The objective of the work being conducted by Protection Engineering Consultants (PEC) for the Combating Terrorism Technical Support Office (CTTSO) and the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) is to utilize the large database being developed by the Energetic Materials Research and Test Center (EMRTC) to evaluate the accuracy of existing CFD codes to predict overpressure in an urban environment. This paper will summarize the data analysis methods being used and the comparison results for several scenarios tested to date. Two modelers are currently using their CFD codes to analyze selected urban environment and charge configurations to develop pressure histories at locations corresponding to actual gauge locations from the EMRTC tests. Options for additional modelers’ participation may be implemented in the future.
PEC has analyzed predictions from Applied Research Associates (ARA) using their Second Order Hydrodynamic Automatic Mesh Refinement (SHAMRC) code and from SAIC using their Finite Element (Navier Stokes) Flow Solver (FEFLO) code. Six unique scenarios have been compared to test results thus far. All six scenarios are for conventional cylindrical and spherical C4 explosive charges at two different heights of burst and at varying locations within the urban canyon test facility.
As the first step in the effort, PEC has established the metrics to be used to compare the analytical predictions to the experimental data. An extensive study of many possible statistical metrics helped PEC determine which ones would provide CTTSO, DHS, and the modelers the most useful indication of how well each code is performing. PEC is responsible for acquiring and organizing the experimental and analytical data, assessing the performance of each predictive code using the established metrics, and creating a controlled repository for developed and derived data. This approach has resulted in: 1) a consistent application of all data analysis and evaluation procedures, and, 2) an unbiased assessment of the predictive code performance.