Research on Prediction of Ignition Delay Using Feedforward Neural Networks as Surrogate Model of CFD

Based on the decoupled n-dodecane skeletal mechanism and the computational fluid dynamics (CFD) numerical framework, a multilayer feedforward neural network surrogate model was developed to predict ignition delay in a constant-volume combustion vessel. The Levenberg–Marquardt optimizer with adaptive damping coefficients was used for model training, with mean squared error as the loss function and an inherent early stopping mechanism to prevent overfitting without additional weight decay regularization. To eliminate random interference from initial parameter settings, the surrogate model underwent 1000 repeated training trials, each with random weight re-initialization. The effects of hidden neurons, data partition strategy, normalization scheme, and sample size on predictive performance were systematically examined. The optimal configuration—three hidden neurons, a 70:15:15 data split, and a 105-sample training set—showed low sensitivity to data normalization. The resulting surrogate model is concise and sample-efficient, maintaining satisfactory prediction accuracy at 800 K and 1100 K while substantially reducing computational overhead. It provides a practical and reliable tool for subsequent combustion prediction and uncertainty quantification of hydrocarbon fuels. The feedforward neural network surrogate model substantially cuts the computational overhead for fuel combustion prediction to merely 15–20 min for every batch of 60 samples.