Numerical Approaches to Complex Fluids

Rosti, Marco E.; Picano, Francesco; Brandt, Luca

doi:10.1007/978-3-030-23370-9_1

Cited by 6 publications

(3 citation statements)

References 134 publications

(150 reference statements)

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“…The numerical simulations were performed using the open-source framework RheoTool 51 integrated with OpenFOAM, 52 where the equations were discretized using a finite volume method and the log-conformation approach was used to calculate the polymeric stress tensor. 53,54 The relationship between the polymeric stress tensor, σ p , and the log-conformation tensor, Θ , is given as:…”

Section: Methodsmentioning

confidence: 99%

Stress and stretching regulate dispersion in viscoelastic porous media flows

Kumar,

Walkama,

Ardekani

et al. 2023

Soft Matter

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Section: Methodsmentioning

confidence: 99%

Stress and stretching regulate dispersion in viscoelastic porous media flows

Kumar,

Walkama,

Ardekani

et al. 2023

Soft Matter

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“…Where, 𝜙 0 is the levelset function prior to reshaping, and 𝑆(𝜙 0 ) is a smoothed-out sign function, and t is the pseudo time. The 𝑆(𝜙 0 ) can numerically approximated [24] as:…”

Section: Levelset Methods Conceptmentioning

confidence: 99%

Improve The Fully Convolutional Network Accuracy by Levelset and The Deep Prior Method

Hussein

Altyar

Abdulmunem

2023

Iraqi Journal of Science

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Deep learning techniques allow us to achieve image segmentation with excellent accuracy and speed. However, challenges in several image classification areas, including medical imaging and materials science, are usually complicated as these complex models may have difficulty learning significant image features that would allow extension to newer datasets. In this study, an enhancing technique for object detection is proposed based on deep conventional neural networks by combining levelset and standard shape mask. First, a standard shape mask is created through the "probability" shape using the global transformation technique, then the image, the mask, and the probability map are used as the levelset input to apply the image segmentation. The test results show that when using the proposed method with DCNN, it can achieve a close segmentation area and extract features with higher detail than traditional segmentation. The proposed model achieved 94.43% in precision and 95.91% in recall %, so it got 95.16% in F1-score. When comparing the proposed model with the same CNN model without Levelset, the result shows that the proposed model achieved accuracy of 0.951, which is higher than CNN model without Levelset that achieved 0.902.

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“…This is due to the fact that the effective viscosity diverges to infinite shear rates and zero shear rates for shear thinning and shear thickening fluid. When solving non-Newtonian flow numerically, it may result in significantly time step constraint when explicit schemes are used, and disrupt the solution technique usually used to solve implicitly the viscous terms in the momentum equation [64]. Several methods have been used to solve the problem analogous in this work.…”

Section: Introductionmentioning

confidence: 99%

Numerical Solution of Biomagnetic Power-Law Fluid Flow and Heat Transfer in a Channel

2020

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The effect of non-Newtonian biomagnetic power-law fluid in a channel undergoing external localised magnetic fields is investigated. The governing equations are derived by considering both effects of Ferrohydrodynamics (FHD) and Magnetohydrodynamics (MHD). These governing equations are difficult to solve due to the inclusion of source term from magnetic equation and the nonlinearity of the power-law model. Numerical scheme of Constrained Interpolation Profile (CIP) is developed to solve the governing equations numerically. Extensive results carried out show that this method is efficient on studying the biomagnetic and non-Newtonian power-law flow. New results show that the inclusion of power-law model affects the vortex formation, skin friction and heat transfer parameter significantly. Regardless of the power-law index, the vortex formation length increases when Magnetic number increases. The effect of this vortex however decreases with the inclusion of power-law where in the shear thinning case, the arising vortex is more pronounced than in the shear thickening case. Furthermore, increasing of power-law index from shear thinning to shear thickening, decreases the wall shear stress and heat transfer parameters. However for high Magnetic number, the wall shear stress and heat transfer parameters increase especially near the location of the magnetic source. The results can be used as a guide on assessing the potential effects of radiofrequency fields (RF) from electromagnetic fields (EMF) exposure on blood vessel.

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Numerical Approaches to Complex Fluids

Cited by 6 publications

References 134 publications

Stress and stretching regulate dispersion in viscoelastic porous media flows

Stress and stretching regulate dispersion in viscoelastic porous media flows

Improve The Fully Convolutional Network Accuracy by Levelset and The Deep Prior Method

Numerical Solution of Biomagnetic Power-Law Fluid Flow and Heat Transfer in a Channel

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