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The presence of tortuosity in coronary artery (CA) affects the local wall shear stress (WSS) which is an influencing hemodynamic descriptor (HD) for the development of atherosclerotic sites. To conduct a morphological parametric study in coronary arteries (CAs), several idealized tortuous artery models were obtained by varying three morphological indices namely, curvature radius (CR), distance between two bends (DBB) and the angle of bend (AoB). Computational fluid dynamics methodology with multiphase mixture theory is used to explore the effect of coronary tortuosity on various WSS based hemodynamic descriptors (HDs) namely, time-averaged WSS, oscillatory shear index, time-averaged WSS gradient, endothelial cell activation potential and the relative residence time that are used to determine the vulnerable locations for the onset of thrombosis and atherosclerosis. Our findings suggest that all the tortuosity morphological indices, CR, DBB and AoB have significant influence on the distributions of various HDs and hemodynamics. It is also observed that atherosclerosis prone sites were witnessed at the inner artery wall at downstream regions of the bend section 1 and bend section 2 in all the tortuous artery models studied and found to increase as the CR and DBB were reduced however, found to increase as the AoB is increased. Hence, severe coronary tortuosity in CAs with small CR, small DBB and higher AoB may have lower WSS zones at inner bend sections which promote atherosclerosis plaque progression. The analysis obtained from this multiphase blood flow study can be employed potentially in the clinical assessment on the severity of atherosclerosis lesions as well as in understanding the underlying mechanisms of localization and formation of atherosclerotic plaques.

The focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavgand MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (α) is analyzed. SOO gives highest fitness of Nuavgand lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavgwith MaxTin the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of αis important during training and testing. The best Nuavgobtained from SOO using MVO algorithm is around 82.06 while MaxTis 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavgand MaxTaround 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxTwhile it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxTand Nuavgrespectively.

The development of integrated electronic devices with increase level of miniaturisation, higher performance and output has increased the cooling requirement of chips considerably. So the use of nanofluids to cool these electronic components is inevitable. In this work, an experimental investigation of heat transfer and pressure drop characteristics of rectangular and circular minichannel arrays cooled with alumina nanofluids (in rectangular minichannel) and copper nanofluids (in circular minichannel) is carried out. The study was conducted under steady forced, turbulent flow conditions keeping heat flux as a constant and varying the flow rates. For all investigated flow rates, it was observed that with increase in Reynolds number, both the Nusselt number and pressure drop increases which further lead to increase in pumping power. The Brownian motion, interaction of nanoparticles and the resulting disturbance in the boundary layer can be the possible reasons for the observed increments.

•Biodiesel was tested as a pilot fuel for biogas run on a dual-fuel diesel engine.•Experimental investigations at several compression ratios and engine load.•RSM was used for a robust model prediction with R2in the range of 0.8673 – 0.9917.•The validation test confirmed expected output was within the 6% error range.•17.53 of CR and 76.84% of engine load were optimal parameters.