In order to build spray model of aviation fuel for the high-fidelity numerical simulation of spray combustion

a surrogate formulation methodology was proposed for coal-based jet fuel based on artificial neural network mixture model. An implicit prediction model was developed on the blended physico-chemical properties using the multi-component fuel properties data set to train the neural network. And then the surrogate of coal-based jet fuel was formulated from the neural network mixing model by the stochastic points' optimization method

which could well simulate the target physico-chemical properties focusing on its atomization.Result shows that the surrogate is composed of 5 hydrocarbons(n-decane

n-dodecane

n-tetradecane

iso-octane and methylcyclohexane)

their mole fraction are 11.46%

23.29%

49.87%

6.66% and 8.72%

respectively. Compared with the real fuel

the atomization simulation of the surrogate was evaluated by experiments. This surrogate formulation methodology can solve the nonlinear issue in the mixing process

and formulate different surrogates for various requirements.