Accurate prediction of equilibrium combustion products and thermodynamic properties is essential for optimizing engine performance, enhancing combustion efficiency, and reducing emissions in diesel-powered systems. Traditional methods for combustion modeling often involve solving complex chemical equilibrium equations or thermodynamic relations, which could be computationally expensive and time-consuming. In this study, we present a data-driven approach using a deep neural network (DNN) model to predict the equilibrium combustion products and key thermodynamic characteristics of diesel under varying thermodynamic conditions.
The proposed DNN model is trained on a comprehensive dataset generated from equilibrium calculations. The inputs include pressure, temperature, and equivalence ratio, covering a relatively wide range to encompass diesel equilibrium combustion under various conditions. Outputs are equilibrium combustion products and thermodynamic properties, including enthalpy, internal energy, specific volume, entropy, specific heat, and mole fractions of ten primary combustion species. The model is built using TensorFlow/Keras with a three-layer feedforward architecture and optimized using the Adam optimizer. Input and output variables are normalized using standard scaling, and model performance is evaluated using MSE, MAE, and R² metrics.
Results show that the DNN achieves high accuracy across most outputs, with R² values exceeding 0.99 for major thermodynamic variables, and provides fast inference suitable for real-time applications. This study shows that deep learning provides a viable and efficient alternative to traditional equilibrium solvers for combustion modeling, with potential applications in engine control, system-level simulations, and design optimization.