Effect of Fuel Injection Pressure and Premixed Ratio on Mineral Diesel-Methanol Fueled Reactivity Controlled Compression Ignition Mode Combustion Engine
Abstract:Reactivity controlled compression ignition (RCCI) mode combustion has attracted significant attention because of its superior engine performance and significantly lower emissions of oxides of nitrogen (NOx) and particulate matter (PM) compared with conventional compression ignition (CI) mode combustion engines. In this experimental study, effects of fuel injection pressure (FIP) of high reactivity fuel (HRF) and premixed ratio of low reactivity fuel (LRF) were evaluated on a diesel-methanol fueled RCCI mode co… Show more
“…During variance analysis on experimental results, mathematical statistics method is used to be sure whether differences in experimental results are incurred by differences in levels corresponding to factors or by experimental errors [52]. In this way, influence of various factors on experimental results may be analyzed in a more intuitive manner.…”
Section: Variance Analysis Of Experimental Resultsmentioning
In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.
“…During variance analysis on experimental results, mathematical statistics method is used to be sure whether differences in experimental results are incurred by differences in levels corresponding to factors or by experimental errors [52]. In this way, influence of various factors on experimental results may be analyzed in a more intuitive manner.…”
Section: Variance Analysis Of Experimental Resultsmentioning
In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.
“…The acid number of the oil will be determined through this analysis; if it finds a value that is not greater than 5, it will be possible to work without problems; acidity index values greater than 5 are not recommended for the production of biodiesel due to the high acid number; however, the use of this is recommended for other products other than biodiesel production. For the acidity test, 5 grams of sample was weighed and diluted with 50 mL of ethyl alcohol neutralized at 50 °C before being placed in an Erlenmeyer flask with a few drops of phenolphthalein and then titrated with potassium hydroxide solution 0.1 N [8]. Figure 1 shows the transesterification process.…”
We investigated how to extract energy from algal oil and convert it to biodiesel by transesterification in this study. In addition to performing engine performance tests with varying quantities of fuel mixture, parameters such as power, hourly torque consumption, specific consumption, and emissions of dioxide and monoxide were evaluated as an alternate solution to the pollution problem caused by fossil fuels. A 5.2 kW diesel engine powered the engine. The production of algal oil-based biodiesel was carried out effectively. The algal oil extraction procedure resulted in the production of biodiesel, which was then blended with commercial diesel in the amounts of 20, 40, and 60%. The engine performance testing revealed no statistically significant difference between the power and torque delivered by the various commercial diesel blends. Both hourly and particular consumption showed an increase of 15% and 20%, respectively, regarding commercial diesel consumption. However, the most significant advantage is the reduction in pollutant emissions, as demonstrated by the reduction in carbon dioxide emissions by more than 20% in any mixture of commercial diesel and biodiesel when compared to commercial diesel.
“…Increasing premixed ratios in RCCI mode decreased the PNC. 474 Due to charge homogeneity, lower AMPs were observed in high premixed ratio than in low premixed ratio cases.…”
Section: Physical Characterization Of Diesel Soot Particlesmentioning
Particulates from compression ignition (CI) engines have received serious attention in the last two decades. CI engines emit higher particulate matter (PM) and nitrogen oxides (NOx) than spark ignition (SI) engines. Both these species are harmful to human health and the environment. Compared to NOx emissions, PM constitutes many more chemical species in solid and liquid phases. This review paper focuses on soot morphology and chemical characterization of PM emissions from CI engines. Effects of different fuels, lubricating oil, and engine operating conditions on particulate characteristics are analyzed exhaustively. The first part of this paper focuses on the effects of particulates on living organisms, the consequences of exposure to diesel particulates, and the composition of diesel particulates. In recent decades, micro and nano-scale characteristics of PM have been exhaustively investigated to understand its structure, formation, and chemical functionalities. Typically, particulates comprise of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), the soluble organic fraction (SOF), and trace metals. This paper summarizes most aspects of diesel particulate emissions for the benefit of active researchers in the field and underlines the importance of particulate emission reduction from the CI engines. Diesel combustion generates particles with enormous long-chain aggregates of smaller sizes and immature soot particles. Low-temperature combustion (LTC) modes and oxygenated fuels reduce the soot emissions and generate compact/clustered aggregates. Oxygenated fuels in CI engines produce more nucleation mode particles (NMPs) and high-reactivity soot aggregates. Higher trace metal concentrations were observed in diesel origin particulates than biofuel origin particulates. Biodiesel origin particulates possess higher mutagenicity and carcinogenicity because of nitro-PAHs. Transient engine operations cause higher particulates than steady-state engine operations.
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