Title:
Evaluation of Convolutional Neural Networks for Modeling Blast Propagation in Multi-room Bunkers
Evaluation of Convolutional Neural Networks for Modeling Blast Propagation in Multi-room Bunkers
Author(s)
Luo, Felix
Advisor(s)
Menon, Suresh
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Abstract
The rapid evaluation of blasts in enclosed geometrically complex spaces has long eluded the design of safer blast-resistant structures. Traditional methods of determining blast responses in enclosed geometrically complex spaces oftentimes rely on the use of traditional computational fluid dynamics (CFD) solvers to compute the entire flow field of the structure. This method has an enormous computational burden, especially considering that blasts are highly transient in nature and require the transient pressure fluctuations to be determined to formulate an accurate blast response prediction. However, more efficient methods of blast evaluation are desired such that parametric sweeps or optimization processes can be performed at low cost to provide a tool for iterative design. To rectify this gap in capabilities, a convolutional neural network based (CNN) model was developed to provide rapid blast predictions for 2D structures to establish this capability to aid in the design of more blast resistant structures. This approach leverages the inherent spatial awareness of CNNs to provide predictions for peak pressures since blasts in enclosed spaces are highly dependent on the spatial relationships between blast locations and wall location. This approach provides a nearly 5,000 times speed up against CFD simulations used within this study with good convergence of errors, correlation coefficients, predicted and truth values and distributions in all situational evaluations. These computational advantages, in part, comes from using the CNN based model to directly predict peak pressures whereas traditional CFD solvers require iterations to propagate fluid flows over time. However, some limitations do exist with respect to higher errors, such as model training costs, and the capability to predict 3D structures. Nonetheless, the results provide a characterization of the capabilities CNN based models in predicting peak pressures from blasts in enclosed spaces. From these evaluations and studies, a model which can provide significant computational savings while maintaining a similar accuracy can be obtained, which enables the rapid iterative design of more blast resistant structures.
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Date Issued
2023-12-15
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