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• W4321369724 endingPage "136532" @default.
• W4321369724 startingPage "136532" @default.
• W4321369724 abstract "Sedimentation directly affects the thermal performance and efficiency of thermal systems such as boilers, heat exchangers, and solar collectors. This work investigates the effect of nanoparticles deposition inside a tube with possible application in parabolic solar collectors. This study combines the lattice Boltzmann (LBM) and the control finite volume (CFV) methods for a realistic simulation of nanoparticles deposition for the first time. While the bulk flow is solved using the CFV method, the flow behavior in the deposition layer is evaluated using the LBM model. Nanoparticle movements are also captured using dynamic mesh refinement in CFV in order to accurately predict their behavior. The numerical results are then used for training a deep feed-forward neural network with appropriate boundary conditions (DFNN-BC) to visualize and predict the transient sedimentation behavior. The prediction includes (i) representation of nanoparticles in the LB domain while it is trained during the particle movement in the FV domain and (ii) extension of the computational domain in space, which is three times bigger than the training domain. DFNN-BC is used to study the heat transfer and fluid flow characteristics for Reynolds numbers ranging from 12 to 50 where the working fluid is a nanofluid. The results indicated that using DFNN-BC can reduce the calculation time by 80% compared to the case where the entire domain is solved numerically. The results show that deposition has a maximum effect of 0.32% on the average velocity ratio (AVR) at Re = 12. This variation is related to the viscosity and shear stress of the fluid. With an increment in Reynolds number, the AVR decreases to 0.12%. This is because of the decrement in the number of sedimented nanoparticles. In addition, increasing the velocity significantly affects the rate of sedimentation and volume fraction ratio. It is also seen that the fluid's velocity and density increase by 8.69% and 6.53%, respectively, whereas the viscosity decreases by 7.74%. The findings of this study provide a better understanding of the details of the sedimentation process, such as particle behavior and variation in parameters near the surface, like concentration, thermal conductivity, and viscosity of the sedimentation and the formation of a deposition layer in fluid–particle multiphase flows. This, in turn, is expected to lead to cost savings in maintenance through more precise predictions of service periods for heat transfer equipment." @default.
• W4321369724 created "2023-02-21" @default.
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• W4321369724 date "2023-05-01" @default.
• W4321369724 modified "2023-10-06" @default.
• W4321369724 title "A hybrid deep learning - CFD approach for modeling nanoparticles’ sedimentation processes for possible application in clean energy systems" @default.
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• W4321369724 doi "https://doi.org/10.1016/j.jclepro.2023.136532" @default.
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