The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Mohamed Kamal Ahmed Ali - One of the best experts on this subject based on the ideXlab platform.
-
anti wear properties evaluation of frictional sliding interfaces in Automobile Engines lubricated by copper graphene nanolubricants
Friction, 2020Co-Authors: Xianjun Hou, Mohammad Ali Abdelkareem, Mohamed Kamal Ahmed AliAbstract:Owing to the significance of improving fuel economy, reducing emissions, and extending the durability of engine components, this study focused on the tribological performance of nano-additives. In this study, copper (Cu) and graphene (Gr) nanomaterials were dispersed in a fully formulated engine oil (5W-30) with different concentrations. The tribological trials were investigated under various speeds and loads, utilizing a reciprocating tribometer to mimic the ring/liner interfaces in the engine. The frictional surface morphologies were comprehensively analyzed using electron probe X-ray microanalysis (EPMA), field emission scanning electron microscopy (FESEM), energy dispersive spectrometer (EDS), and three dimensional (3D) surface profilometry to explore the mechanisms responsible for improving the tribological performance of the frictional sliding parts in the engine. The tribological test results illustrated that lubrication by nano-additives reduced the wear rate (WR) and friction coefficient (COF) by 25%–30% and 26.5%–32.6%, respectively, as compared with 5W-30. The results showed that this is a promising approach for increasing the durability and lifespan of frictional sliding components and fuel economy in Automobile Engines.
-
novel approach of the graphene nanolubricant for energy saving via anti friction wear in Automobile Engines
Tribology International, 2018Co-Authors: Hou Xianjun, Mohammad Ali Abdelkareem, Mohamed Kamal Ahmed Ali, Mubashir Gulzar, Ammar H ElsheikhAbstract:Abstract The friction and wear of the worn surfaces is a principal cause of energy dissipation in Automobile Engines. Therefore, the objective of this work was to improve the tribological behavior using graphene (Gr) nanolubricant designed for saving energy and reducing the exhaust emissions. Anti-friction and anti-wear properties of Gr nanolubricant have been evaluated using tribometer based on ASTMG181. Herein, we present the self-healing mechanism responsible for the tribological events. To link tribological tests with factual engine performance, the engine performance was evaluated utilizing AVL dynamometer under New European Driving Cycle (NEDC). The tribological results showed that the lubrication via Gr nanolubricant improves the anti-friction and anti-wear properties by 29–35% and 22–29%, respectively, during boundary lubrication system. The engine lubrication using Gr nanolubricant revealed reducing cumulative fuel mass consumed by 17% with road load simulation during NEDC test. Furthermore, the exhaust emissions (CO, CO2, HC and NOx) were decreased by 2.79–5.42%, as compared to the reference oil.
Mohammad Ali Abdelkareem - One of the best experts on this subject based on the ideXlab platform.
-
anti wear properties evaluation of frictional sliding interfaces in Automobile Engines lubricated by copper graphene nanolubricants
Friction, 2020Co-Authors: Xianjun Hou, Mohammad Ali Abdelkareem, Mohamed Kamal Ahmed AliAbstract:Owing to the significance of improving fuel economy, reducing emissions, and extending the durability of engine components, this study focused on the tribological performance of nano-additives. In this study, copper (Cu) and graphene (Gr) nanomaterials were dispersed in a fully formulated engine oil (5W-30) with different concentrations. The tribological trials were investigated under various speeds and loads, utilizing a reciprocating tribometer to mimic the ring/liner interfaces in the engine. The frictional surface morphologies were comprehensively analyzed using electron probe X-ray microanalysis (EPMA), field emission scanning electron microscopy (FESEM), energy dispersive spectrometer (EDS), and three dimensional (3D) surface profilometry to explore the mechanisms responsible for improving the tribological performance of the frictional sliding parts in the engine. The tribological test results illustrated that lubrication by nano-additives reduced the wear rate (WR) and friction coefficient (COF) by 25%–30% and 26.5%–32.6%, respectively, as compared with 5W-30. The results showed that this is a promising approach for increasing the durability and lifespan of frictional sliding components and fuel economy in Automobile Engines.
-
novel approach of the graphene nanolubricant for energy saving via anti friction wear in Automobile Engines
Tribology International, 2018Co-Authors: Hou Xianjun, Mohammad Ali Abdelkareem, Mohamed Kamal Ahmed Ali, Mubashir Gulzar, Ammar H ElsheikhAbstract:Abstract The friction and wear of the worn surfaces is a principal cause of energy dissipation in Automobile Engines. Therefore, the objective of this work was to improve the tribological behavior using graphene (Gr) nanolubricant designed for saving energy and reducing the exhaust emissions. Anti-friction and anti-wear properties of Gr nanolubricant have been evaluated using tribometer based on ASTMG181. Herein, we present the self-healing mechanism responsible for the tribological events. To link tribological tests with factual engine performance, the engine performance was evaluated utilizing AVL dynamometer under New European Driving Cycle (NEDC). The tribological results showed that the lubrication via Gr nanolubricant improves the anti-friction and anti-wear properties by 29–35% and 22–29%, respectively, during boundary lubrication system. The engine lubrication using Gr nanolubricant revealed reducing cumulative fuel mass consumed by 17% with road load simulation during NEDC test. Furthermore, the exhaust emissions (CO, CO2, HC and NOx) were decreased by 2.79–5.42%, as compared to the reference oil.
Ammar H Elsheikh - One of the best experts on this subject based on the ideXlab platform.
-
novel approach of the graphene nanolubricant for energy saving via anti friction wear in Automobile Engines
Tribology International, 2018Co-Authors: Hou Xianjun, Mohammad Ali Abdelkareem, Mohamed Kamal Ahmed Ali, Mubashir Gulzar, Ammar H ElsheikhAbstract:Abstract The friction and wear of the worn surfaces is a principal cause of energy dissipation in Automobile Engines. Therefore, the objective of this work was to improve the tribological behavior using graphene (Gr) nanolubricant designed for saving energy and reducing the exhaust emissions. Anti-friction and anti-wear properties of Gr nanolubricant have been evaluated using tribometer based on ASTMG181. Herein, we present the self-healing mechanism responsible for the tribological events. To link tribological tests with factual engine performance, the engine performance was evaluated utilizing AVL dynamometer under New European Driving Cycle (NEDC). The tribological results showed that the lubrication via Gr nanolubricant improves the anti-friction and anti-wear properties by 29–35% and 22–29%, respectively, during boundary lubrication system. The engine lubrication using Gr nanolubricant revealed reducing cumulative fuel mass consumed by 17% with road load simulation during NEDC test. Furthermore, the exhaust emissions (CO, CO2, HC and NOx) were decreased by 2.79–5.42%, as compared to the reference oil.
Ma Fanhua - One of the best experts on this subject based on the ideXlab platform.
-
experimental study of hydrogen enriched compressed natural gas hcng engine and application of support vector machine svm on prediction of engine performance at specific condition
International Journal of Hydrogen Energy, 2020Co-Authors: Dua Hao, Roopesh Kuma Mehra, Sijie Luo, Zhibi Nie, Ma FanhuaAbstract:Abstract The effect of excess air ratio (λ) and ignition advance angle (θig) on the combustion and emission characteristics of hydrogen enriched compressed natural gas (HCNG) on a 6-cylinder compressed natural gas (CNG) engine has been experimental studied in an engine test bench, aiming at enriching the sophisticated calibration of HCNG fueled engine and increasing the prediction accuracy of the SVM method on Automobile Engines. Three different fuel blends were selected for the experiment: 0%, 20% and 40% volumetric hydrogen blend ratios. It is noted that combustion intensity varies with the excess air ratio and the ignition advance angle, so are the emissions. The optimal value of λ or θig has been explored in the specific engine condition. Results show that blending hydrogen can enhance and advance the combustion and stability of CNG engine, and it also has some benefic influence on the emissions such as reducing the CO and CH4. Meanwhile, a simulation research on forecasting the engine performance by using the support vector machine (SVM) method was conducted in detail. The torque, brake specific fuel consumption and NOx emission have been selected to characterize the power, economic and emissions of the engine with various HCNG fuels, respectively. It can be seen that the optimal model built by the SVM method can highly describe the relationship of the engine properties and condition parameters, since the value of the complex correlation coefficient is larger than 0.97. Secondly, the prediction performance of the optimal model for torque or BSFC is much better than the case of NOx. Besides, the optimal model built by the PSO optimization method has the best prediction accuracy, and the accuracy of the model obtained based on the training group with 20% hydrogen blend ratio is the best compared with those of others. The upshots in this article provide experimental support and theoretical basis for the adoption of HCNG fuel on internal combustion Engines as well as the application of intelligent algorithmic in the engine calibration technology field.
K I Ramachandran - One of the best experts on this subject based on the ideXlab platform.
-
hybrid fuzzy model based expert system for misfire detection in Automobile Engines
International journal of artificial intelligence, 2011Co-Authors: Babu S Devasenapati, K I RamachandranAbstract:This paper evaluates the use of fuzzy unordered rule induction algorithm (FURIA) with correlation based feature selection (CFS) embedded feature subset selection as a tool for misfire detection. The vibration data of the Automobile engine contains the engine performance data along with multitudes of other information. The decoding of engine misfire condition was achieved by processing the statistical features of the signals. The quantum of information available at a given instant is enormous and hence suitable techniques are adopted to reduce the computational load due to excess information. The effect of recursive entropy discretiser as feature size reduction tool and CFS based feature subset selection is analysed for performance improvement in the FURIA model. The FURIA based model is found to have a consistent high classification accuracy of around 88% when designed as a multi class problem and approaches 100% when the system is modeled as a two-class problem. From the results obtained the authors conclude that the combination of statistical features and FURIA algorithm is suitable for detection of misfire in spark ignition Engines.
