The Effects of Biodiesel on NOx Emissions for Automotive Transport

Automobiles with internal combustion engine using diesel fuel have large harmful emissions of nitrogen oxides and soot, which affect the health of the population and especially children and carbon dioxide, which is dangerous for the planet as a whole. Biodiesel is used in Europe as an additive to diesel fuel to reduce soot emissions (including carcinogens), as well as to improve the balance of carbon dioxide on the planet. Using the biodiesel in internal combustion engines tends to show higher nitrogen oxides emissions compared to diesel. In this paper, the impact of flame temperature, ignition delay and density on NOx formation of biodiesel and its component for both stationary engine and automotive engine were analysed. Emissions of nitrogen oxides increase with increasing load. In no-load modes, biodiesel shows lower emissions of nitrogen oxides than diesel.

Application of an artificial neural network for determination of the cylinder-piston group wear level of the automobile-tractor engine

The article proposes reducing the redundancy of the neural network and the need to reduce the number of neurons in the hidden layer for a given level of network learning error. The minimum number of neurons of the hidden layer for the case of 11 monitoring standard sensors, the parameters of the automobile and tractor engine (ATE) and five classes of typical defects of the ATE nodes can be reduced to 5-7 with a high quality of recognition of the state of the ATE engine. The goal is to provide an expanded reliable knowledge base, the speed of information processing, the accuracy of the resulting technical diagnosis and the ability to quickly determine the technical state of an automotive engine in the mode real time. The basis of the proposed method is to ensure obtaining an extended reliable knowledge base, the speed of information processing, the accuracy of the obtained technical diagnosis and the ability to quickly determine the technical state of an ATE engine in real time. A feature of the proposed method is the use of voltages obtained in an artificial neural network from sensors that are standard in an ATE engine as input signals, and additionally indicate the output signal of the fuel cut-off device, provided for one step, containing a winding of a normally closed electromagnetic valve, which redirects fuel to the drain line. The use of the algorithm for identifying the values of the indicators of operating modes and malfunctions of the cylinder-piston group is the result of the analysis of an artificial neural network, which receive the results of the diagnostic parameters of the automotive engine. Having studied the artificial neural network 1 with different volumes of training data, we obtained the dependence of the change in the reliability of the result on the size of the training data and the reliability of the recognition result is 91.2%, the optimal amount of training data is 1200. Having examined the artificial neural network 2 with different volumes of training data, we obtained the dependence of the change in the reliability the result from the size of the training data and the reliability of the recognition result is 86.5%, the optimal amount of training data is from 10 to 15. The results obtained show the fundamental possibility of creating predictive models of units and assemblies of the tested automotive engines. The model can be created using the apparatus of artificial neural networks and using a fairly large database of tests performed.

Liquid-mediated particle capture by nonwoven filter media for automotive engine intake air filtration

This article reports on development, characterization, and performance of liquid-treated nonwoven air filter media for automotive engine intake application. A polypropylene fiber-based needle-punched nonwoven fabric was prepared for treatment with four viscous liquids (glycerol, SAE 20W/50 engine oil, PEG 400, and deionized water) by liquid spraying technique. The filtration performance was evaluated in terms of initial and final gravimetric filtration efficiencies, fractional filtration efficiency, evolution of pressure drop, and dust holding capacity. The liquid-treated filter media registered higher gravimetric as well as fractional filtration efficiency and higher dust holding capacity as compared to the untreated ones. The initial and final gravimetric filtration efficiencies were found to be directly related to liquid add-on via a power law relationship. The liquid-treated filter media also exhibited higher fractional filtration efficiency than their untreated counterparts for all sizes of tested particles. Interestingly, the increase of fractional efficiency was more for smaller particles as compared to larger ones. This was explained quantitatively through single fiber efficiency due to adhesion. The viscosity of liquid was found to be a very crucial parameter as the dust deposition morphology was contingent to the flow of liquid onto the filter media. The stickiest liquid yielded highest filtration efficiencies, displayed slowest rise of pressure drop, and exhibited highest dust holding capacity.

Novel Automotive Engine Shaft Made of Composite with and without Material Property Grading

Engine Shafts are a very critical component of Automotive and Aerospace. Their basic purpose is to transmit power by rotation. They suppose various parts like gears and pulleys, and they are supported by bearings, which reside in the rigid machine housing. In their operation, the shafts rotate and hence they are subjected to Torsion and Bending moments. Hence, it is critical for us to choose the best material and the surface treatment process to provide optimum performance for the shaft. The primary aspects to keep into consideration while choosing a material pertaining to surface selection are Wear Resistance and Corrosion resistance. It has been shown how the selection of shaft material affects these two factors. The shafts of 3 types of materials have been discussed in the paper, a homogeneous one, a composite material, and FGM. The best material considered is FGM for the optimum operation of the automotive engine shaft.

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.

Experimental and Theoretical Investigation of Vibro-Impact Motions of a Gear Pair Subjected to Torque Fluctuations to Define a Rattle Noise Severity Index

Vibro-impacts are common in various automotive engine and transmission gear applications. They are known to cause excessive noise levels, often called rattling or hammering. Input and output fluctuations acting on such systems cause tooth separations and sequences of impacts allowed by backlash at the gear mesh interfaces. The fluctuations leading gear rattling have often been studied for specific applications with the excitations produced typically by an internal combustion engine. As such, rattle evaluations have been often empirical and specific to the systems considered. In this study, an experimental test set-up of a gear pair is developed to emulate the same torque fluctuations in a laboratory environment. This set-up is used to establish an impact velocity-based rattle severity index defined by the measured torsional behavior of the drive train that is shown to correlate well with the measured sound pressure levels. With that, a validated dynamic model of the experimental setup is employed to predict the same index to allow estimation of rattle noise outcome solely from a torsional dynamic model of the drivetrain. Predicted rattle severity indexes are shown to agree well with the measured ones within wide ranges of torque fluctuations and backlash magnitudes, allowing an assessment of rattle performance of a drivetrain solely from a torsional model.

Assesment of the possibility of using nanomaterials as fuel additives in combustion engines

Nanomaterials are a new group that has quickly found a wide range of applications in medicine, cosmetology, the food, weapons or automotive industry. They are also used as a fuel additive. This paper reviews the literature and assesses the current state of knowledge regarding the use of nanoparticles in automotive engine fuels. The results obtained so far are presented and further research directions in this field are identified Conclusion: The results of the review showed a discrepancy, selected groups favor the reduction of harmful gas emissions, while others do not and even increase emissions, e.g. the use of carbon nanotubes contributes to the increase in the emission of environmentally harmful nitrogen oxides, while the presence of graphene oxide reduces it. An interesting observation is also the fact that groups such as titanium and graphene oxide reduce the emission of harmful carbon monoxide by improving fuel combustion from semi-combustion to complete combustion, but at the same time increase CO2 emissions, which in turn is a greenhouse gas The whole group of nanomaterials contributes to the reduction of hydrocarbon emissions Nanomaterials improve the quality of fuel combustion The review shows tests only on diesel and a mixture with biodiesel in the review there were no studies for gasoline

Surrogate-Based Optimization of a Centrifugal Pump with Volute Casing for an Automotive Engine Cooling System

This paper investigates the capability of a surrogate-based optimization technique for the advanced design of centrifugal pumps. The centrifugal pump considered in this work is designed for the automotive cooling system and consists of an impeller, a vaneless diffuser and a volute. A fully three-dimensional geometry parametrization based on Bézier surfaces is presented. The optimization procedure includes the following software packages: Scilab for the geometric parametrization, Ansys-CFX for the CFD simulations and DAKOTA for the optimization management. The initial geometry is defined by a 0D code that provides a preliminary design of the pump, given the operating conditions, i.e., the volumetric flow rate, the head and the rotating speed. In this work an operative point typical of high performance gasoline cars is considered.