Turbulence Intensity Modeling of In-Cylinder Swirl Flow Using GA/SVD Designed Polynomial Neural Network

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Some aspects of in-cylinder swirl flow in reciprocating internal combustions engine have been investigated employing Laser Doppler Velocimetry (LDV) and modelled using generalized GMDH-type (Group Method of Data Handling) neural networks. Genetic Algorithm (GA) and Singular Value Decomposition (SVD) are applied simultaneously for optimal design of both connectivity configuration and the values of coefficients involved in GMDH-type neural networks to model turbulence intensity of the swirl flow. In particular, the aim of such modelling is to show how the turbulence intensity changes with the variation of important parameters involved in the swirl flow. In this way, a new encoding scheme is presented to genetically design the generalized GMDH-type neural networks in which the connectivity configuration in such networks is not limited to adjacent layers. Such generalization of network's topology provides optimal networks in terms of hidden layers and/or number of neurons so that a polynomial expression for the turbulence intensity can be achieved consequently.
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Swirl Flow; LDV; Genetic Algorithms (GAs); GMDH; SVD

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