Fire modeling software has become increasingly important for the prediction of fire development in industrial and transportation accident scenarios. A key parameter required as input to fire models is the rate at which the fuel load is consumed, often reported as the fuel regression rate. This variable is linked to the energy output of the fire in a tightly coupled, cyclical fashion [1] and, for the most part, must be determined experimentally for model verification. Measurement of fuel regression rate proves to be difficult and is further complicated by the fact that ambient winds are typically significant in large accident scenarios. Wind is known to greatly affect fire behavior [2,3].

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