FUEL AND COMBUSTION
Year:2020 | Volume:13 | Issue:2|Papers Count:7

In this work, film cooling of a 10N thrust chamber is investigated using different numerical models. The thruster is modeled by feeding gas at a chemical equilibrium state from the inlet. Heat flux is computed for different flow rates of the coolant and is compared to the analytical Bartz equation for the no coolant case. In the second part, solid wall heat conduction is modeled, and the computed wall temperature profile is compared to the available experimental data. Chemical dissociation of MMH in the coolant layer is modeled by constructing a chemical mechanism for the reactions of Methyl Hydrazine with Nitrogen Tetroxide. Chemical reactor modeling shows a close prediction to other available data for the combustion of MMH/NTO system. To assess the effect of different cooling mechanisms in the coolant layer, different approaches for heat transfer modeling with different levels of complexity are investigated in this paper. The considered models include cold gas, reactive gas, cold droplets, and a reactive evaporating layer of droplets. For the most sophisticated model considered (reactive evaporating layer of droplets), a 48% reduction of heat flux is computed at the throat when 20% of the fuel is used as the coolant. Also, when solid wall heat conduction is considered, the computed wall temperature profile is closest to the experimental data for the case of 20% of the fuel as coolant.

Biodiesel is one of the clean and renewable energy resources that can be a good alternative to fossil fuels. In this study, a biodiesel (B5) was blended with the diesel. This study was divided into two parts. In the first part, we compared the results of additives of Al2O3, SiO2 and SiO2_Al2O3 nano-composite to biodiesel fuel on the performance parameters and the emission. In the second part, the performance of neural network in predicting performance and emissions was investigated. Initially, adding B5 to diesel fuel was led to reduce CO emissions and fuel consumption, and increase torque and brake power. In the next step, alumina and silica nanoparticles were added separately to the diesel-biodiesel blend at 30, 60, 90 and 120 ppm. The results showed that SiO2, compared to Al2O3, improved the performance and reduced emissions which resulted in 21. 6% increase in brake power at 90 ppm, 8. 1% decrease in CO2, 56. 16% decrease in CO, 3. 05% decrease in fuel consumption and a very slight increase of 0. 57% and 0. 6% in NOX and NO, respectively. Then SiO2_Al2O3 composite samples with different ratios were added to diesel-biodiesel fuel. Among the nanocomposites, B5Al60Si60 had the highest power and torque, resulting in 1. 44% increase in torque and 1. 64% increase in brake power compared to diesel fuel and a reduction of 39. 21%, 9. 9%, 10, 6. 9% and 6. 85% in CO, CO2, NO and NOX, respectively. A multilayered neural network with one and two hidden layers and two types of sigmoid activation and hyperbolic tangent were used to analyze the results. The MSE and R values for brake power, CO, CO2, NO, NOX and torque were 21. 10 and 0. 9905, 1414/75 and 0. 9910, 0. 0009 and 0. 9940, 3. 94 and 0. 9965, 0. 00079919, and 0. 9905 respectively. In total, the best network is that with the sigmoid activation function and the hidden layer.

In this paper, a fire whirl in a high-rise room with a gap in one corner is investigated. Methanol fuel is intended. Large Eddy Simulation method was used and the OpenFoam software, one-equation sub-grid model and eddy dissipation combustion model were performed. Results in two different conditions (fuel bed of 5. 8 and 7 cm) were obtained and compared with the experimental results. By comparing the numerical results with the experimental results, it is observed that the numerical results of the mean temperature in the middle and corner of the room are consistent with the experimental results. The results of the various sub-grid models in the center line show that the WALE model fits better with the experimental results and also the performance of the two one-equation and Smagorinsky sub-grid models is lower than WALE. On average, the relative error percentage of the WALE model is 7. 3 percent, while each of the Smagorinsky and one-equation models have error of 8 and 8. 9 percent compared to the experimental results, respectively. The results of the three sub-grid models in the corner are not significantly different and are more consistent with the experimental results. The corner line error of the room being less than 8%.

Spray impingement on a solid surface has been studied. A flow blurring injector is used to atomized the liquid. In this type of injector, a back flow of air penetrates into the liquid orifice at the orifice exit just before the injection surface. The remained air flow rate also squeezes the two phase flow at the orifice exit. Here, the film thickness formed by the impingement of the spray with a thin wall is measured using imaging techniques. The results showed that at the high volumetric liquid flow rate there is a liquid film on solid surface which can lead to an incomplete combustion. There are many parameters which can affect the liquid film on the surface like liquid and gas flow rates and distance from nozzle to surface. In this study it is intended to measure and analyze the liquid film which is formed by the spray of the novel injector named flow blurring injector and investigate the effect of aforementioned parameters.

Combustion of a mixture of inhomogeneity reactants is a type of combustion that has been used since 1997. All previous studies have been done on this type of outdoor combustion. The aim of this study was to investigate the numerical effect of different levels of inhomogeneity on the flame length at different velocities and equivalence ratios in closed space without the presence of ambient air and its influence on the flame. In this numerical study, the configuration modeling of Navier Stokes equations using Reynolds averaging method has been used. The results show that the flame length during a given inhomogeneity has a low limit so that the flame length increases before and after the inhomogeneity. This length of inhomogeneity varies at different velocities and equivalence ratios. The results also show that the flame length in the non-premixed state is less than the flame length when the fuel/air mixture is almost pre-mixed in the ignition chamber but as the equivalence ratio decreases, the difference is expected to be equal. The lower flame length values in the non-mixed state will be greater than the near-mixed state. Also, the cross-sectional area of the flame inside the chamber changes significantly both in length and width by changing the length of the inhomogeneity.

The aims of this study were to immobilize K2O nanoparticles over bentonite clay and evaluate and compare its performance with that of bare K2O nanoparticles in order to study the role of mineral support in the biodiesel production. For this purpose, a heterogeneous K2O/Bentonite nanocatalyst was sonochemichally prepared by loading 30 wt. % potassium hydroxide over bentonite and used in the transesterification reaction of sunflower oil. The obtained result was compared with KOH as a homogeneous catalyst and bare K2O in order to evaluation of performance and stabilization effect, respectively. The physical and chemical properties of synthesized composite nanocatalyst were investigated using XRD, FESEM, EDX, BET, and FTIR analyzes. The results of the characterization analysis indicate the successful preparation of the synthesized sample and suitable surface and structural properties of this sample for use in the oil transesterification process. XRD analysis confirmed the formation of the crystalline phase of K2O in the synthesized sample. FESEM images showed that the surface of bentonite has been covered by small K2O nanoparticles with uniform size distribution and dispersion. The results of EDX analysis confirmed the presence of all the elements used and there was no impurity in the catalyst structure. BET analysis showed that the synthesized nanocomposites had an appropriate specific surface area. The catalytic results of the samples in operating conditions of methanol to oil molar ratio of 12: 1, catalyst amount of 3 wt. %, reaction temperature of 65 ° C and reaction time of 3 h indicate acceptable efficiency of synthesized nanocomposite with production yield of 95. 17% compared with K2O and KOH samples which had 80. 57% and 76. 62% production efficiencies, respectively. This better performance can be attributed to improved surface and structural properties of the active particles stabilized by ultrasound energy such as large cavities, relatively high specific surface area, uniform morphology, uniform distribution of potassium particles, and strong interaction of potassium particles with bentonite layers and formation of Al-O-H groups. Also, the biodiesel properties, such as density, viscosity, cloud point, pour point, and cetane number were measured and compared to standards.

Blended biofuels such as biodiesel and bioethanol besides adding carbon nanotubes as catalyst to diesel fuel significantly improve the engine performance and reduce emissions. In this study, biodiesel (5%) was initially added to the diesel fuel to evaluate engine performance and its emissions. The studied fuels were prepared as MWENT-COOH nanoparticles (30, 60, and 90 ppm) were added to the fuels of B5 (5% biodiesel and 95% diesel) and E6 (6% bioethanol and 94% diesel) and E3 (3% bioethanol and 97% diesel). Experiments were performed in triplicates and a multilayer feed-forward back-propagation (FFBP) artificial neural network (ANN) was used for modeling the obtained results. Fuel type, engine speed, density, viscosity, the thermal value of the fuel, inlet manifold pressure, fuel consumption, exhaust gas temperature, oil temperature, oxygen in the exhaust gases, relative humidity and pressure of inlet air were considered as independent or inlet layer parameters. Output layer parameters included engine performance and emission. The results represented the decrease in emissions of CO and unburned hydrocarbons and specific fuel consumption as well as an increase in nitrogen oxides emissions. Considering the regression coefficient (R ) and MSE, the ANN model based on the sigmoid learning function was introduced as the optimal one in comparison to the linear and hyperbolic tangent networks. The values of R were also obtained for training, evaluation, and testing of the optimal network model. In conclusion, it can be mentioned that ANN was the most effective model in simulating the obtained data and investigating the sensitivity coefficient.