To further develop physics-based forest fire models, generic pinewood model trees are being studied both experimentally and numerically under controlled conditions. The precise design of the model trees, together with a standardised ignition procedure under dry conditions, allows for reproducible experiments. The numerical simulation is based on the coupling of thermally thick mesh-based solid particle models with a steady flamelet approach for the gas phase.

In a study recently accepted for the International Symposium on Combustion, Parsa Ghofrani examines the ignition dynamics of iron particle clouds. High-fidelity simulations reveal that the “rust,” or oxide layer, forming on iron particles acts as a critical barrier to efficient combustion and reliable ignition. As particles are heated within a turbulent flow, this oxide layer continues to grow; when heating is too slow, the layer thickens and significantly impedes ignition.

M.Sc. Maximilian Schäfer has developed a new method to enforce elemental and mass conservation in neural networks predicting chemical source terms. The approach applies a weighted projection as a final correction layer, ensuring strict physical consistency without modifying or retraining the neural network. Compared to current state-of-the-art conservation methods, the new technique reduces computational overhead by roughly an order of magnitude while maintaining high predictive accuracy.

Simulations by J.S. Tampah Fossi and A. Karimi Noghabi have been presented at the DECHEMA conference on February 26th. The work, a collaboration with Prof. Segets’ group (also EMPI), shows the effectiveness of a new rotating micro mixer that enables the transition from batch to continuous processing for the formation of nano-particles in the liquid phase.

Read the article here.