Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats
We evaluate an adaptive optimisation methodology, Bayesian optimisation (BO), for designing a minimum weight explosive reactive armour (ERA) for protection against a surrogate medium calibre kinetic energy (KE) long rod projectile and surrogate shaped charge (SC) warhead. We perform the optimisation...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-10-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724001144 |
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| Summary: | We evaluate an adaptive optimisation methodology, Bayesian optimisation (BO), for designing a minimum weight explosive reactive armour (ERA) for protection against 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 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. |
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| ISSN: | 2214-9147 |