Numerical study on the blending of excellent anti-knock fuel using artificial neural network

Abstract

In order to reduce emissions, the content of olefins and aromatic hydrocarbons in gasoline is required to be lower. The reduction of these two high-octane-number components increases the knock tendency of turbocharged engines, which limits thermal efficiency. Therefore, the optimization of the component proportion of gasoline in turbocharged GDI engines under the new gasoline standard is discussed in this paper. To blend the fuel with excellent anti-knock property, the influence of each component in the gasoline on the knock of turbocharged gasoline engine was analyzed by three-dimensional CFD simulation, and BP neural network was established based on a large number of data to predict the in-cylinder combustion of the fuels with different component proportion, which greatly saved the calculation cost. The results show that increasing of aromatic hydrocarbons and olefin in fuel can effectively improve the anti-knock property of fuel. Within the range of component proportion required by the Chinese gasoline standard specification, when the volume fraction of isooctane is 47%, n-heptane is 0, toluene is 35%, and diisobutylene is 18%, Pmax and MAPO value is the smallest, that is, the fuel has better anti-knock property.