A hybrid deep learning - CFD approach for modeling nanoparticles’ sedimentation processes for possible application in clean energy systems

Mesgarpour，Mehrdad2; Mahian，Omid3,4,5; Zhang，Ping1; Wongwises，Somchai2,6; Wang，Lian Ping7; Ahmadi，Goodarz8; Nižetić，Sandro9; Sheremet，Mikhail5; Shadloo，Mostafa Safdari10

2023-05-01

10.1016/j.jclepro.2023.136532

Journal of Cleaner Production   Impact Factor & Quartile

0959-6526

1879-1786

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.

Control finite volume (CFV) Deep neural networks Deposition process Lattice Boltzmann method (LBM) Nanoscale simulation

SCI

English

Others

National Science, Research and Innovation Fund (NSRF)[2022006] ; French regional computing center of Normandy CRIANN[ANR-20-CE92-0007-01] ; Deutsche Forschungsgemeinschaft (DFG)[Priority-2030] ; null[S3P19]

Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology

Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences

WOS:000956076800001

ELSEVIER SCI LTD

2-s2.0-85149880681

Scopus

1.School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guangxi,541004,China
2.Fluid Mechanics,Thermal Engineering and Multiphase Flow Research Lab. (FUTURE),Department of Mechanical Engineering,Faculty of Engineering,King Mongkut's University of Technology Thonburi (KMUTT),Bangkok,Bangmod,10140,Thailand
3.School of Chemical Engineering and Technology,Xi'an Jiaotong University,Xi'an,China
4.Department of Chemical Engineering,Imperial College London,London,SW7 2AZ,United Kingdom
5.Laboratory on Convective Heat and Mass Transfer,Tomsk State University,Tomsk,634050,Russian Federation
6.National Science and Technology Development Agency (NSTDA),Pathum Thani,12120,Thailand
7.Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Center for Complex Flows and Soft Matter Research,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
8.Department of Mechanical and Aerospace Engineering,Clarkson University,Potsdam,13699-5725,United States
9.University of Split,FESB,Split,Rudjera Boskovica 32,21000,Croatia
10.CORIA Laboratory CNRS-UMR 6614,Normandie University,CNRS & INSA of Rouen,Rouen,76000,France

Mesgarpour，Mehrdad,Mahian，Omid,Zhang，Ping,等. A hybrid deep learning - CFD approach for modeling nanoparticles’ sedimentation processes for possible application in clean energy systems[J]. Journal of Cleaner Production,2023,399.

Mesgarpour，Mehrdad.,Mahian，Omid.,Zhang，Ping.,Wongwises，Somchai.,Wang，Lian Ping.,...&Shadloo，Mostafa Safdari.(2023).A hybrid deep learning - CFD approach for modeling nanoparticles’ sedimentation processes for possible application in clean energy systems.Journal of Cleaner Production,399.

Mesgarpour，Mehrdad,et al."A hybrid deep learning - CFD approach for modeling nanoparticles’ sedimentation processes for possible application in clean energy systems".Journal of Cleaner Production 399(2023).