as a red line. Only the fluid domain is shown in the diagram. The temperature field, in degrees Celsius, is shown in gray-scale, velocity vectors are colored according to the their magnitude (in m/s). Small flow velocities are observed in the image taken at 36 ms, showing superheated liquid (light gray) in motion toward the nucleation site (see figure online for color).

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Fig. 11

Example of temporal variation of typical radial heat flux profiles at the solid–fluid interface during bubble growth (top panel), bubble detachment (middle panel), and after bubble lift-off (bottom panel)

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Fig. 12

Showing the temporal evolution of bubble shape, and radial profiles of the wall-normal heat flux component, evaluated at the solid–fluid interface during rewet of the dry patch

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Fig. 13

Illustration of the procedure used to compute the quenching flow of heat

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Fig. 14

Area of influence of the quench energy transfer

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Fig. 15

Quench energy transfer as function of substrate thermophysical properties

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Fig. 16

Quench energy fluence for different thermal conductivity values

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Fig. 17

Spatial and temporal variation of the heat flux at the solid–liquid interface during rewet of a high thermal conductivity surface

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Fig. 18

Comparison between energy fluence from CFD analysis and RPI partitioned energy fluence, for the case of a poorly conducting substrate

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Fig. 19

Axial temperature variations in the solid and in the liquid at a fixed radial distance from the nucleation site. Negative z-values indicate the solid, positive z locations are in the liquid.

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Fig. 20

Radial profiles of heat flux at the solid surface from ITM and RPI modeling

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Fig. 21

Comparison between energy transfer from mechanistic simulation and RPI partitioned energy transfer, for the case of a poorly conducting substrate

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Fig. 22

Comparison between energy fluence from CFD analysis and RPI partitioned energy fluence, for the case of a high-conductivity substrate

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Fig. 23

Comparison between energy transfer from mechanistic simulation and RPI partitioned energy transfer, for the case of a high-conductivity substrate

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Fig. 24

Effect of bubble-affected radius and driving temperature difference on quench energy transfer

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Fig. 25

Comparison between energy fluence from CFD analysis and modified RPI partitioned energy fluence

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Fig. 26

Comparison between energy transfer from mechanistic simulation and modified RPI partitioned energy transfer

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