流固耦合
结构工程
流离失所(心理学)
鉴定(生物学)
工程类
计算机科学
航程(航空)
有限元法
航空航天工程
心理学
植物
心理治疗师
生物
作者
Luca Lomazzi,David Morin,Francesco Cadini,Andrea Manes,Vegard Aune
标识
DOI:10.1177/20414196231198259
摘要
Blast events within urban areas in recent decades necessitate that protective design is no longer reserved for military installations. Modern civil infrastructure composed of light-weight, flexible materials has introduced the consideration of fluid-structure interaction (FSI) effects in blast-resistant design. While the action of blast loading on massive, rigid structures in military fortifications is well established, assessment of FSI effects is, at present, only possible through computationally expensive coupled simulations. In this study, a data-driven approach is proposed to assist in the identification of the blast-loading scenarios for which FSI effects play a significant role. A series of feed-forward deep neural networks (DNNs) were designed to learn weighted associations between characteristics of uncoupled simulations and a correction factor determined by the out-of-plane displacement arising from FSI effects in corresponding coupled simulations. The DNNs were trained, validated and tested on simulation results of various blast-loading conditions and material parameters for metallic target plates. DNNs exposed to mass-per-unit-area, identified as an influential factor in quantifying FSI effects, generalised well across a range of unseen data. The explainability approach was used to highlight the driving parameters of FSI effect predictions which further evidenced the findings. The ability to provide quick assessments of FSI influence may serve to identify opportunities to exploit FSI effects for improved structural integrity of light-weight protective structures where the use of uncoupled numerical models is currently limited.
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