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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.

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Machine learning (ML) offers a variety of techniques to understand many complex problems in different fields. The field of heat transfer, and thermal systems in general, are governed by complicated sets of governing physics that can be made tractable by reduced-order modeling, and by extracting simple trends from measured data. Therefore, ML algorithms can yield computationally efficient models for more accurate predictions or to generate robust optimization frameworks. This study reviews past and present efforts that use ML techniques in heat transfer from the fundamental level to full-scale applications, including the use of ML to build reduced-order models, predict heat transfer coefficients and pressure drop, real-time analysis of complex experimental data, and optimize large-scale thermal systems in a variety of applications. The appropriateness of different data-driven ML models in heat transfer problems is discussed. Finally, some of the imminent opportunities and challenges that the heat transfer community faces in this exciting and rapidly growing field are identified.

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