An Investigation towards Coupling Molecular Dynamics with Computational Fluid Dynamics for Modelling Polymer Pyrolysis

Building polymers implemented into building panels and exterior façades have been determined as the major contributor to severe fire incidents, including the 2017 Grenfell Tower fire incident. To gain a deeper understanding of the pyrolysis process of these polymer composites, this work proposes a multi-scale modelling framework comprising of applying the kinetics parameters and detailed pyrolysis gas volatiles (parent combustion fuel and key precursor species) extracted from Molecular Dynamics models to a macro-scale Computational Fluid Dynamics fire model. The modelling framework was tested for pure and flame-retardant polyethylene systems. Based on the modelling results, the chemical distribution of the fully decomposed chemical compounds was realised for the selected polymers. Subsequently, the identified gas volatiles from solid to gas phases were applied as the parent fuel in the detailed chemical kinetics combustion model for enhanced predictions of toxic gas, charring, and smoke particulate predictions. The results demonstrate the potential application of the developed model in the simulation of different polymer materials without substantial prior knowledge of the thermal degradation properties from costly experiments.

Mathematical Modelling of Turbulent Combustion of Two-Phase Mixtures of Gas and Solid Particles with a Eulerian–Eulerian Approach: The Case of Hydrogen Combustion in the Presence of Graphite Particles

The numerical modelling of turbulent combustion of H2–air mixtures with solid graphite particles is a challenging and key issue in many industrial problems including nuclear safety. This study presents a Eulerian–Eulerian model based on the resolution of the Navier–Stokes equations via large eddy simulation (LES) coupled with a system of ordinary differential equations (ODEs) of the detailed chemical kinetics to simulate the combustion of mixtures of gases and particles. The model was applied to predict the transient evolution of turbulent combustion sequences of mixtures of hydrogen, air and graphite particles under low concentration conditions. When applied to simulate lab-scale combustion experiments, the results showed a good agreement between experimental and numerical data using a detailed chemical kinetic model. Moreover, the model was able to predict some key experimental tendencies and revealed that the presence of a low concentration of graphite particles (~96 g/m3) in the scenario influenced the hydrogen combustion dynamics for mixtures of 20% (in volume) of hydrogen in air. Under these conditions, pressure levels reached at the walls of the sphere were increased and the combustion time was shortened. The results also showed the viability of using this kind of a model for obtaining global combustion parameters such as wall pressure evolution with time.

A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs

High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinetics and traditional artificial neural network layers are used to enable the usage of GPU-optimized linear algebra libraries without the need for modeling. Regarding GPU performance, speedup and saturation behaviors are assessed for several chemical mechanisms of varying complexity. The performance analysis is based on trends for absolute compute times and throughput for the various arithmetic operations encountered during the source term computation. The goals are ultimately to provide insights into how the source term calculations scale with the reaction mechanism complexity, which types of reactions benefit the GPU formulations most, and how to exploit the matrix-based formulations to provide optimal speedup for large mechanisms by using sparsity properties. Overall, the GPU performance for the species source term evaluations reveals many informative trends with regards to the effect of cell number on device saturation and speedup. Most importantly, it is shown that the matrix-based method enables highly efficient GPU performance across the board, achieving near-peak performance in saturated regimes.

Effect of Temperature Conditions on Flame Evolutions of Turbulent Jet Ignition

Turbulent jet ignition technology can significantly improve lean combustion stability and suppress engine knocking. However, the narrow jet channel between the pre-chamber and the main chamber leads to some difficulties in heat exchange, which significantly affects combustion performance and mechanical component lifetime. To clarify the effect of temperature conditions on combustion evolutions of turbulent jet ignition, direct numerical simulations with detailed chemical kinetics were employed under engine-relevant conditions. The flame propagation in the pre-chamber and the early-stage turbulent jet ignition in the main chamber were investigated. The results show that depending on temperature conditions, two types of flame configuration can be identified in the main chamber, i.e., the normal turbulent jet flame propagation and the spherical flame propagation, and the latter is closely associated with pressure wave disturbance. Under low-temperature conditions, the cold jet stoichiometric mixtures and the vortexes induced by the jet flow determine the early-stage flame development in the main chamber. Under intermediate temperature conditions, pre-flame heat release and leading pressure waves are induced in the jet channel, which can be regarded as a transition of different combustion modes. Whereas under high-temperature conditions, irregular auto-ignition events start to occur, and spherical flame fronts are induced in the main chamber, behaving faster flame propagation.

Effects of five different parameters on biodiesel HCCI combustion in free-piston engine generator

A coupled 3-D CFD and detailed chemical kinetics model of free-piston engine generator (FPEG) was adopted to investigate the effects of initial parameters on homogeneous charge compression ignition (HCCI) combustion and emission. Biodiesel with 115 species skeletal mechanism was selected as fuel. Five different parameters, namely the initial pressure, the initial temperature, the working frequency, the compression ratio and the fuel equivalence ratio, were selected to analyze their influences in the HCCI combustion simulation of FPEG. The simulation results showed that the change of the five parameters had visible impact on the heat release rate of HCCI combustion, which caused the in-cylinder temperature and pressure to change, and also caused the emission content of NOx and SOOT to change obviously.