A Graphics Process Unit-Based Multiple-Relaxation-Time Lattice Boltzmann Simulation of Non-Newtonian Fluid Flows in a Backward Facing Step

Molla, Md. Mamun; Nag, Preetom; Thohura, Sharaban; Khan, Amirul

doi:10.3390/computation8030083

Cited by 21 publications

(8 citation statements)

References 58 publications

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“…A singularity-free modified version of the power-law viscosity model is used as in [54,59,60] and the formula is given below:…”

Section: Mathematical Formulation Of Mrt-lbmmentioning

confidence: 99%

Mesoscopic simulation of MHD mixed convection of non-newtonian ferrofluids with a non-uniformly heated plate in an enclosure

Hossain¹,

Nag²,

Molla³

2022

Phys. Scr.

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Mixed convective study has been popular in recent years because of its large applications, including the cooling of electronic devices, furnaces, lubrication technologies, high-performance building insulation, multi-shield structures used in nuclear reactors, food processing, glass manufacturing, solar power collectors, drying technologies, chemical processing equipment, and others involve mixed convection in a lid-driven cavity flow problems. Graphics process unit (GPU) based multiple-relaxation-time(MRT) lattice Boltzmann method (LBM) has been employed for investigating the numerical simulation of magnetohydrodynamic(MHD) mixed convection with a non-uniformly heated plate at the mid of an enclosure. The physical model consists of a two-dimensional square enclosure with the top wall moving at a constant speed. Thermally adiabatic conditions are imposed on the top and bottom walls, while the two vertical walls are cold. In the center of the enclosure, a plate has been placed that is non-uniformly heated. A magnetic field is applied with different angles of inclination. Numerical simulations were performed for various influential parameters such as Richardson number ($Ri$), Hartmann number ($Ha$), power-law index ($n$), ferroparticles volume fraction ($\phi$), magnetic field angle ($\gamma$) to study the flow phenomena in terms of the velocity and temperature distributions as well as streamlines and isotherms, respectively. The present study also investigates entropy generation due to the convective heat transfer flow for industrial purposes. The results reveal that as the Richardson number rises, the average Nusselt number rises, and as the Hartmann number rises, the average Nusselt number reduces. Furthermore, it is found that the average Nusselt number is inversely proportional to the power-law index. Total entropy generation increases with the increase of the power-law index and Richardson number. Entropy due to fluid friction, heat transfer, and total entropy shows a maximum at $\gamma=90^\circ$.

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“…A singularity-free modified version of the power-law viscosity model is used as in [54,59,60] and the formula is given below:…”

Section: Mathematical Formulation Of Mrt-lbmmentioning

confidence: 99%

Mesoscopic simulation of MHD mixed convection of non-newtonian ferrofluids with a non-uniformly heated plate in an enclosure

Hossain¹,

Nag²,

Molla³

2022

Phys. Scr.

Self Cite

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“…Non-newtonian viscosity model for the LBM A modified power-law viscosity model is used here as like [53,54] and is given in (40):…”

Section: Boundary Conditionsmentioning

confidence: 99%

Multiple-relaxation-time lattice Boltzmann simulation of free convection and irreversibility of nanofluid with variable thermophysical properties

et al. 2021

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This numerical study demonstrates heat transfer and irreversibility or entropy generation of non-Newtonian power-law Al2O3-H2O (aluminum oxide-water) nanofluids in a square enclosure using multiple-relaxation-time lattice Boltzmann method accelerated by graphics processing unit computing. In this investigation, the effective thermal conductivity and viscosity are variables, and they depend on the fluid temperature and rate of strain, respectively. The enclosure’s left and right walls are uniformly heated with different temperatures, and the upper and lower walls are thermally adiabatic. There is no valid study and results on non-Newtonian fluid using multiple-relaxation-time lattice Boltzmann method for this configuration and hence the novelty of the present results have been ensured. This paper has formulated and appropriately validated the Newtonian and non-Newtonian natural convection problem with the available numerical results. This study includes a set of comprehensive simulations, showing the effects of these fluids’ natural convection by varying three key parameters: the Rayleigh number, the volume fraction of nanoparticles, and the power-law index on the streamlines, isotherms, local and average Nusselt number as well as the local and total entropy generation. The results show that increasing the volume fraction of the nanoparticles from 0% to 2%, the average rate of heat transfer and the total entropy generation increase 6.5% and 7.4%, respectively, while the Rayleigh number, Ra = 105 and the power-law index n = 0.6.

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“…The CUDA (compute unified device architecture) is the most popular programming platform which enables us to use general-purpose programming languages (e.g., C++) for compiling GPU programs. The programming model uses the single instruction multiple thread (SIMT) principle [119]. We can declare a function in the CUDA program a "kernel" function and run this function on the steaming multiprocessors.…”

Section: Cuda Implementationmentioning

confidence: 99%

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Kazakovtsev¹,

Rozhnov²,

Popov³

et al. 2020

Computation

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The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

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A Graphics Process Unit-Based Multiple-Relaxation-Time Lattice Boltzmann Simulation of Non-Newtonian Fluid Flows in a Backward Facing Step

Cited by 21 publications

References 58 publications

Mesoscopic simulation of MHD mixed convection of non-newtonian ferrofluids with a non-uniformly heated plate in an enclosure

Mesoscopic simulation of MHD mixed convection of non-newtonian ferrofluids with a non-uniformly heated plate in an enclosure

Multiple-relaxation-time lattice Boltzmann simulation of free convection and irreversibility of nanofluid with variable thermophysical properties

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

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