Mass loss prediction of inclined fuel combustion using variable B-number theory

Author：Li, M., Wang, Y., Jiang, L., Gou, F. H., Sun, J. H.

Journal：Fuel

DOI:  10.1016/j.fuel.2021.122446

Keywords: Inclined flame spread, Width effect, B-number, Mass loss rate, Combustion efficiency, flame spread, sample orientation, fire spread, flow, surface, width

Abstract: 

Predictions of combustion behavior over solid fuel have long been goals for researchers in fuel science. This study theoretically and experimentally investigated the free buoyancy flame spread over translucent fuel (polymethyl methacrylate, PMMA) with different widths and inclinations, through developing global and effective mass loss rate prediction models based on the variable B-number theory. A total of 90 tests were performed: five in-clinations (30 degrees to 90 degrees from the horizontal) were selected to investigate inclined flame spread; in each inclination, six sample widths ranging from 2 to 7 cm were tested. The flame length, pyrolysis length, flame spread rate, and the mass loss rate of experimental data were investigated. Based on the reasonable approximations and as-sumptions for the free-convective laminar burning of inclined fuel sheet, theoretical and numerical relationships on flame standoff distance, global mass loss rate, and the lateral diffusion mass loss rate were obtained from the transform of the conservation equations. Then a methodology for calculating the experimental B-number was developed by processing the flame image sequences in the stable stage of the inclined flame spread. From the calculated B-number, a prediction model of the global mass loss rate was developed and validated. Moreover, with the consideration of the lateral diffusion mass loss rate, the global effective mass loss rate, the heat release rate, and the heat loss rate at the fuel surface were obtained and analyzed. The study provided an insight into the inclined laminar burning of solid fuel with free buoyancy in the aspect of heat and mass transfer, which may be applied in the prediction and assessment of the usage and recycling of energy sources in reality.