The Validation of a Two-Dimensional Transient Catalyst Model for Direct Injection Two-Stroke Applications

This paper describes the detailed validation of a computer model designed to simulate the transient light-off in a two-stroke oxidation catalyst. A plug flow reactor is employed to provide measurements of temperature and gas concentration at various radial and axial locations inside the catalyst. These measurements are recorded at discrete intervals during a transient light-off in which the inlet temperature is increased from ambient to 300oC at rates of up to 6oC/sec. The catalyst formulation used in the flow reactor, and its associated test procedures, are then simulated by the computer and a comparison made between experimental readings and model predictions. The design of the computer model to which this validation exercise relates is described in detail in a separate technical paper. The first section of the paper investigates the warm-up characteristics of the substrate and examines the validity of the heat transfer predictions between the wall and the gas in the absence of chemical reactions. The predictions from a typical single-component CO transient light-off test are discussed in the second section and are compared with experimental data. In particular the effect of the temperature ramp on the light-off curve and reaction zone development is examined. An analysis of the C3H6 conversion is given in the third section while the final section examines the accuracy of the light-off curves which are produced when both CO and C3H6 are present in the feed gas. The analysis shows that the heat and mass transfer calculations provided reliable predictions of the warm-up behaviour and post light-off gas concentration profiles. The self-inhibition and cross-inhibition terms in the global rate expressions were also found to be reasonably reliable although the surface reaction rates required calibration with experimental data.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.