{"title":"On the Intermediate-Field Blast Wave Shielding Effect of a Porous Wall","authors":"Gérard-Philippe Zéhil","doi":"10.1109/ACTEA58025.2023.10193988","DOIUrl":null,"url":null,"abstract":"Coupled Eulerian-Lagrangian (CEL) simulations are designed and executed in this work to explore the blast wave shielding effect of a porous spatially-periodic wall in the intermediate field of a large-scale explosion. To this aim, the incident peak average overpressure and the average specific positive impulse are determined over virtual planes located at various distances behind rigid walls of different porosity levels. A resulting manifold of high-cost high-fidelity numerical solutions is then used to devise simplified and more computationally-efficient data-driven surrogate analytical and machine-learning predictive models.","PeriodicalId":153723,"journal":{"name":"2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACTEA58025.2023.10193988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Coupled Eulerian-Lagrangian (CEL) simulations are designed and executed in this work to explore the blast wave shielding effect of a porous spatially-periodic wall in the intermediate field of a large-scale explosion. To this aim, the incident peak average overpressure and the average specific positive impulse are determined over virtual planes located at various distances behind rigid walls of different porosity levels. A resulting manifold of high-cost high-fidelity numerical solutions is then used to devise simplified and more computationally-efficient data-driven surrogate analytical and machine-learning predictive models.