Effect of spatial distribution of mesoscale heterogeneities on the shock-to-detonation transition in liquid nitromethane

Mi, Xiaocheng; Michael, Louisa; Nikiforakis, Nikolaos; Higgins, Andrew

doi:10.1016/j.combustflame.2020.08.053

Cited by 9 publications

(3 citation statements)

References 54 publications

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“…( 27 ) developed a more sophisticated surrogate model spanning a larger parameter space, including not only the void sizes but also the aspect ratios of voids, their orientations, and volume fractions. Other approaches to quantifying hotspot dynamics have also relied on idealized synthetic microstructures with voids represented by circles ( 4 ), ellipses ( 27 ), and rectangles ( 18 ). Surrogate models derived in this manner ( 29 , 30 ) were used to close the macroscale system of equations modeling the shock-to-detonation transition to determine the critical energy for initiation ( 31 ) and the run-to-detonation distances ( 21 ).…”

Section: Introductionmentioning

confidence: 99%

“…EMs are composites of organic crystals, plasticizers, metals, and other inclusions, forming complex microstructural morphologies, which strongly influence the properties and performance characteristics of these materials (1). For instance, the sensitivity to affect and shock loading-one of the key performance parameters for the design of safe and reliable EMs-is strongly influenced by their microstructures (2)(3)(4). Voids, cracks, and interfaces in EM microstructures are potential sites for energy localization, i.e., the formation of high-temperature regions called "hotspots" (5)(6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

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PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

Nguyen

Choi

et al. 2023

Sci. Adv.

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The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a “materials-by-design” framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physics-awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

Nguyen

Choi

et al. 2023

Sci. Adv.

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“…This idea/observation of the secondary effect is not novel in the context of mesoscale simulations. 46,47 However, it has not been widely considered so we include a discussion below about how these observations connect to the broader field of shock initiation in heterogeneous materials.…”

Section: Downstream Effects Of the Expanded Temmentioning

confidence: 99%

Novel method to control explosive shock sensitivity: A mesoscale study to understand the effect of thermally expandable microsphere (TEM) inclusions in high explosives (HE) microstructure

Kumar

Perry

Duque

2022

Journal of Applied Physics

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When the void content and/or void structure of a high explosive (HE) is altered by some means (i.e., bulk heating or mechanical damage), the shock initiation behavior of the material changes. The ability to precisely predict the change in shock sensitivity after an HE has undergone microstructural changes is a crucial capability in multi-scale reactive flow models. Here, we utilize thermally expandable microspheres (TEMs) as a dopant in a polymer bonded explosive (PBX) matrix to alter the shock initiation properties in a controlled fashion. Using a mesoscale modeling approach, we evaluated how a single TEM (before and after thermal expansion) behaves under shock compression, as well as how the matrix PBX in the direct vicinity of the TEM is affected. We first examined the effect of an unexpanded TEM in the explosive matrix and found that its presence does not significantly perturb the bulk flow and by extension will not affect bulk sensitivity. Next, we examined the effect of an expanded TEM and found that its presence significantly perturbs the flow via hydrodynamic jetting, which causes a secondary shock wave with a strength that exceeds that of the incident wave. Finally, we showed that this secondary shock interacts with the downstream porosity to ignite a larger fraction of the overall pore volume, commensurate with the secondary shock strength and the affected volume, increasing the global (bulk) shock sensitivity.

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Adaptive moving window technique for SPH simulation of stationary shock waves

Murzov,

Dyachkov,

Zhakhovsky

2024

Computer Physics Communications

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Effect of spatial distribution of mesoscale heterogeneities on the shock-to-detonation transition in liquid nitromethane

Cited by 9 publications

References 54 publications

PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

Novel method to control explosive shock sensitivity: A mesoscale study to understand the effect of thermally expandable microsphere (TEM) inclusions in high explosives (HE) microstructure

Adaptive moving window technique for SPH simulation of stationary shock waves

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