CAGIN, Stéphanie; FISCHER, Xavier; DELACOURT, Eric; BOURABAA, Nachida; MORIN, Céline; COUTELLIER, Daniel; CARRE, Bertrand; LOUMÉ, Sylvain

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CAGIN, Stéphanie
ESTIA INSTITUTE OF TECHNOLOGY

FISCHER, Xavier
ESTIA INSTITUTE OF TECHNOLOGY

DELACOURT, Eric
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
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International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering. 2015, vol. 9, n° 9, p. 1684 - 1693

World Academy of Science, Engineering and Technology

Résumé en anglais

The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).< Réduire

Mots clés en anglais

Scavenging

Neural network

NTF algorithm

Diesel engine

Design optimization

Model reduction

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Specialized Reduced Models of Dynamic Flows in 2-Stroke Engines

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