Artificial Neural Network modelling for the System of blood flow through tapered
artery with mild stenosis

Jyoti Kumar Arora

doi:https://doi.org/10.14445/22315373/IJMTT-V2I1P502

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 2 | Issue 1 | Year 2011 | Article Id. IJMTT-V2I1P502 | DOI : https://doi.org/10.14445/22315373/IJMTT-V2I1P502

Artificial Neural Network modelling for the System of blood flow through tapered artery with mild stenosis

Jyoti Kumar Arora

Citation :

Jyoti Kumar Arora, "Artificial Neural Network modelling for the System of blood flow through tapered artery with mild stenosis," International Journal of Mathematics Trends and Technology (IJMTT), vol. 2, no. 1, pp. 1-5, 2011. Crossref, https://doi.org/10.14445/22315373/IJMTT-V2I1P502

Abstract

ANN model was designed to study the effect of blood flow and cross sectional area through tapered artery with mild stenosis , considering blood flow as a two-fluid model with the suspension of all the erythrocytes in the core region as Herschel-Bulkley fluid and the plasma in the peripheral layer as Newtonian fluid. Different ANN architecture were tested by varying network topology, resulting into an excellent agreement between the experimental data and the analytical values. Various experimental parameters i.e. stenosis height, peripheral layer thickness, yield stress, viscosity ratio, angle of tapering and power law index were used for ANN modelling and their effect on the velocity, wall shear stress, flow rate and the longitudinal impedance are analysed. It is reported that the velocity and flow rate increase with the increase of the peripheral layer thickness and decrease with the increase of the angle of tapering and depth of the stenosis. It is observed that the flow rate decreases nonlinearly with the increase of the viscosity ratio and yield stress. The estimates of the increase in the longitudinal impedance to flow are considerably lower for the two-fluid Herschel-Bulkley model compared with those of the single-fluid Herschel-Bulkley model. Hence, it is concluded that the presence of the peripheral layer helps in the functioning of effected arterial system. The findings indicate that the ANN provides reasonable predictive performance in resemblance to the analytical values. The Levenberg– Marquardt algorithm (LMA) was found best of BP algorithms with a minimum mean squared error (MSE) for training and cross validation.

Keywords

Artificial Neural Network; Wall shear stress; tapered artery; mild stenosis.

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