The Optimization of Intake Port using Genetic Algorithm and Artificial Neural Network for Gasoline Engines

Yanzhe Sun; Tianyou Wang; Zhen Lu; Lei Cui; Ming Jia

doi:10.4271/2015-01-1353

The flow performance of intake port significantly affects engine output power, fuel economy, and exhaust emissions in gasoline engines. Thus, optimal intake port geometry is desired in gasoline engines. To optimize the flow performance of intake port, a new optimization method combining genetic algorithm (GA) and artificial neural network (ANN) was proposed.

First, an automatic system for generating the geometry of the tangential intake port was constructed to create various port geometries through inputting the 18 pre-defined structural parameters.

Then, the effects of four critical structural parameters were investigated through numerical simulation. On the basis of the computational results, an ANN was used to model the flow performance of the intake port, and a genetic algorithm was simultaneously employed to optimize the flow performance by optimizing the four important structural parameters.

Finally, the optimization results were verified through numerical simulation. The results show that, compared to the original design, the tumble ratio significantly increases (about 6.12%), while flow coefficient remains nearly unchanged in the new design of the intake port.

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The Optimization of Intake Port using Genetic Algorithm and Artificial Neural Network for Gasoline Engines 2015-01-1353

SAE MOBILUS