Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization

Abad, Javad Mohebbi Najm; Alizadeh, Rasool; Fattahi, Abolfazl; Doranehgard, Mohammad Hossein; Hajri, Ebrahim Al; Karimi, Nader

doi:10.1016/j.molliq.2020.113492

Cited by 75 publications

(21 citation statements)

References 60 publications

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“…s 1 and s 2 indicate CNT nanotubes and Cu nanoparticles, individually. More details of the values for different shapes of nanoparticles can be found in ref . Table expresses the thermophysical properties of water, CNT nanotubes, and Cu nanoparticles.…”

Section: Numerical and Theoretical Methodsmentioning

confidence: 99%

“…Mutual information (MI) is one statistical dependency criterion used in this method. In each step of the algorithm, mRMR tries to choose the proper feature that has the maximum MI with the model output and minimum MI with the set of selected features before that. − The MI between two features of x and y is calculated by the following equation: where p ( x ) and p ( y ) are the probability density functions of features x and y , respectively. p ( x , y ) shows the simultaneous occurrence of both variables.…”

Section: Estimator and Optimizer Algorithmsmentioning

confidence: 99%

“…This method has been shown to be most useful for the analysis of computationally expensive problems, see for example refs and . By developing hard and soft reservoirs of computing machines, ML has become a vital complement for experimental, computational, and theoretical fluid dynamics. , In recent years, some research has been performed using machine learning together with computational fluid dynamics (CFD) problems. − This predictive tool can significantly push the boundaries of classical approaches and allows conduction of more sophisticated analysis.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Enhanced Analysis of Mixed Biothermal Convection of Single Particle and Hybrid Nanofluids within a Complex Configuration

Alizadeh

Abad

Fattahi

et al. 2021

Ind. Eng. Chem. Res.

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Transport phenomena in a hybrid or single-particle nanofluid over a conical body embedded inside a porous medium are investigated. The fluid contains homogeneously mixed nanoparticles and live cells that are able to migrate, collectively sculpturing a thermo-biosolutal system. Transport processes including mixed convection as well as species and cell transfer are simulated using a similarity technique. As the problem involves a large number of parameters with complicated interactions, machine learning is applied to predict a wide range of parametric variations. The simulation data are used to build an intelligent tool based on an artificial neural network to predict the behavior of the system. This also aids the development of precise correlations for nondimensional parameters dominating the transport phenomena. The results indicate that lower values of the motile Lewis number and a higher mixed convection parameter enhance the Nusselt number. However, it is contained respectively by the increment of the Peclet number and increases in the bio Rayleigh number. It is further shown that an increase in the Prandtl number enhances the Sherwood number and makes the motile microorganisms more uniform. The Peclet number directly influences the transport of heat, mass, and microorganisms. This study clearly demonstrates the abilities of combining numerical simulations with machine learning to significantly extend and enrich analysis of problems with large numbers of variables. The findings also pave the way for predicting behaviors of complex thermo-biosolutal systems without resorting to computationally demanding simulations.

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Section: Numerical and Theoretical Methodsmentioning

confidence: 99%

Section: Estimator and Optimizer Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine-Learning Enhanced Analysis of Mixed Biothermal Convection of Single Particle and Hybrid Nanofluids within a Complex Configuration

Alizadeh

Abad

Fattahi

et al. 2021

Ind. Eng. Chem. Res.

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“…This will lead to the development of a data-driven model that uses the computationally generated data for a small section of the domain and extrapolates those. The effectiveness of such combined approach has been already demonstrated in the context of propulsion and process engineering ( Christodoulou et al, 2020 , Alizadeh et al, 2020a , Mohebbi Najm Abad et al, 2020 , Alizadeh et al, 2020b ). It was shown that data-driven approaches could predict complicated spatiotemporal behaviors, while high fidelity computation was performed only for a small fraction of the domain ( Christodoulou et al, 2020 ).…”

Section: Introductionmentioning

confidence: 95%

Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach

Mesgarpour

Abad

Alizadeh

et al. 2021

Journal of Hazardous Materials

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Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus.

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“…17 An analytic study of heat and mass transport over an axisymmetric thick wall microchannel with an iso-flux thermal boundary constraint appointed on the outer surface and thermodynamics of porous microreactors are done by Hunt et al 18 The heat and mass transfer was studied by Alizadeh et al 19,20 for the flow from the surface of a cylinder coated with a catalyst and circumferentially non-uniform transpiration also it is embedded inside a homogeneous porous medium. Heat transfer and thermodynamics of an impinging nanofluid flow upon a cylinder, embedded in a porous media, with constant surface temperature are inspected and a considerable effect of nanoparticle concentration was observed by Gomari et al 21 A reacting hybrid nanofluid flow over a bluff-body embedded in porous media was analyzed by Mohebbi Najm Abad et al 22 They used an artificial neural network to overcome the complexity of the problem.…”

Section: Introductionmentioning

confidence: 99%

An exact solution for unsteady free convection flow of chemically reacting Al2O3−SiO2/water hybrid nanofluid

Suganya

Muthtamilselvan

Al-Amri

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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An unsteady natural convective boundary layer fluid flow of thick incompressible [Formula: see text] water hybrid nanofluid is discussed over an oscillating permeable stretching surface. The radiative and chemically reactive hybrid nanofluid flow is induced in rotating coordinates. Essential thermophysical properties of hybrid nanofluid are utilized. The boundary layer equations governing the present phenomena are converted into nondimensional form using appropriate dimension free transformations. The non dimensionalized partial differential equations are then solved analytically by Laplace transform technique and the exact solutions are attained. The performance of all the involving parameters are surveyed and the set of mathematical outputs for temperature, velocity and concentration are graphically depicted. The expressions for skin friction, Nusselt number and Sherwood number have been derived and the numerical results are displayed through tables and graphs. Growth of heat transfer rate is observed by rising radiation parameter and frequency of oscillation. Important aspects of the present study in the physical ground are examined.

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Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization

Cited by 75 publications

References 60 publications

Machine-Learning Enhanced Analysis of Mixed Biothermal Convection of Single Particle and Hybrid Nanofluids within a Complex Configuration

Machine-Learning Enhanced Analysis of Mixed Biothermal Convection of Single Particle and Hybrid Nanofluids within a Complex Configuration

Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach

An exact solution for unsteady free convection flow of chemically reacting Al2O3−SiO2/water hybrid nanofluid

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