Prediction of Direct Initiation Requirements of Ethylene-Air Detonation

Prediction of direct initiation (DI) of detonation in a fuel-air mixture is hindered by the probabilistic nature of the outcome, which is a function of many variables including composition, temperature, pressure, and ignition energy. This paper presents a statistical surrogate model of this relationship in terms of the latter three variables for stoichiometric mixtures of ethylene and air. An idealized 2D piston-driven detonation tube was simulated at different conditions and diaphragm burst pressures and the database of DI outcomes was used to train a radial basis function neural network (RBFNN). The surrogate model identified the manifold of 50% DI probability and correctly expressed the fundamental outcome variability near the threshold. The necessary ignition pressure showed a negative exponential dependence on temperature and a complex dependence on pressure. The detonation velocity and detonation cell size were also obtained at every case for validation and verification, and a variation of the Cellpose-SAM neural network was used to automatically identify the detonation cells in 355 numerical soot foils with a high level of accuracy. The detonation cell size was used as a proxy to evaluate the CFD results and the surrogate model. Underprediction of the cell size indicates that the critical energy is also underpredicted, though the scope of this study allows the difference to be rigorously assessed.