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Large eddy simulations of large-scale CH 4 fire plumes (1.59-2.61 MW) with two different CFD packages, FireFOAM and FDS, are presented. It is investigated how the vorticity generation mechanism and puffing behavior of largescale fire plumes differs from previously studied iso-thermal buoyant plumes of the same scale. In addition, the predictive capabilities of the turbulence and combustion models, currently used by the two CFD codes, to accurately capture the fire dynamics and the buoyancy-generated turbulence associated with large-scale fire plumes are evaluated. Results obtained with the two CFD codes, typically used for numerical simulations of fire safety applications, are also compared with respect to the average and rms velocities and temperatures, puffing frequencies, average flame heights and entrainment rates using experimental data and well-known correlations in literature. Furthermore, the importance of the applied reaction time scale model in combination with the Eddy Dissipation Model is examined. In particular, the influence of the considered mixing time scales in the predicted centerline temperatures is illustrated and used to explain the discrepancies between the two codes.

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The paper presents a detailed sensitivity analysis on the volume flux probability density function (PDF) to represent water spray patterns with computational fluid dynamics (CFD). The effects of the turbulent viscosity model and the cell size are also investigated. The test case considered herein is a 30 • full cone water mist spray emerging from a nozzle that operates at a pressure of 750 kPa and delivers a water flow rate of 0.084 lpm. The errors solely induced by the limited number of computational droplets per second, N p , are proportional to 1/ N p and could reach up to 35 %. The computational time generally increases linearly with N p. The paper illustrates also the better numerical performance of the lognormal-Rosin-Rammler droplet size distribution over the Rosin-Rammler distribution, especially in terms of reaching a converged volume-median diameter with increased N p. Furthermore, a uniform angular distribution is shown to provide results in better agreement with experimental data than a Gaussian-type distribution for the case at hand. For a sufficiently fine grid, the dynamic Smagorinsky and the modified Deardorff models converge to similar radial profiles of the water volume flux at 300 mm from the nozzle, with a deviation of less than 6% from the experiments. The deviations for the volume-median diameter are about 50% in the core region of the spray.

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This paper provides a report of the discussions held at the first workshop on Measurement and Computation of Fire Phenomena (MaCFP) on June 10–11 2017. The first MaCFP work-shop was both a technical meeting for the gas phase subgroup and a planning meeting for the condensed phase subgroup. The gas phase subgroup reported on a first suite of experimental- computational comparisons corresponding to an initial list of target experiments. The initial list of target experiments identifies a series of benchmark configurations with databases deemed suitable for validation of fire models based on a Computational Fluid Dynamics approach. The simulations presented at the first MaCFP workshop feature fine grid resolution at the millimeter- or centimeter- scale: these simulations allow an evaluation of the performance of fire models under high-resolution conditions in which the impact of numerical errors is reduced and many of the discrepancies between experimental data and computational results may be attributed to modeling errors. The experimental-computational comparisons are archived on the MaCFP repository [1]. Furthermore, the condensed phase subgroup presented a review of the main issues associated with measurements and modeling of pyrolysis phenomena. Overall, the first workshop provided an illustration of the potential of MaCFP in providing a response to the general need for greater levels of integration and coordination in fire research, and specifically to the particular needs of model validation.