Master Thesis Defense

Felix Luo

(Advisor: Prof. Suresh Menon)

"Evaluation of Convolutional Neural Networks for Modeling Blast Propagation in Multi-room Bunkers"

Monday, December 11

3:00 p.m.

Montgomery Knight Building 325

and

Virtual:

Microsoft Teams Meeting
Meeting ID: 247 946 590 597
Passcode: iVHfDi

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 blasts in enclosed spaces where spatial relationships between blast locations and wall locations greatly affect the overall blast response. This approach provides a nearly 50,000 times speed up with good convergence of errors, correlation coefficients, predicted and truth values and distributions in all situational evaluations. 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 key parameters 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 permits the rapid iterative design of more blast-resistant structures.

Committee

• Prof. Suresh Menon – School of Aerospace Engineering (advisor)
• Prof. Lakshmi Sankar– School of Aerospace Engineering
• Prof. Spencer Bryngelson– School of Mechanical Engineering