Engine Modelling for Control Applications: A Critical Survey

In earlier work published by the author and co-authors, a dynamic enginemodel called a Mean Value Engine Model (MVEM) was developed. This model isphysically based and is intended mainly for control applications. In itsnewer form, it is easy to fit to many different engines and requires littleengine data for this purpose. It is especially well suited to embedded modelapplications in engine controllers, such as nonlinear observer basedair/fuel ratio and advanced idle speed control. After a brief review of thismodel, it will be compared with other similar models which can be found inthe literature. The attempt will be made to point out the differencesbetween the new modified MVEM and those developed elsewhere. In particular,the questions of fitting simplicity and general applicability are to betreated.     

Development and Validation of Hierarchical Models for the Design of Engine Control Strategies

The development of mathematical models for the design of controlstrategies for spark ignition automotive engines is described. The objectiveof the models, used for both simulation and optimization analysis, is theprediction of the effects of control strategies on fuel consumption andemissions of a vehicle over arbitrary driving cycles. In order to achievethe best compromise between precision, experimental costs, computationaltime and flexibility, a mixed modelling approach is used, withphenomenological and input-output models integrated within a hierarchicalsystem.Mean value models have been used to describe the most significant dynamiceffects: (i) air flow. (ii) two phases fuel flow in the intake manifold, and(iii) thermal flow in the cylinder walls. Stochastic effects due to sensorsand actuators can be also predicted.Two-zone and multizone thermodynamic models for the prediction ofpressure cycle sub-models for engine emissions (HC, CO, andNO _x and mechanical losses have been developed. Experimentaldesign techniques are also under development to optimize the interactionsbetween experimental analysis and models. Most of the models have beenintegrated in a computer code, used by a major automotive supplier.     

Engine Control using Neural Networks: A New Method in Engine Management Systems

To be able to meet the demands of low emissions and fuel consumption ofmodern combustion engines, new ways have to be found to control thecombustion. We use new sensors to measure the pressure in the combustionchamber and analyze this signal with a neural network in order to receiveseveral form factors which can be used to control the ignition timing. Theneural network is trained off line with measured data and used on line toderive the form factors. The proposed algorithm can be computed in real timeon conventional digital signal processors and adapted to new engines withvery little effort.     

Joint RANS/LES Approach to Premixed Flame Modelling in the Context of the TFC Combustion Model

We present an original timesaving joint RANS/LES approach to simulate turbulent premixed combustion. It is intended mainly for industrial applications where LES may not be practical. It is based on successive RANS/LES numerical modelling, where turbulent characteristics determined from RANS simulations are used in LES equations for estimation of the subgrid chemical source and viscosity. This approach has been developed using our TFC premixed combustion model, which is based on a generalization of the Kolmogorov’s ideas. We assume existence of small-scale statistically equilibrium structures not only of turbulence but also of the reaction zones. At the same time, non-equilibrium large-scale structures of reaction sheets and turbulent eddies are described statistically by model combustion and turbulence equations in RANS simulations or follow directly without modelling in LES. Assumption of small-scale equilibrium gives an opportunity to express the mean combustion rate (controlled by small-scale coupling of turbulence and chemistry) in the RANS and LES sub-problems in terms of integral or subgrid parameters of turbulence and the chemical time, i.e. the definition of the reaction rate is similar to that of the mean dissipation rate in turbulence models where it is expressed in terms of integral or subgrid turbulent parameters. Our approach therefore renders compatible the combustion and turbulent parts of the RANS and LES sub-problems and yields reasonable agreement between the RANS and averaged LES results. Combining RANS simulations of averaged fields with LES method (and especially coupled and acoustic codes) for simulation of corresponding nonstationary process (and unsteady combustion regimes) is a promising strategy for industrial applications. In this work we present results of simulations carried out employing the joint RANS/LES approach for three examples: High velocity premixed combustion in a channel, combustion in the shear flow behind an obstacle and the impinging flame (a premixed flame attached to an obstacle).