Multi-scale modeling of shock initiation of a pressed energetic material I: The effect of void shapes on energy localization

Choi

et al. 2023

Sci. Adv.

Self Cite

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.

Section: Resultssupporting

confidence: 89%

“…( 27 ), Rai and Udaykumar ( 18 ), Nguyen et al. ( 34 ), and others, which demonstrated the strong influence of void aspect ratios and void orientations on the formation of hotspots.…”

Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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

Choi

et al. 2023

Sci. Adv.

Self Cite

“…The representation of microstructural data is of critical importance as it defines the search space (or design space) of the materials of interest along with the design parameters to be optimized. EMs, in particular, have complex microstructural morphologies (porosity, shape distribution of voids and crystals) [1] which have a direct impact on the material properties (strength, reactivity) and performance (initiation sensitivity, energy release rate) [1, 45–46, 49, 54]. For example, it has long been known that the number density and size of the voids in an EM affect the sensitivity of the explosives [1, 48–49, 51].…”

Section: Data Representation For Microstructure Designmentioning

confidence: 99%

Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

Choi

Propellants Explo Pyrotec

Sen

et al. 2023

Self Cite

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/ targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure-property-performance (SÀ PÀ P) linkage estimation, and optimization/design exploration. We leave out "process" as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materialsby-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research.

“…The rate of reaction progress variable _ l governs the rate of energy release during the SDT and is used in the macroscale governing equation as a source term, either directly [8,[11][12][13] or indirectly [14,15]. Common approaches to derive the reaction progress rate rely on the DNS of resolved material microstructures at the mesoscale [16][17][18][19][20][21]. However, such a computation is intensive and thus discourages the execution of concurrent multiscale modeling in which the mesoscale reaction progress rate is derived simultaneously or "on-line" with the macroscale simulation.…”

Section: Introductionmentioning

confidence: 99%

A Physics‐Aware Deep Learning Model for Energy Localization in Multiscale Shock‐To‐Detonation Simulations of Heterogeneous Energetic Materials

Propellants Explo Pyrotec

Seshadri

et al. 2023

Self Cite

Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock-initiated EM microstructures. The proposed multiscale modeling framework is divided into two stages. First, a physics-aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock-initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths.After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub-grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high-performance and safer energetic materials.