جستجوی مقالات مرتبط با کلیدواژه "emissions" در نشریات گروه "مکانیک"

The RCCI method is used in dual-fuel diesel-gas engines in this study. Previous studies have shown that different natural gas compounds and their percentages have a significant effect on the performance of dual-fuel engines. This research aims to investigate the combustion behavior of the OM-355 engine in dual-fuel (diesel/gas) mode using computational fluid dynamics. For this purpose, the dual-fuel engine is simulated using CONVERGE software. The simulation was performed at 1400 rpm, and different combinations of CNG gas and their various percentages and the output parameters of engine performance and pollution were studied. The simulation results show that natural gas compounds with a higher methane content increase the combustion quality and reduce unburned hydrocarbons and smoke pollutants. Additionally, in the combustion of this mixture, the temperature inside the combustion chamber increases, and the amount of NOX pollutants in the exhaust gases increases. The investigation of different percentages of natural gas in the dual-fuel engine shows that an increase in the amount of natural gas until 70% reduces the maximum temperature inside the combustion chamber, and beyond this amount, it increases. Therefore, for natural gas ratios up to 70%, the NOX pollutant is reduced by 75% compared to a diesel engine, and with further increases in the amount of natural gas, the NOX pollutant increases due to the rise in the temperature of the combustion chamber.

Fossil fuel reserves are running out and its use for energy production also affects the environment. Therefore, sustainable and clean energy sources must be produced to meet the needs. In this research, mixed fuels of methyl esters of rapeseed oil, soybean oil and palm oil were produced with diesel fuel. To achieve the advantages of palm oil biodiesel (high calorific value) and soybean and rapeseed oil biodiesel (low kinematic viscosity), different biodiesel mixtures (BS10, BS20, BR10, BR20, BP10, BP20, BRSP10 and BRSP20) were used to evaluate their effect on engine performance and greenhouse gas emissions at speeds of 1800 to 2700 rpm with a step of 300 rpm under full load conditions. The physical and chemical properties of all fuel mixtures were measured according to the ASTM D6751 standard. An air-cooled, 4-stroke, naturally aspirated single-cylinder diesel engine was used for different mixture testing. The experimental results showed that in all the combined fuels, the values of power and specific fuel consumption increase with increasing engine speed, while the torque decreases. Also, the number of pollutants increases with the increment of engine speed. Based on the results, BP20 mixture fuel can be used as an alternative in diesel engines without any engine modification.

One of the novel strategies to improve the performance and emissions of diesel engines is the use of alternative fuels as well as suitable additives such as nanoparticles to diesel fuel. Nanofuels play an important role in the optimization of combustion processes, fuel consumption, and emissions. In this paper, the effect of adding different nanoparticles (cerium, aluminum, and copper oxide nanoparticles) at a concentration of 100 particles per million (ppm) to diesel fuel on the combustion process and emissions of diesel engines has been investigated by using FIRE computational fluid dynamics code. For validation, the in-cylinder pressure variations, the experimental peak pressure, and the angle of occurrence are compared with the numerical results. In addition, the experimental data of NOx, soot, power as well as brake specific fuel consumption were evaluated with numerical values. The results show that nanoparticles increase the amount of heat transfer to the fuel and decrease the ignition delay. Also, better mixing of fuel and air in cerium oxide nanoparticles compared to other nanoparticles improved the fuel ignition mechanism, which leads to more complete combustion and a 14.5% increase in power and also a 6% and 34% reduction in fuel consumption and soot compared to diesel fuel respectively. The only downside is the 31% increase in NOx, which can be reduced by catalytic converters.

Reactivity Controlled Compression Ignition (RCCI) engines are new ideas for decreasing the emissions and fuel consumption in Compression Ignition engines. In this study, numerical study of the combustion and emission characteristics on a RCCI engine with dual fuel natural gas/diesel and methane/diesel are investigated. The numerical simulation is done by Converge software. The natural gas is composition of methane, ethane, propane, carbon dioxide, nitrogen and oxygen. The results were compared with the pressure and heat released in-cylinder, mean gas temperature, emissions and gross power with dual fuel natural gas/diesel and methane/diesel. The results showed that the maximum pressure, mean gas temperature and heat released in-cylinder with methane/diesel are about 5%, 2% and 3% higher than natural gas/diesel, respectively. NOx and Soot emissions with dual fuel natural gas/diesel are about 12.5% and 12% lower than methane/diesel, respectively. Other emissions including unburned hydrocarbon (UHC) and carbon monoxide (CO), was relatively high. Also, the gross engine power for dual fuel methane/diesel was about 4% higher than natural gas/diesel.

The aim of this study was to investigate the effect of magnetic flux density (6000 Gauss) on diesel engine fuel consumption and emissions. Experiments were performed at different distances with respect to the magnet in the fuel line (20, 40 and 60 cm) and at different engine speeds (1800, 2100, 2400 and 2700 rpm). We then selected carbon monoxide (CO), unburned hydrocarbons (UHC), carbon dioxide (CO2), nitrogen oxide (NOX) and fuel consumption as our study traits. According to the findings, the levels combination of 1800 rpm and 40 cm resulted in the greatest reduction of carbon monoxide, which was declined by 67 % compared to the control sample. The least amount of unburned hydrocarbon was observed at 1800 speed and 40 cm distance from the magnet with a value of 131.33. Moreover, the highest amount of nitrogen oxide was found in the 1800-20, 1800-40, 2100-40 levels compounds with 62, 59, 57 and 56 ppm, respectively. Fuel consumption was 3.023, 3.23, and 3.25 (Lit/h) at the maximum engine speed and distances of 20, 40, and 60 cm from the magnet, correspondingly; and dropped by 12, 6.1, and 5.52% compared to the control sample. We recruited response surface methodology to optimize process conditions. Based on the results, the optimal conditions were obtained at 1800 rpm and a distance of 37 cm of magnet on the fuel line. Under these optimal conditions, the amount of carbon monoxide, unburned hydrocarbons, carbon dioxide, nitrogen oxide and fuel consumption were 0.49%V, 128.45 ppm, 1.96%V, 61.3 ppm, and 1.92 (Lit/h) with desirability of 0.7, respectively.

Numerous studies on using light fuels in compression ignition engines to reduce emission and increase efficiency have been done. The Reactivity Controlled Compression Ignition engines are one of these studies. Nevertheless, using heavy fuels vapor for achieving partially premixed combustion is not investigated. Using diesel fume (to upgrade conventional combustion to premixed combustion) resolves the need for a secondary fuel tank in a car. However, diesel fuel has heavy hydrocarbons and is a high reactivity fuel. So in this study, diesel has evaporated in a tank, and its vapor has injected into the intake air for studying a semi homogeneous combustion. The tests have performed at 2000 rpm (the speed of maximum torque). According to the achieved results, although diesel has heavy hydrocarbons and is a high reactivity fuel, adding diesel fumigation can reduce soot and NOx emissions up to 20% and 50%, respectively. Increasing load reduces the positive impact of adding diesel fumigation on soot and NOx emission reduction. However, the positive impact of adding diesel fumigation continues up to 80% of the full load. Adding diesel fumigation has no impact on cyclic variation and ringing intensity, but increases CO and HC emission. The evaporation of diesel averagely consumes 15% of brake power. Also on average, 5% of diesel evaporation energy can be supplied by recovering heat energy from the exhaust gas (warming up diesel from ambient temperature to the exhaust gas temperature).

In this study, ethanol and diisopropyl ether were combined with base gasoline in three different volume ratios. The XU7JP/L3 engine was selected to test the effect of fuel additives on engine performance and emission parameters. Engine performance parameters were recorded simultaneously by a computer device at the time of the test, and engine output pollutants were manually recorded by a pollutant meter. The results showed that with the addition of oxygenated additives to gasoline, the oxygen in the fuel-air mixture was increased, the fuel was closer to the stoichiometric conditions and the combustion was more complete. As a result, the enginechr('39')s torque and brake power increased and its brake-specific fuel consumption decreased. Oxygenated additives also reduced carbon monoxide and unburned hydrocarbons and increased carbon dioxide and nitrogen oxides. The results of increasing diisopropyl ether additive compared to ethanol additive in combination with base gasoline also showed that the amount of carbon monoxide, unburned hydrocarbon, and nitrogen oxide pollutants is higher and the amount of carbon dioxide production is lower.

The scarceness of non-renewable energies, rising fossil fuels prices, as well as strict environmental regulations have forced many researchers to focus on utilizing environmentally friendly renewable energies such as biogas in internal combustion engines. Variety in raw materials and process of biogas production results in different components and percentages of various elements, including methane. These differences make it hard to control the combustion, effectively, in internal combustion engines. In this research, by means of 3D-CFD model, the effect of reformed biogas (R.BG) was investigated on the combustion characteristics, emissions and performance of a RCCI engine. Validation of the model was done on a single-cylinder compression ignition engine in conventional diesel and dual-fuel operations. The combustion model of the RCCI engine was simulated by replacing diesel with 20%, 40% and 60% of R.BG as low reactivity fuel. The results showed that by increasing the R.BG energy share with a constant equivalence ratio of 0.43, the average combustion temperature decreases to 1300 K and the maximum in-cylinder pressure increases up to 22.63%. Instead, it results in 2.3%, 7.9%, and 14.5% engine output losses, respectively. Also, the emission of NOx, against CO, is reduced by 50%. Soot and UHC emissions were found to be slightly decreased while using R.BG more than 40%.

In the present study, using artificial neural network, modeling and prediction of NOx, soot and fuel consumption in a direct injection diesel engine is done by applying control variables of engine speed, inlet air temperature and fuel mass injected into the combustion chamber. For this purpose, the empirical experiments were carried out to prepare the necessary modeling and correlation between input and output parameters by neural network. The neural network with the Levenberg-Marquardt training algorithm is designed to train the existing relationship between the above parameters, in which the output variables are modeled completely independently. In other words, for any output such as NOx, the number of hidden layer neurons as well as the lattice control parameters would be quite different from the same parameters for soot or fuel consumption. The results show that the designed neural network reaches accuracy 0.97 for 36 neurons in the hidden layer at 3733th epoch to test the data in NOx modeling. Also, modeling of soot with more neurons and accuracy 0.96 is performed in the 2081th epoch. On the other hand, the test accuracy for modeling fuel consumption for 19 neurons in the hidden layer at 3698th epoch was 0.94 which is due to the uneven distribution of the experimental data over a wide range of modeling ranges. The neural network can also be used as an effective method in direct injection diesel engines intelligent control systems to reduce pollutants and fuel consumption due to its fast convergence and hence short response time.

The advantages and disadvantages of using gasoline and NG as single-fuel is a challenge for researchers in development of SI engines. Singular utilization of these fuels results in some advantages and disadvantages from economics, thermodynamics, pollution and development aspects and make it difficult to prefer one to the other. Assuming that using combination of the fuels can modify the output results, in the present research, different combinations of 100, 90, 75 and 60% gasoline and the rest of natural gas, designated as G100, G90, G75 and G60, were investigated in a SI single-cylinder engine at running at 1800rpm, 9 compression ratio and stoichiometric equivalence ratio. After collecting and processing in-cylinder experimental data in the combinations and different spark advances and their experimental data processing, consecutive cycle-to-cycle data were extracted and analyzed with engine output data. First, optimum spark advance of each combination was determined and then, the combinations were compared at their spark advances. The results revealed that increasing natural gas fraction in combination causes substantial reductions in standard deviation, σ, and coefficient of variation, COV of IMEP, so that σ and COV of G60 reduced by 51.6% and 49.2%, respectively, in comparison with G100. Reducing the gasoline presence in combination, the amount of CO2, NOx and HC reduced except G90 which have the higher HC and NOx, whereas, CO amount in G90 decreased to the lowest level. Also, no satisfactory performance was observed in the G90 combination.

Biodiesel and some nano-catalysts are an important additive to diesel fuel and can improve the engine performance and reduce emissions. In this study, biodiesel was added to pure diesel with ratios of 5 and 10 percent. Then, the carbon nanotubes were mixed as additive with these blends with concentrations of 30, 60, and 90 ppm to evaluate the performance, emissions, and vibration levels in a diesel engine. An ANN model, based on standard back-propagation learning algorithm for the engine, was developed. Multi-layer perception network (MLP) was used. The input or independent parameters were fuel blend, engine speed, fuel density, fuel viscosity, LHV, intake manifold pressure, fuel consumption, exhaust gas temperature, oxygen contained in exhaust gases, oil temperature, relative humidity and ambient air pressure. The target parameters were performance, emissions and RMS and Kurtosis of engine vibrations. The results showed that the specific fuel consumption and CO and UHC emissions decreased, while NOx emission increased. Also, the ANN model showed the training algorithm of back-propagation with 20-20 neurons in hidden layers (logsig-logsig) is able to predict different parameters with good performance and accuracy. The corresponding R-values for training, validation and testing were 0.9999, 0.9985 and 0.9994, respectively.