Author(s)

ALEKSANDR CHERNIAEV, RILEY CARRIERE

Abstract

Cell size, foil thickness, and the material of the core, influence the ballistic performance of honeycombcore 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%.

Keywords

orbital debris shielding, honeycomb-core sandwich panels, hypervelocity impact, ballistic limit equation, artificial neural network