Abstract

The cone calorimeter is a standard apparatus used to evaluate how materials respond to heat and under controlled conditions. Accurate characterization of the radiative heat flux is critical, as it directly influences ignition behavior, burning rates, and flammability metrics. This study extends a previous factor model by incorporating the effect of sample inclination on the radiative heat flux distribution. mathematical formulation (MF) of the view factor between the cone heater and inclined samples is developed using contour integrals derived from Stokes' theorem. To verify its accuracy, detailed Monte Carlo (MC) tracing simulations are performed, directly modeling the cone heater's helical coil geometry. The MF and predictions are in close agreement under baseline alignment, with a mean relative difference of 0.49% 95% of spatial heat flux values within +/- 5%. Similar agreement is observed across a range of tested inclinations, demonstrating that the MF accurately captures the radiative heat flux distribution. Given its substantially computational cost compared to the MC approach, the MF offers an efficient and reliable tool for modeling heat flux in cone calorimeter experiments. Additionally, a normalized coefficient of variation (Cv*) is introduced to quantify spatial non-uniformity, showing that inclination generally increases flux variability. These highlight the importance of accounting for sample inclination when interpreting cone calorimeter data establish the MF as a practical method for efficient, accurate radiative heat flux prediction.