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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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-10-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724001144 |
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| author | Philipp Moldtmann Julian Berk Shannon Ryan Andreas Klavzar Jerome Limido Christopher Lange Santu Rana Svetha Venkatesh |
| author_facet | Philipp Moldtmann Julian Berk Shannon Ryan Andreas Klavzar Jerome Limido Christopher Lange Santu Rana Svetha Venkatesh |
| author_sort | Philipp Moldtmann |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df9f6d26414445af87bea98567de65ae |
| institution | OA Journals |
| issn | 2214-9147 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj-art-df9f6d26414445af87bea98567de65ae2025-08-20T02:11:38ZengKeAi Communications Co., Ltd.Defence Technology2214-91472024-10-014011210.1016/j.dt.2024.05.007Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threatsPhilipp Moldtmann0Julian Berk1Shannon Ryan2Andreas Klavzar3Jerome Limido4Christopher Lange5Santu Rana6Svetha Venkatesh7University of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg 22043, GermanyApplied Artificial Intelligence Institute (A2I2), Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC 3216, AustraliaApplied Artificial Intelligence Institute (A2I2), Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC 3216, Australia; Corresponding author.French-German Research Institute of Saint-Louis (ISL), 5 Rue de General Cassagnou, Saint-Louis 68300, FranceABSTRAO, 16 Rue de Isaac Newton, Plaisance-du-Touch 31830, FranceUniversity of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg 22043, Germany; French-German Research Institute of Saint-Louis (ISL), 5 Rue de General Cassagnou, Saint-Louis 68300, FranceApplied Artificial Intelligence Institute (A2I2), Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC 3216, AustraliaApplied Artificial Intelligence Institute (A2I2), Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC 3216, AustraliaWe 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.http://www.sciencedirect.com/science/article/pii/S2214914724001144Terminal ballisticsArmourExplosive reactive armourOptimisationBayesian optimisation |
| spellingShingle | Philipp Moldtmann Julian Berk Shannon Ryan Andreas Klavzar Jerome Limido Christopher Lange Santu Rana Svetha Venkatesh Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats Defence Technology Terminal ballistics Armour Explosive reactive armour Optimisation Bayesian optimisation |
| title | Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| title_full | Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| title_fullStr | Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| title_full_unstemmed | Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| title_short | Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| title_sort | adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats |
| topic | Terminal ballistics Armour Explosive reactive armour Optimisation Bayesian optimisation |
| url | http://www.sciencedirect.com/science/article/pii/S2214914724001144 |
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