RT Journal Article
T1 Flow and heat transfer analysis of a gas-particle fluidized dense suspension in a tube for CSP applications
A1 Córcoles, J. I.
A1 Díaz-Heras, M.
A1 Fernández Torrijos, María
A1 Almendros Ibáñez, José Antonio
AB This work presents a numerical study of the flow of particles in a gas–particle fluidized dense suspension for CSP applications using the Multi-Phase Particle in Cell (MP-PIC) method, implemented in CPFD-Barracuda software. The study covers two different numerical simulations. The first is a cold and isothermal model in which the fluctuations and control of the mass flow of particles ascending along the vertical tube was studied. In the second, a high-temperature boundary condition was imposed on the external surface of the tube and the energy equation was solved. In this second case, the heat transfer coefficient between the inner surface of the tube and the particles was numerically computed. The numerical results in the cold model are highly consistent with experimental data available in the literature (with values up to 150 kg/h and differences of approximately ±10 kg/h) and underline the significant impact of the pressure at the bottom of the bed and of the aeration flow rate on the mass flow of particles. The results of the non-isothermal case present heat transfer coefficients in the range of 300–400 W∕(m2 K) with transient fluctuations during the fluidization process. These fluctuations may be an influence on the mechanical damage of the tube, which is exposed to high levels of concentrated irradiation.
PB Elsevier
SN 0960-1481
YR 2023
FD 2023-04-01
LK https://hdl.handle.net/10016/38911
UL https://hdl.handle.net/10016/38911
LA eng
NO This work was partially funded by the project PID2021-127322OB-I00, funded by the Ministerio de Ciencia e Innovación MICIN/AEI/10.13039/501100011033/ and by FEDER Una manera de hacer Europa; Project SBPLY/21/180501/000017, funded by the Regional Government of Castilla-La Mancha and by FEDER Una manera de hacer Europa; Project RED2018-102431-T, funded by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) and Project 2020-GRIN-28725, funded by Universidad de Castilla-La Mancha .
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RD 18 jul. 2024