Improved SI engine modelling techniques with application to fault detection

Vinsonneau, J. A. F.; Shields, D.N.; King, Paul J.; Burnham, Keith J.

doi:10.1109/cca.2002.1038689

Cited by 16 publications

(8 citation statements)

References 9 publications

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“…The value of the coefficient K thr depends on the opening rate of the throttle value and the values are presented in Table 1. Due to differences between experimental and numerical results, other authors like Vinsonneau et al (2002) have chosen to change the model in their engine simulation code. In this one, manifolds are modelled by using a filling and emptying method and the throttle valve mass flow is calculated by the use of a discharge coefficient which depends on the throttle valve angle and also the engine speed.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Unsteady Flow Through a Throttle Valve Using CFD

Chalet

Chessé

2010

Engineering Applications of Computational Fluid Mechanics

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In automotive application, modelling of the various singularities in pipe systems still remain challenging. The aim of this study is to develop a new throttle valve model which can be included in a one dimensional engine simulation code. First, experimental study was conducted to evaluate an existing literature model, and results show drawbacks in this model. Then after a new model was developed based on the experimental study and CFD analysis, with the throttle valve modeled as a function of flow characteristics and geometrical parameters. After including the new model in the engine simulation code, the engine performance at partial load was determined and results show good agreement with the experimental study.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Unsteady Flow Through a Throttle Valve Using CFD

Chalet

Chessé

2010

Engineering Applications of Computational Fluid Mechanics

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“…However, experimental data prove this approach to be rather inaccurate, as both throttle valve angle and pressure ratio seem coupled. Vinsonneau et al 15 proposed a polynomial equation for the discharge coefficient as a function of both engine speed and throttle angle. This type of estimation has many disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Improved throttle valve modeling for spark-ignition engine simulations

Haddad

Chalet

Chessé

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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Automotive manufacturers nowadays are constantly working on improving their internal combustion engines’ performance by reducing the fuel consumption and emissions, without compromising the power generated. Manufacturers are therefore relying on virtual engine models that can be run on simulation software in order to reduce the amount of time and costs needed, in comparison with experiments done on engine test benches. One important element of the intake system of an internal combustion engine is the throttle valve, which defines the amount of air reaching the plenum before being drawn into the cylinders. This article discusses a widely used model for the estimation of air flow rate through the throttle valve in an internal combustion engine simulation. Experiments have been conducted on an isolated throttle valve test bench in order to understand the influence of different factors on the model’s discharge coefficient. These experiments showed that the discharge coefficient varies with the pressure ratio across the throttle valve and with its angle. Furthermore, for each angle, this variation can be approximated with a linear model composed of two parameters: the slope and the Y-Intercept. These parameters are calibrated for different throttle valve angles. This calibration can be done using automotive manufacturers’ standard engine test fields that are often available. This model is then introduced into an engine simulation model, and the results are compared to the experimental data of a turbocharged engine test bench for validation. They are also compared with a standard discharge coefficient model that varies only with the throttle valve angle. The results show that the new model for the discharge coefficient reduces mass flow estimation errors and allows expanding the applications of the throttle valve isentropic nozzle model.

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“…Various forms and variants of computerized diagnostic systems that use fault trees or statistical methods or analytical techniques coupled with model based or signal-based approaches for fault detection have been reported in process control or even in automotive or aerospace applications [2][3][4][5][6]. The current paper presents the implementation study of Machine Learning techniques undertaken at our research labs towards a Fault Diagnosis System in a Manufacturing plant environment.…”

Section: Introductionmentioning

confidence: 99%

Application case study of machine learning techniques towards a fault diagnosis system for a manufacturing plant environment

Rao

Diwanji

Parthasarathi

2012

Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication

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Fault diagnosis is a vital problem in process engineering. It is the fundamental component of anomalous event management (AEM) which has attracted a lot of attention over recent years. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid anomalous event evolution, improve uptime and reduce efficiency loss. There is a great quantity of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches. From a modeling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models. At the other end of the gamut, there are methods that do not assume any form of model information and rely only on historical process data. In this paper we present the performance of few approaches of data driven modeling /machine learning techniques on the simulated data from a distillation column.

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Improved SI engine modelling techniques with application to fault detection

Cited by 16 publications

References 9 publications

Analysis of Unsteady Flow Through a Throttle Valve Using CFD

Analysis of Unsteady Flow Through a Throttle Valve Using CFD

Improved throttle valve modeling for spark-ignition engine simulations

Application case study of machine learning techniques towards a fault diagnosis system for a manufacturing plant environment

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