First-principle simulation of blast barrier effectiveness for the development of simplified design tools
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Blast barrier walls have been shown to reduce the blast load on structures in most scenarios. Analysis of existing data for blast barrier response reveals that a need exists to determine the bounds of the problem and produce a fast-running accurate model for the effects of barrier walls on blast wave propagation. Since blast experiments are very time intensive and extremely cost prohibitive, it is vital that computational capabilities be developed to generate the required data set that can be utilized to produce simplified design tools. The combination of high fidelity first principles model-based simulation with artificial neural network techniques for providing solutions to blast barrier problems results in a very effective means to tackle the challenging problem. A review of current methods of modeling blast wave propagation identifies a need for a modeling approach that is both fast and versatile in its scope for application. Artificial neural network approaches to modeling the propagation of blast waves in a built-up environment are developed. A comprehensive study of numerical simulation approaches for modeling blast propagation is presented and applied to populating data for blast barrier site configurations. The proposed approach is demonstrated to estimate the peak pressure, impulse, time of arrival, and time of duration of blast loads on buildings protected by simple barriers, using data generated from validated computational hydrocode simulations. Once verified and validated, the proposed neural-network model-based simulation procedure provides an efficient engineering tool for predicting blast loads on structures which are protected by blast barrier walls.
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