SADI approach programming on GPU: convective heat transfer of nanofluids flow inside a wavy channel

In this study, the numerical simulation of convective heat transfer of nanofluids (Al 2 O 3 /water and CuO/water) inside a sinusoidal wavy channel is performed using the Graphics Processing Units (GPU). The governing equations including stream-function, vorticity transport, and energy are discretize...

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Veröffentlicht in:Journal of thermal analysis and calorimetry Jg. 146; H. 1; S. 31 - 46
Hauptverfasser: Taghavi, S. M. H., Akbarzadeh, P., Mahmoodi Darian, H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.10.2021
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Aluminum oxide
Analytical Chemistry
Central processing units
Chemistry
Chemistry and Materials Science
Coefficient of friction
Computational fluid dynamics
Convective heat transfer
CPUs
Fluid flow
Graphics processing units
Inorganic Chemistry
Mathematical analysis
Mathematical models
Measurement Science and Instrumentation
Microprocessors
Nanofluids
Nanoparticles
Numerical analysis
Physical Chemistry
Polymer Sciences
Reynolds number
Run time (computers)
Skin friction
Spherical coordinates
Vorticity
SADI
CUDA Platform
GPU
Wavy wall channel
Nanofluids
ISSN:1388-6150, 1588-2926
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Beschreibung
Zusammenfassung:In this study, the numerical simulation of convective heat transfer of nanofluids (Al 2 O 3 /water and CuO/water) inside a sinusoidal wavy channel is performed using the Graphics Processing Units (GPU). The governing equations including stream-function, vorticity transport, and energy are discretized using the fourth-order Spline Alternating-Direction Implicit (SADI) approach in combination with the curvilinear coordinates mapping. The final tridiagonal-matrices are solved by Parallel-Thomas-Algorithm (PTA) on GPU. A homogenous one-phase model is also applied to consider the effective characteristics of the nanofluids flow. In the first part of the results section, the effects of nanoparticle volume fraction, Reynolds number, and amplitude of the wavy wall on average Nusselt number and the skin friction coefficient of the channel are investigated. In the second part of the results section, the ability of GPU to accelerate the computation (or runtimes) is compared to the classic Thomas algorithm that runs on a Central Processing Unit (CPU). The results demonstrate that the speedup of PTA against CPU runtime for the finest grid is around 18.32×.
Bibliographie:ObjectType-Article-1
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ISSN:1388-6150
1588-2926
DOI:10.1007/s10973-020-09924-0