Cascade computational model for prediction impact of transient depth change on combustion parameters of certain timber species under continuous heating rate

Abstract

The purpose of this research is to examine the effects of depth variation and exposure duration on the combustion characterization of the Maple wood species (Acer platanoides L.) and Walnut (Juglans regia L.) according to ISO 5660–1 in case of exposing the specimens to a constant heating rate 50 kWm−2. The delivered experimental data by cone calorimeter apparatus is used to train, test, and validate the optimized cascade forward artificial neural network with topology 2–8–8–12–2. This structure is simulated to predict the target parameters: the heat release rate (HRR) and the mass burning rate (MBR). With real systems, it is possible to achieve a reasonable performance and the closest approximations for the cascade model. The experimental combustion parameters: time of flame out, average specific mass burning rate, total heat release, maximum average rate of heat emission, and fire growth index are considered to make a systematic analysis and assessment with change depth. Three regions are considered for investigation of output target curves, thermally thin (d ≤ 1.1 cm), thermally medium (1.1 cm < d< 1.5 cm), thermally thick (d≥ 1.5cm). The findings demonstrate that independent of exposure period, an increase in depth change causes a sizable reduction in the HRR and MBR. Beyond 1.5 cm of depth in thermally thick regions, there is no discernible effect of time instant on changes in target parameters. In contrast, HRR and MBR gradients across depth in thermally thin regions are striking.