We evaluate an adaptive optimisation methodology, Bayesian optimisation (BO), for designing a minimum weight explosive reactive armour (ERA) for protection against hacklinkci.com a surrogate medium calibre kinetic energy (KE) long rod projectile and surrogate shaped charge (SC) warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach, utilising a novel human-machine teaming framework for BO is also evaluated.
Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations, outperforming argan oil pure purple both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.