Experiments in Pebble Bed Heat Transfer

2020 ◽  
pp. 121-159
Author(s):  
Shengyao Jiang ◽  
Jiyuan Tu ◽  
Xingtuan Yang ◽  
Nan Gui
Keyword(s):  
Heat Transfer ◽  
Pebble Bed

scholarly journals Evaluation of radiation heat transfer in porous medial: Application for a pebble bed modular reactor cooled by CO2 gas

2013 ◽  
Vol 28 (2) ◽  
pp. 118-127
Author(s):  
Kamel Sidi-Ali ◽  
Khaled Oukil ◽  
Tinhinane Hassani ◽  
Yasmina Amri ◽  
Abdelmoumane Alem
Keyword(s):  
Heat Transfer ◽  
Porous Medium ◽  
Free Convection ◽  
Radiation Heat Transfer ◽  
Convection Heat Transfer ◽  
Emergency Situation ◽  
System Of Equations ◽  
Radiation Heat ◽  
Pebble Bed ◽  
Free Convection Heat

This work analyses the contribution of radiation heat transfer in the cooling of a pebble bed modular reactor. The mathematical model, developed for a porous medium, is based on a set of equations applied to an annular geometry. Previous major works dealing with the subject have considered the forced convection mode and often did not take into account the radiation heat transfer. In this work, only free convection and radiation heat transfer are considered. This can occur during the removal of residual heat after shutdown or during an emergency situation. In order to derive the governing equations of radiation heat transfer, a steady-state in an isotropic and emissive porous medium (CO2) is considered. The obtained system of equations is written in a dimensionless form and then solved. In order to evaluate the effect of radiation heat transfer on the total heat removed, an analytical method for solving the system of equations is used. The results allow quantifying both radiation and free convection heat transfer. For the studied situation, they show that, in a pebble bed modular reactor, more than 70% of heat is removed by radiation heat transfer when CO2 is used as the coolant gas.

scholarly journals A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed

2020 ◽  
Vol 34 (01) ◽  
pp. 1029-1036
Author(s):  
Hao Wu ◽  
Shuang Hao
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Radiation ◽  
Radiative Heat Transfer ◽  
Power Plants ◽  
Deep Neural Network ◽  
Radiative Heat ◽  
Particle Packing ◽  
View Factor ◽  
Pebble Bed

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.

Progress in the design development of EU DEMO helium-cooled pebble bed primary heat transfer system

2019 ◽  
Vol 146 ◽  
pp. 2416-2420 ◽  
Author(s):  
I. Moscato ◽  
L. Barucca ◽  
S. Ciattaglia ◽  
F. D’Aleo ◽  
P.A. Di Maio ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Transfer System ◽  
Design Development ◽  
Pebble Bed ◽  
Heat Transfer System

Combined CFD and DEM Simulations of Fluid Flow and Heat Transfer in a Pebble Bed Reactor

2011 ◽  
Author(s):  
Xiang Zhao ◽  
Trent Montgomery ◽  
Sijun Zhang
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Discrete Element Method ◽  
Reactor Core ◽  
Turbulence Models ◽  
Discrete Element ◽  
Pebble Bed ◽  
Pebble Bed Reactor ◽  
Packing Structure ◽  
Dem Simulations

This paper presents combined computational fluid dynamics (CFD) and discrete element method (DEM) simulations of fluid flow and relevant heat transfer in the pebble bed reactor core. In the pebble bed reactor core, the coolant passes highly complicated flow channels, which are formed by thousands of pebbles in a random way. The random packing structure of pebbles is crucial to CFD simulations results. The realistic packing structure in an entire pebble bed reactor (PBR) is generated by discrete element method (DEM). While in CFD calculations, selection of the turbulence models have great importance in accuracy and capturing the details of the flow features, in our numerical simulations both large eddy simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) models are employed to investigate the effects of different turbulence models on gas flow field and relevant heat transfer. The calculations indicate the complex flow structure within the voids between the pebbles.

Research on Heat Transfer Characteristics of the Thermometric Sphere in HTR-10

2018 ◽  
Author(s):  
Shiyan Sun ◽  
Youjie Zhang ◽  
Yanhua Zheng ◽  
Xiang Fang ◽  
Xiaoyong Yang
Keyword(s):  
Heat Transfer ◽  
Thermal Equilibrium ◽  
Reactor Core ◽  
Fluid Temperature ◽  
Heat Transfer Characteristics ◽  
Pebble Bed ◽  
The Core ◽  
Temperature Measuring ◽  
Transfer Characteristics ◽  
Metal Wires

During the operation of the High Temperature Gas-cooled Reactor (HTGR), the hot-spot temperature in the reactor core must be lower than the maximum permissible temperature of the fuel elements and the materials of construction, so that the reactor kept safe. However, no fixed temperature-measuring devices can be set in a pebble-bed core. A special spherical temperature-measuring device is adopted to make sure it brings as small impact to the reactor operation as possible. There are several metal wires with different melting points inside. The graphite thermometric balls will be put onto the top of HTR-10 reactor core, and they record and reflect the highest temperature in different positions in the core when flowing in the pebble bed. Before the reactor core temperature-measuring experiment of HTR-10, we must study the heat transfer characteristics of the graphite thermometric sphere to find out the relationship of the melting conditions and the temperature in the reactor core. A 3-D model of the graphite thermometric ball is established, and CFD method is adopted to research and figure out the thermal equilibrium time and temperature difference between the metal wires in the ball and the hot fluid outside the balls. Multiple situations are simulated, and the heat transfer process of the thermometric sphere is comprehensively studied. The heat convection is certified the most important aspect. Thermal equilibrium can be achieved within 19 minutes, far shorter than the period while the spheres flowing through the core. The simulation results can also applied to derive the thermal fluid temperature backward.

Simulation of Multiphase Heat Transfer in Pebble Bed Modular Reactor

2004 ◽  
Author(s):  
Andrey A. Troshko ◽  
Ajey Y. Walavalkar
Keyword(s):  
Heat Transfer ◽  
Gas Phase ◽  
Packed Bed ◽  
Multiphase Model ◽  
Test Rig ◽  
Pebble Bed ◽  
Model Predictions ◽  
Two Phases ◽  
Drag Law ◽  
Eulerian Multiphase Model

Computational Fluid Dynamics in conjunction with an Eulerian multiphase model of heat transfer in a Pebble Bed Modular Reactor (PBMR) was validated against experimental data obtained in a test rig. The cooling gas and packed fuel pebbles constituted two phases. The velocity of pebble phase was fixed to zero and a drag law accounting for a packed bed condition was used. The density of the gas phase varied with temperature. Volume averaged effective thermal conductivities accounting for radiation and packed spheres geometry were used for both phases. Model predictions compared favorably with the experiment for two gases — helium and nitrogen and two power levels. It was found that accounting for increased affective porosity close to walls results in more realistic velocity field prediction.

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