Prediction of Heat Release and Nox Emissions for Direct-Injection Diesel Engines Using an Innovative Zero-Dimensional Multi-Phase Combustion Model

Most of the current zero-dimensional (0D) predictive combustion models for diesel engines are based on their simple artificial assumptions rather than actual diesel spray processes, resulting in poor prediction accuracy. Conventional multi-zone models have good prediction performance but a long CPU time. This paper presents an innovative mechanism-based 0D multi-phase combustion model for direct-injection diesel engines. This model was primarily developed to reduce the CPU time for the one-dimensional (1D) simulation of engine performance and improve the optimization efficiency. The main idea of the model was accurately partitioning the spray zone according to different combustion mechanisms based on the concentration field of diesel spray. A representation method of each combustion phase based on the information of concentration field and equivalent injection velocity was proposed. In particular, some special modeling efforts were made to describe the diffusion combustion phase entirely and concisely. Diffusion combustion in different concentration areas exhibited different mechanisms, and diffusion combustion after the end of injection showed a slower burn rate than that during the injection process. All the different conditions of diffusion combustion were considered in this model based on the proposed representation method. Moreover, the in-cylinder temperature was divided into two zones to evaluate the NOx emissions. Extensive experiments were performed using two different diesel engines. The influence of engine speed, injection timing, load, and injection pressure on the combustion process and NOx emissions were investigated. High prediction accuracy was confirmed over a wide operating range. The prediction accuracy of both the heat release rate and NOx emissions of the proposed model was found to be comparable to that of a conventional multi-zone packet model. Compared with the packet model, the proposed model could reduce more than 90% of the CPU time, which significantly improved the engine performance simulation and optimization efficiency