Toward an Improved Processing Methodology for Mass Loss Rate Data in Fire Testing Procedures

Abstract

Time-resolved mass loss rate (MLR) is a calculated quantity used in fire safety science as a surrogate for the burning rate across various scales and standardized testing procedures. MLR data is generally characterized by a high degree of noise and is often smoothed when presented in literature. A recent study demonstrated that some smoothing techniques can heavily skew MLR data; thus, researchers should consider these skewing effects and choose an appropriate smoothing technique to most appropriately represent the MLR behavior for their application. This study presents both a residual analysis and an integral method (based on conservation of mass) to guide users in choosing an appropriate smoothing filter—both of which can be applied to any combination of differentiation and smoothing techniques the user chooses. Guidance is provided to quantify the observed noise in MLR to add clarity when comparing MLR data across studies or between experimental conditions. Additional consideration is given to discontinuities in mass data to mitigate the need for high degrees of smoothing. The guidance presented in this work provides a basis for a more intentional application of smoothing filters to mitigate unwanted error while enabling researchers to more transparently present MLR in literature.