The present numerical study is aimed at investigating the effect of rotation on heat transfer to non-Newtonian nanofluid flowing through a pipe. Non-Newtonian fluid flow under laminar condition with heat transfer finds the applications in various industries like food processing, pharmaceutical and polymer etc. Various proportions (1–3%) of copper nanoparticles are mixed with water to study the heat transfer rates non-Newtonian nanofluid flowing through the rotating pipe. Effect of rotation rate on heat transfer rates are also studied. In this study for 1% nanofluid at a constant rotation rate of 0.8, the Nusselt number is increased by 119.45%. The highest thermal performance factor (TPF) is 1.74, observed at N = 0.8, Pe = 5000, and for 1% volume concentration of non-Newtonian nanofluid.
Purpose
The present work aims at improving the performance of the engine using optimized fuel injection strategies and operating parameters for plastic oil ethanol blends. To optimize and predict the engine injection and operational parameters, response surface methodology (RSM) and artificial neural networks (ANN) are used respectively.
Design/methodology/approach
The engine operating parameters such as load, compression ratio, injection timing and the injection pressure are taken as inputs whereas brake thermal efficiency (BTHE), brake-specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx) and smoke emissions are treated as outputs. The experiments are designed according to the design of experiments, and optimization is carried out to find the optimum operational and injection parameters for plastic oil ethanol blends in the engine.
Findings
Optimum operational parameters of the engine when fuelled with plastic oil and ethanol blends are obtained at 8 kg of load, injection pressure of 257 bar, injection timing of 17° before top dead center and blend of 15%. The engine performance parameters obtained at optimum engine running conditions are BTHE 32.5%, BSFC 0.24 kg/kW.h, CO 0.057%, HC 10 ppm, NOx 324.13 ppm and smoke 79.1%. The values predicted from ANN are found to be more close to experimental values when compared with the values of RSM.
Originality/value
In the present work, a comparative analysis is carried out on the prediction capabilities of ANN and RSM for variable compression ratio engine fuelled with ethanol blends of plastic oil. The error of prediction for ANN is less than 5% for all the responses such as BTHE, BSFC, CO and NOx except for HC emission which is 12.8%.
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