Automotive Science and Engineering

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Turbocharging an engine boosts its power by increasing the amount of input air. This task is accomplished by using the exhaust gas to power a turbine which is engaged with a compressor. The Variable Geometry Turbocharger, VGT is a unique turbocharger that the diffuser vane angle can be changed over a wide range of positions. The mathematics of turbomachinery flow analysis is intensive and uses iterative methods. Most of the flow analyses in the area of turbochargers are either experimental or numerical. Three-dimensional Computational Fluid Dynamics (CFD), two-dimensional multiple streamline and one dimensional mean line is the three primary numerically available methods. In this paper a mean line method has been used for predicting the performance of a centrifugal compressor with variable diffuser vane angle position at subcritical Mach numbers. The calculation is based on common thermodynamic and aerodynamic principles, and empirical correlations for losses in a mean line analyses. The model calculates the velocities, pressures, temperatures, pressure losses, work consumption, and efficiencies for a specified set of turbocharger geometry, atmospheric conditions, rotational speed, and fluid mass flow rate. The obtained numerical results are validated with the in house measured experimental data and good agreement observed. The purpose for compressor model analysis is to generate overall characteristic map and identify the impact of the diffuser vane angles on the performance. The overall characteristic map is generated by this method demonstrate very good agreement and the effect of variable vane angle in pressure ratio and operating range observed.

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One of the factors that affects the efficiency and lifetime of spark ignited internal combustion engine is “knock”. Knock sensor is a commonly used to detect this phenomenon. However, noise, limits detection accuracy of this sensor. In this study, Empirical Mode Decomposition (EMD) method is introduced as a fully adaptive signal-based analysis. Then, based on weighting decompositions, a method for reducing knock signal noise to enhance detection accuracy of knock, has been proposed. Then, the presented method has been evaluated using recorded signals from four engine cylinders. Internal pressure of each cylinder were recorded and used as reference for knock detection. Test results verifies that knock detection accuracy improved by about 11.3%. The results of optimization method were consistent with our expectations and the weights of middle levels are higher than other levels, which means that the proposed method not only extracts the main frequencies of knock, but also assigns reasonable weights to them.

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Premature combustion that affects outputs, thermal efficiencies and lifetimes of internal combustion engine is called “knock effect”. However knock signal detection based on acoustic sensor is a challenging task due to existing of noise in the same frequency spectrum. Experimental results revealed that vibration signals, generated from knock, has certain frequencies related to vibration resonance modes of the combustion chamber. In this article, a new method for knock detection based on resonance frequency analysis of the knock sensor signal is introduced. More specifically at higher engine speed, where there is additional excitation of resonance frequencies, continuous wavelet transform has been proposed as an effective and applicative tool for knock detection and a formula for knock detection threshold based on this method is suggested. Measurement results demonstrate that this technique provide 15% higher accuracy in knock detection comparing to conventional method.