{"title":"NEW PREDICTIVE MODELS FOR THE BALLISTIC LIMIT OF SPACECRAFT SANDWICH PANELS SUBJECTED TO HYPERVELOCITY IMPACT","authors":"A. Cherniaev, R. Carriere","doi":"10.2495/hpsu220091","DOIUrl":null,"url":null,"abstract":"Cell size, foil thickness, and the material of the core, influence the ballistic performance of honeycomb-core sandwich panels (HCSP) in the case of hypervelocity impact (HVI) by orbital debris. Two predictive models that account for this influence have been developed in this study: a dedicated ballistic limit equation (BLE) and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP. The BLE is a modified version of the Whipple shield BLE and demonstrated excellent accuracy in predicting the ballistic limits of HCSP, when tested against a new set of simulation data, with the discrepancy ranging from 1.13% to 5.58% only. The ANN was developed using MATLAB’s Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as that employed for the BLE fitting and demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%.","PeriodicalId":23773,"journal":{"name":"WIT Transactions on the Built Environment","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"WIT Transactions on the Built Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2495/hpsu220091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cell size, foil thickness, and the material of the core, influence the ballistic performance of honeycomb-core sandwich panels (HCSP) in the case of hypervelocity impact (HVI) by orbital debris. Two predictive models that account for this influence have been developed in this study: a dedicated ballistic limit equation (BLE) and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP. The BLE is a modified version of the Whipple shield BLE and demonstrated excellent accuracy in predicting the ballistic limits of HCSP, when tested against a new set of simulation data, with the discrepancy ranging from 1.13% to 5.58% only. The ANN was developed using MATLAB’s Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as that employed for the BLE fitting and demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%.