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Background: Local hemodynamics plays an important role in atherogenesis and the progression of coronary atherosclerosis disease (CAD). The primary biological effect due to blood turbulence is the change in wall shear stress (WSS) on the endothelial cell membrane, while the local oscillatory nature of the blood flow affects the physiological changes in the coronary artery. In coronary arteries, the blood flow Reynolds number ranges from few tens to several hundreds and hence it is generally assumed to be laminar while calculating the WSS calculations. However, the pulsatile blood flow through coronary arteries under stenotic condition could result in transition from laminar to turbulent flow condition.Methods: In the present work, the onset of turbulent transition during pulsatile flow through coronary arteries for varying degree of stenosis (i.e., 0%, 30%, 50% and 70%) is quantitatively analyzed by calculating the turbulent parameters distal to the stenosis. Also, the effect of turbulence transition on hemodynamic parameters such as WSS and oscillatory shear index (OSI) for varying degree of stenosis is quantified. The validated transitional shear stress transport (SST) k-ω model used in the present investigation is the best suited Reynolds averaged Navier-Stokes turbulence model to capture the turbulent transition. The arterial wall is assumed to be rigid and the dynamic curvature effect due to myocardial contraction on the blood flow has been neglected.Results: Our observations shows that for stenosis 50% and above, the WSS avg , WSS max and OSI calculated using turbulence model deviates from laminar by more than 10% and the flow disturbances seems to significantly increase only after 70% stenosis. Our model shows reliability and completely validated.Conclusions: Blood flow through stenosed coronary arteries seems to be turbulent in nature for area stenosis above 70% and the transition to turbulent flow begins from 50% stenosis.

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

A variety of wall shear stress (WSS) based hemodynamic descriptors have been defined over the years to study hemodynamic flow instabilities as potential indicators or prognosticators of endothelial wall dysfunction. Generally, these hemodynamic indicators have been calculated numerically using 'single phase' approach. In single phase models, the flow-dependent cell interactions and their transport are usually neglected by treating blood as a single phase non-Newtonian fluid. In the present investigation, a multiphase mixture-theory model is used to define the motion of red blood cells (RBCs) in blood plasma and interactions between these two-components. The multiphase mixture theory model exhibited good agreement with the experimental results and performed better than non-Newtonian single phase model. The mixture-theory model is then applied to simulate pulsatile blood flow through four idealized coronary artery models having different degrees of stenosis (DOS) severities viz., 30, 50, 70 and 85% diameter reduction stenosis. The maximum WSS is seen at the stenosis throat in all the cases and maximum oscillatory shear index (OSI) is seen in downstream region of the stenosis. Our findings suggest that for degree of coronary stenosis more than 50%, a more disturbed fluid dynamics is observed downstream of stenosis. This could lead to further progression of stenosis and may promote a higher risk of atherogenesis and plaque buildup in the flow-disturbed area. The potential atherosclerotic lesion sites were identified based on clinically relevant values of WSS, timeaveraged WSS gradient (TAWSSG), time-averaged WSS (TAWSS), and OSI. Finally, the change in potential atherosclerotic lesion sites with respect to DOS has been quantified.

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