Prediction of hypervelocity orbital debris impact damage to the space station by neural networks

Prediction of damage to orbiting space craft due to collisions with hypervelocity space debris is an important issue in the design of Space Station Freedom. Space station wall structures are designed to absorb impact energy during a collision. A proposed wall structure consists of a multilayer insulation (MLI) directly covering the pressure wall, and a bumper layer placed 100mm from the pressure wall. In experiments at The Marshall Space Flight Center, 2.5–12.7 mm projectiles have been fired at this wall structure at speeds of 2–8 km/s. In this paper, three-layer backpropagation networks are trained with two sets of impact damage data. The input parameters for training are pressure wall thickness, bumper plate thickness, projectile diameter, impact angle, and the projectile velocity. Output from the network consists of hole dimensions for the bumper and the pressure wall in the minor and major axis directions, and damage to the MLI. To evaluate network generalization, networks are tested with experimental data points that are not used for training. Network performance is compared with that of other damage prediction methods. Network determination of qualitative damage estimation is suggested as a new direction for research. Preliminary testing of qualitative prediction of pressure wall damage is presented. The results are promising, and suggest several areas for further study.