ABSTRACT
Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.
ABSTRACT
Friction and wear are the main factors in the failure of the piston in automobile engines. The objective of this work was to improve the tribological behaviour and lubricant properties using hybrid Cellulose Nanocrystal (CNC) and Copper (II) oxide nanoparticles blended with SAE 40 as a base fluid. The two-step method was used in the hybrid nanofluid preparation. Three different concentrations were prepared in a range of 0.1% to 0.5%. Kinematic viscosity and viscosity index were also identified. The friction and wear behavior were evaluated using a tribometer based on ASTM G181. The CNC-CuO nano lubricant shows a significant improvement in term of viscosity index by 44.3-47.12% while for friction, the coefficient of friction (COF) decreases by 1.5%, respectively, during high and low-speed loads (boundary regime), and 30.95% during a high-speed, and low load (mixed regime). The wear morphologies results also show that a smoother surface was obtained after using CNC-CuO nano lubricant compared to SAE 40.
