AI-Based Material Parameter Prediction from Cone Calorimeter Measurements

Abstract In pyrolysis simulation, methods such as inverse modelling are used to determine the material parameters from small-scale experiments. This often requires significant computational resources due to its iterative nature. This study investigates an artificial intelligence (AI)-based alternative approach, that can give instantaneous predictions for material parameters once trained. A dataset based on Fire Dynamics Simulator (FDS) simulation of a cone calorimeter experiment is used for training these AI models. Different AI models are trained to predict polymethyl methacrylate’s thermo-physical parameters using heat release rate (HRR) curves as input. AI models including Random Forest, 1D-convolution, and Recurrent Neural Networks showed the ability to predict the material parameters accurately with low mean squared error on the test dataset. These models were also able to recreate the HRR curves in FDS using their predictions, following the trend of the experimental HRR curve closely. Expanding the dataset to include materials with different behaviours and modelling different experiments could give these AI models broader applicability. However, FDS version dependence is a limitation for the AI models explored here because they were trained on a simulation-based dataset. Looking at the results, AI models in general can be used to predict material parameters required for pyrolysis modelling, potentially saving time and effort or, at the very least, used to complement the existing inverse modelling approaches.