Mathematical models of flame dynamics in natural fires require solving complex mechanisms and involve large and small, length and time scales. These models demand heavy resources and computational time periods that are far greater than the time associated with the processes being simulated (hours to model seconds). If comprehensive computational models are ever to be used to estimate, forecast and understand fire growth in support of emergency response, the computational time has to be shorter than the event itself: super-real time. A mechanism to achieve these computational speeds is by means of theoretical models steered by continuous calibration against sensor measurements, In this paper, the concept of super-real time predictions steered by measurements is studied in the simple yet meaningful scenario of upward flame spread. Experiments have been conducted with PMMA slabs to feed sensor data into a simple analytical model. The sample is 150 mm wide, 40 mm thick and 200 mm tall. CCD cameras and thermocouples embedded into the solid provide estimates of the evolution of the pyrolysis front. Heat flux gauges estimate the radiative heat flux from the flame to the solid. Flame height is measured with CCD cameras, and a PIV system is used to characterize the flow field. A simple algebraic expression from the literature linking flame spread, flame characteristics and pyrolysis evolution has been used to model upward flame spread. The measurements are continuously fed to the computations so that projections of the frame spread velocity can be established at each instant in time, ahead of the real flame. It was observed that as the input parameters in the analytical models were optimised to the scenario, rapid convergence between the evolving experiment and the predictions was attained.
|Number of pages||11|
|Journal||Turkish Journal of Engineering and Environmental Sciences|
|Publication status||Published - 1 Dec 2007|
- Flame spread
- Sensor driven prediction