Heat transfer in pulsatile blood flow obeying Cross viscosity model through an artery with aneurysm

Mukhopadhyay, Subrata; Mandal, Mani Shankar; Mukhopadhyay, Swati

doi:10.1007/s10665-021-10172-w

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…As the typical pseudoplastic fluid, most of the PNCs fluids exhibit shear thinning behavior. − This is a prevalent phenomenon for pseudoplastic fluids in which the η of fluids decreases by orders of magnitude with increasing γ̇ as the structural organization was damaged by shear action. , The characteristic of the η–γ̇ relationship presented two-stage distinct regimes, crossovered at the critical shear rate γ̇ c = τ d –1 (where τ d is the terminal relaxation time), above which shear thinning was observed that could be described by the power law relationship, and below w hich the φ dependence (or T dependence) Newtonian plateau (η independent of γ̇) was seen. , Many rheological studies indicated that the power law exponent n varied in the range of 0.4–0.9. ,, For shear thinning, the γ̇ dependence of η under steady shear flow could be well characterized by Cross, , Carreau, , and Ostwald-de Waele models. In addition, numerous previous experimental − and simulation ,, studies have indicated that the η of PNCs decreases with the increase in T and increases with the increase in φ.…”

Section: Introductionmentioning

confidence: 99%

“…21,22 Many rheological studies indicated that the power law exponent n varied in the range of 0.4−0.9. 21,23,24 For shear thinning, the γ̇dependence of η under steady shear flow could be well characterized by Cross, 25,26 Carreau, 26,27 and Ostwaldde Waele 28 models. In addition, numerous previous experimental 29−33 and simulation 15,34,35 studies have indicated that the η of PNCs decreases with the increase in T and increases with the increase in φ.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Tian

Chen

et al. 2023

J. Phys. Chem. B

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Predicting the viscosity (η) of polymer nanocomposites (PNCs) is of critical importance as it governs a dominant role in PNCs’ processing and application. Machine-learning (ML) algorithms, enabled by pre-existing experimental and computational data, have emerged as robust tools for the prediction of quantitative relationships between feature parameters and various physical properties of materials. In this work, we employed nonequilibrium molecular dynamics (NEMD) simulation with ML models to systematically investigate the η of PNCs over a wide range of nanoparticle (NP) loadings (φ), shear rates (γ̇), and temperatures (T). With the increase in γ̇, shear thinning takes place as the value of η decreases on the orders of magnitude. In addition, the φ dependence and T dependence reduce to the extent that it is not visible at high γ̇. The value of η for PNCs is proportional to φ and inversely proportional to T below the intermediate γ̇. Using the obtained NEMD results, four machine-learning models were trained to provide effective predictions for the η. The extreme gradient boosting (XGBoost) model yields the best accuracy in η prediction under complex conditions and is further used to evaluate feature importance. This quantitative structure–property relationship (QSPR) model used physical views to investigate the effect of process parameters, such as T, φ, and γ̇, on the η of PNCs and paves the path for theoretically proposing reasonable parameters for successful processing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Tian

Chen

et al. 2023

J. Phys. Chem. B

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show abstract

“…[21,22] Many rheological studies indicated that the power law exponent n varied in the range of 0.4−0.9. [21,23,24] For shear thinning, the  -dependence of  under steady shear flow could be well characterized by Cross, [25,26], Carreau [26,27] and Ostwald-de Waele [28] models. In addition, numerous previous experimental [29][30][31][32][33] and simulation [15,34,35] studies have indicated that the  of PNCs decreases with the increase in T and increases with the increase in  .…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticles Loading with Molecular Dynamics Simulations and Machine Learning

Tian

Chen

et al. 2023

Preprint

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Predicting the viscosity of polymer nanocomposites (PNCs) is of critical importance as it governs a dominant role in PNCs processing and application. Machine learning (ML) algorithms, enabled by pre-existing experimental and computational data, have emerged as robust tools for the prediction of quantitative relationships between feature parameters and various physical properties of materials. In this work, we employed nonequilibrium molecular dynamics (NEMD) simulation with ML models to systematically investigate the viscosity of PNCs over a wide range of NPs loading, shear rates and temperature.With the increase in shear rates, shear thinning takes place as the value of viscosity decreases on the orders of magnitude. In addition, the NPs loading -dependence and T-dependence reduce to the extent that it is not visible at high shear rates. The value of viscosity for PNCs is proportional to NPs loading and inversely proportional to T below the intermediate shear rates. Using the obtained NEMD results, four machine learning models were trained to provides eective predictions for the viscosity. The extreme gradient boosting (XGBoost) model yields the best accuracy in viscosity prediction under complex conditions and is further used to evaluate feature importance. This quantitative structure-property relationship (QSPR) model used physical views to investigate the effect of process parameters, such as temperature, NPs loading and shear rates, on the viscosity of PNCs and paves the path for theoretically proposing reasonable parameters for successful processing.

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“…Mandal, M.S. et al [ 33 ] studied the cross-viscosity model in aneurysmal artery for pulsatile flow.…”

Section: Introductionmentioning

confidence: 99%

Effects of stenosis and aneurysm on blood flow in stenotic-aneurysmal artery

Hussain

Dar

Eldin

2023

Heliyon

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Heat transfer in pulsatile blood flow obeying Cross viscosity model through an artery with aneurysm

Cited by 7 publications

References 20 publications

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticles Loading with Molecular Dynamics Simulations and Machine Learning

Effects of stenosis and aneurysm on blood flow in stenotic-aneurysmal artery

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