Characterising the friction and wear between the piston ring and cylinder liner based on acoustic emission analysis

In this paper, an experimental investigation was carried out to evaluate the friction and wear between the cylinder liner and piston ring using acoustic emission (AE) technology. Based on a typical compression ignition (CI) diesel engine, four types of alternative fuels (Fischer-Tropsch fuel, methanol-diesel, emulsified diesel and standard diesel) were tested under dif-ferent operating conditions. AE signals collected from the cylinder block of the testing en-gine. In the meantime, the AE signals in one engine cycle are further segregated into small segments to eliminate the effects of valve events on friction events of cylinder liner. In this way, the resulted AE signals are consistent with the prediction of hydrodynamic lubrication processes. Test results show that there are clear evidences of high AE deviations between dif-ferent fuels. In particular, the methanol-diesel blended fuel produces higher AE energy, which indicates there are more wear between the piston ring and cylinder liner than using standard diesel. On the other hand, the other two alternative fuels have been found little dif-ferences in AE signal from the normal diesel. This paper has shown that AE analysis is an ef-fective technique for on-line assessment of engine friction and wear, which provides a novel approach to support the development of new engine fuels and new lubricants

Ramalan Dan Ka Walan Keluaran Nox Dari Enjin Diesel Satu Selinder Menggunakan Rangkaian Neural Buatan

Kerja tesis ini berkenaan dengan kajian ujikaji dan simulasi komputer yang digunakan untuk membina model-model yang sesuai untuk ramalan dan kawalan keluaran nitrogen oksida (NOx) daripada enjin Diesel Yanmar L60AE-D satu selinder suntikan terus. Hari ini, enjin Diesel merupakan antara loji kuasa yang terbaik di kalangan semua jenis enjin pembakaran dalam. Walau bagaimanapun, NOx yang terkandung di dalam gas ekzos enjin Diesel telah dikenal pasti sebagai elemen yang bertanggungjawab mencemarkan atmosfera kita dan menyebabkan masalah-masalah kesihatan. Untuk mengurangkan keluaran pencemaran enjin Diesel, dua aplikasi berdasarkan kepada model-model rangkaian neural buatan telah dibangunkan. Aplikasi pertama adalah untuk mendapatkan model ramalan kepekatan keluaran NOx di bawah keadaan kendalian pelbagai. This thesis work concerns with an experimental and computer simulation studies used to develop suitable models to predict and control the oxides of nitrogen (NOx) emitted from the Yanmar L60AE-D single cylinder direct injection diesel engine, fitted in a Cusson's Engine Test Bed Model P8160. Today, diesel engine is the most efficient power plant among all known types of internal combustion engines. However, the NOx contained in the exhaust gases of diesel engines have been identified as elements responsible for polluting our atmosphere and causing health problems. In order to reduce diesel engine polluting emissions, two applications based on artificial neural network models have been developed. The first application is to obtain the prediction model of NOx emission concentration under various operating condition

Non-parametric models in the monitoring of engine performance and condition: Part 2: non-intrusive estimation of diesel engine cylinder pressure and its use in fault detection

An application of the radial basis function model, described in Part 1, is demonstrated on a four-cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the trained model are validated against measured data and an example is given of the application of this model to the detection of a slight fault in one of the cylinders

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Valve recession: From experiment to predictive model

Increasing demands on engine performance and cost reductions have meant that advances made in materials and production technology are often outpaced This frequently results in wear problems occurring with engine components. Few models exist for predicting wear, and consequently each wear problem has to be investigated, the cause isolated and remedial action taken. The objective of this work was to carry out experimental studies to investigate valve and seat insert wear mechanisms and use the test results to develop a recession prediction tool to assess the potential for valve recession and solve problems that occur more quickly. Experimental apparatus has been developed that is capable of providing a valid simulation of the wear of diesel automotive inlet valves and seats. Test methodologies developed have isolated the effects of impact and sliding. A semi-empirical wear model for predicting valve recession has been developed based on data gathered during the bench testing. A software program, RECESS, was developed to run the model. Model predictions are compared with engine dynamometer tests and bench tests. The model can be used to give a quantitative prediction of the valve recession to be expected with a particular material pair or a qualitative assessment of how parameters need to be altered in order to reduce recession. The valve recession model can be integrated into an industrial environment in order to help reduce costs and timescales involved in solving valve/seat wear problems

Heavy Duty Vehicle Fuel Consumption Modelling Using Artificial Neural Networks.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly, a transient state test was performed, in order to evaluate the polynomial regression and 25 ANN models with different parameters. Based on the results, the best ANN model was chosen. Then, validation test was conducted using real duty cycle loads for model comparison. The neural network model outperformed the conventional method and represents fuel consumption of the engine operating in transient states significantly better. The presented method can be applied in order to reduce fuel consumption in utility vehicles delivering accurate fuel economy model of truck engines, in particular in low engine speed and torque range

A comparative study on mean value modelling of two-stroke marine diesel engine

In the present paper, two mean value modelling approaches of varying complexity, capable of simulating two-stroke marine Diesel engines, are presented. Both approaches were implemented in the computational environment of MATLAB Simulink®. Simulation runs of transient operation cases of a large two-stroke marine Diesel engine were performed. The derived results were validated against previously published data are used for comparing the two modelling approaches and discussing the advantages and drawbacks of each

Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Modelling of Diesel fuel properties through its surrogates using Perturbed-Chain, Statistical Associating Fluid Theory

The Perturbed-Chain, Statistical Associating Fluid Theory equation of state is utilised to model the effect of pressure and temperature on the density, volatility and viscosity of four Diesel surrogates; these calculated properties are then compared to the properties of several Diesel fuels. Perturbed-Chain, Statistical Associating Fluid Theory calculations are performed using different sources for the pure component parameters. One source utilises literature values obtained from fitting vapour pressure and saturated liquid density data or from correlations based on these parameters. The second source utilises a group contribution method based on the chemical structure of each compound. Both modelling methods deliver similar estimations for surrogate density and volatility that are in close agreement with experimental results obtained at ambient pressure. Surrogate viscosity is calculated using the entropy scaling model with a new mixing rule for calculating mixture model parameters. The closest match of the surrogates to Diesel fuel properties provides mean deviations of 1.7% in density, 2.9% in volatility and 8.3% in viscosity. The Perturbed-Chain, Statistical Associating Fluid Theory results are compared to calculations using the Peng–Robinson equation of state; the greater performance of the Perturbed-Chain, Statistical Associating Fluid Theory approach for calculating fluid properties is demonstrated. Finally, an eight-component surrogate, with properties at high pressure and temperature predicted with the group contribution Perturbed-Chain, Statistical Associating Fluid Theory method, yields the best match for Diesel properties with a combined mean absolute deviation of 7.1% from experimental data found in the literature for conditions up to 373°K and 500 MPa. These results demonstrate the predictive capability of a state-of-the-art equation of state for Diesel fuels at extreme engine operating conditions