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

doi:10.1051/meca/2017029

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CAGIN, Stephanie
ESTIA - Institute of technology [ESTIA]
Institut de Mécanique et d'Ingénierie [I2M]

FISCHER, Xavier
ESTIA - Institute of technology [ESTIA]
Institut de Mécanique et d'Ingénierie [I2M]

DELACOURT, Eric

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Ce document a été publié dans

Mechanics & Industry. 2017, vol. 18, n° 5, p. 509

Résumé en anglais

In an optimization process, models are applied to simulate different design behaviors in order to determine the most suitable one. However, this requires the use of a structured methodology to correctly explore the design space and truly converge to the best solution. It is therefore necessary to test and validate the optimal design. For engines, two ways are essentially used: building and testing a real cylinder, or simulating the new design with Computational-Fluid-Dynamics (CFD) models. These two techniques are both expensive and time consuming. An alternative way is proposed to test new designs with a fast simulation based on a kriging method. The exploration of the design space is based on 27 cylinder configurations and the results of their CFD models. It converged to an optimal design depending on the objective function. A kriging method was used to interpolate the behavior of the optimal design just found. In this paper we present the β-NTF model reduction (to define the data set used by the kriging method) and the principle of the kriging technique. We then briefly discuss the results. The results underline the method's advantages despite the small gap between the expected results and those for kriging.< Réduire

Mots clés en anglais

kriging

fast simulation

β-NTF reduction

design space

2-stroke engine optimization