His research interests include subgrid-scale models and numerical methods for large-eddy simulation, adaptive mesh refinement, immersed boundary methods, and Lagrangian particle methods.

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The paper highlights key components of a fire model validation study conducted by the U.S. Nuclear Regulatory Commission and the Electric Power Research Institute. These include the selection of fire phenomena of interest to nuclear power plant safety, the selection of appropriate models, the selection of relevant experimental data, and the selection of appropriate evaluation criteria. For each model and each quantity of interest, there are two metrics of accuracy. The first is a bias factor, which indicates the extent to which the model tends to over or under-predict the given quantity. The second is a relative standard deviation, which indicates the degree of scatter in the predicted quantity when compared with experimental measurements. While the study is motivated by nuclear power plant safety, the general procedure and results are appropriate for most industrial applications.

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Abstract. A ubiquitous source of uncertainty in fire modeling is specifying the proper heat release rate (HRR) for the fuel packages of interest. An inverse HRR calculation method is presented to determine an inverse HRR solution that satisfies measured temperature data. The methodology uses a predictor-corrected method and the Consolidated Model of Fire and Smoke Transport (CFAST) zone model to calculate hot gas layer (HGL) temperatures in single compartment configurations. The inverse method runs at super-real-time speeds while calculating an inverse HRR solution that reasonably matches the original HRR curve. Examples of the inverse method are demonstrated by using a multiple step HRR case, complex HRR curves, experimental temperature data with a constant HRR, and a case with an experimentally measured HRR. In principle, the methodology can be applied using any reasonably accurate fire model to invert for the HRR.