Search

In this research, the spray combustion, soot formation and exhaust emissions of diesel, biodiesel, gasoline fuels and their mixtures are analysed in a constant volume chamber. A multicomponent kinetic mechanism (CDBG) suitable for diesel-biodiesel-gasoline mixtures developed by our research group is utilised, and the associated physicochemical properties are thoroughly calculated. Adaptive mesh refinement scheme with appropriate mesh independency analysis are applied. Liquid penetration length, lift-off length, ignition delay and soot formation have been benchmarked against experimental data in the literature. A hybrid RANS-LES model, known as DES model, is used to simulate the turbulent condition. The effects of different ambient temperature/oxygen levels on the flame structure, soot formation and emissions of different ternary mixtures of D75|BD20|G5, D70|BD20|G10 and D65|BD20|G15 were analysed. D65|BD20|G15 resulted in a lower soot mass yield than that of BD100 (pure biodiesel) and D100 (pure diesel) for about 35% and 27%, respectively, at T = 900 K | O2 = 15%. Greater soot mass reductions for the tested fuels were captured by the decrease in ambient temperature from 900 K to 800 K by a factor of ∼1/3 (same ambient O2 concentration). Lower nitrogen oxides (NOx) emissions were obtained for D100 by factors of ∼1/2 at T = 900 K | O2 = 15% compared to BD100. Gasoline-added mixtures revealed lower NOx compared to BD100 (∼20%) yet still higher than D100. Lower carbon dioxide (CO2) and carbon monoxide (CO) emissions were captured for D65|BD20|G15 compared to BD100 and D100.

In this research, a multi-input multi-output artificial neural network (MIMO-ANN) is developed, in which 14 features associated with the engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline mixtures are meant to be modelled by a diverse combination of engine/combustion parameters. The selected targets comprise brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), combustion efficiency, coefficient of variance (COV), NOx, CO2, CO and HC emissions, exhaust temperature (Texh), in-cylinder pressure (Pcyl), maximum pressure rise rate (MPRR), heat release rate (HRR), combustion duration (CD) and ignition delay (ID). The inputs variables entail the load, speed, compression ratio, gasoline, biodiesel and diesel ratios, crank angle (CA), injection temperature (Tinj), injection pressure (Pinj), brake mean effective pressure (BMEP) and start of injection (SOI). Sensitivity analysis and outlier detection are applied in order to eliminate less-effective inputs/data points. The prepared data sets are then used to train and test the ANN model, in conjunction with benchmarking the model outcomes using coefficient of determination (R2), average absolute relative deviation (AARD) and relative mean squared errors (RMSE). The R2 ranged within 0.9804–0.9998, which is close to unity, proving that the proposed network is accurately capable of predicting the intended combustion characteristics.

In this study, a numerical investigation on the effects of ozone addition on the laminar premixed flame development and emissions formation of carbon monoxide (CO) and nitrogen oxides (NOx) for methane and propane in a co-flow burner is presented. Simulations for the reacting mixtures with the addition of ozone concentrations of 3810 ppm and 6000 ppm were performed at atmospheric pressure, ambient temperature of 298 K and equivalence ratios (φ) of 0.8, 1.0 and 1.3. Based on the simulation results, ozone improved the flame development, where the flame temperatures for methane/propane were raised by 42 K (maximum difference). Besides, the formaldehyde (CH2O) and hydroxyl (OH) mass fractions were also increased and subsequently elongated the stand-off distances for methane/propane by 12%. The laminar burning velocities for methane/propane were accelerated by 8.7%, which were contributed by the higher rates of fuel oxidation due to the increased ozone concentration. Although the CO and NOx mass fractions for the ozone enhanced methane/propane flames were deviated by a maximum difference of 29%, the effect of ozone on these emissions were found varied according to the fuels and φ values.

In this study, the performance of a gas-fired radiant-tube which was fuelled with syngas and air mixtures was investigated numerically using ANSYS Fluent version 17.2. The burner was operated in a non-premixed mode with swirled air. The exhaust emissions of nitrogen oxides (NOx) and carbon monoxide (CO) as well as the temperature distributions along the radiant tube were analysed for three different syngas fuels which were composed of hydrogen (H2) and CO, with ratios (by vol.) of 75:25, 50:50, and 25:75. The simulations were conducted at equivalence ratio values of 0.82, 0.7 and 0.5, while the air flow Reynolds numbers were set to 5400, 6320 and 8850. Based on the simulation results under all the tested conditions, it was found that the 75H2:25CO syngas fuel performed better, where the non-uniformity parameter calculated for this fuel was 35% lower than that of the 25H2:75CO. In terms of CO emissions, 75H2:25CO had the lowest value among the tested fuels, where the maximum difference as compared to that of the 25H2:75CO was up to 93%. Meanwhile, the NOx emissions predicted for 75H2:25CO were approximately 67% lower than those of the 25H2:75CO.

This work numerically examines the quasi-steady spray combustion and soot development for soybean methyl ester (SME) and SME-ethanol blends (S90E10, S70E30). A validated reduced ethanol mechanism (37 species and 149 reactions) was formulated using a temperature sensitivity analysis under auto-ignition and jet-stirred reactor conditions. At initial pressures between 10.0 bar and 60.0 bar, initial temperatures between 750 K and 1350 K and equivalence ratios between 0.5 and 2.0, the ignition delays and key species profiles (C2H5OH, CO2, O2) of the reduced mechanism deviated by 0.5 order and 40%, respectively as compared to the experimental measurements. Subsequently, the reduced ethanol mechanism was combined with a reduced biodiesel mechanism to form a surrogate mechanism (102 species and 446 reactions) for biodiesel-ethanol blends and integrated into OpenFOAM for spray combustion modelling. Under reacting spray conditions at a constant ambient density of 22.8 kg/m3 and ambient temperatures of 900 K and 1000 K, the spray penetrations for SME-ethanol blends were shortened by 35.5% (maximum difference). Smaller flame areas with 60 K higher local flame temperature were obtained. Due to a 20% decrease in acetylene mass fractions and soot formation rates, the peak soot volume fractions for SME-ethanol blends were 19.6% lower.

This reported work investigates the sensitivities of spray and soot developments to the change of thermo-physical properties for coconut and soybean methyl esters, using two-dimensional computational fluid dynamics fuel spray modelling. The choice of test fuels made was due to their contrasting saturation-unsaturation compositions. The sensitivity analyses for non-reacting and reacting sprays were carried out against a total of 12 thermo-physical properties, at an ambient temperature of 900 K and density of 22.8 kg/m 3 . For the sensitivity analyses, all the thermo-physical properties were set as the baseline case and each property was individually replaced by that of diesel. The significance of individual thermo-physical property was determined based on the deviations found in predictions such as liquid penetration, ignition delay period and peak soot concentration when compared to those of baseline case. Among all the properties, latent heat of vaporisation produced the greatest effect on the spray and soot developments under the tested conditions, as evidenced by a longer liquid penetration of 35.0% and a reduced peak soot concentration of 22.8%. Besides, coupled effects among the thermo-physical properties were also determined. Meanwhile, the effects of thermo-physical properties were found to vary with the addition of unsaturation levels and chemical kinetics in the simulation.