63 research outputs found
Scaling Law for Criticality Conditions in Heterogeneous Energetic Materials under Shock Loading
Initiation in heterogeneous energetic material (HEM) subjected to shock
loading occurs due to the formation of hot spots. The criticality of the hot
spots governs the initiation and sensitivity of HEMs. In porous energetic
materials, collapse of pores under impact leads to the formation of hot spots.
Depending on the size and strength of the hot spots chemical reaction can
initiate. The criticality of the hot spots is dependent on the imposed shock
load, void morphology and the type of energetic material. This work evaluates
the relative importance of material constitutive and reactive properties on the
criticality condition of spots. Using a scaling-based approach, the criticality
criterion for cylindrical voids as a function of shock pressure, Ps and void
diameter, Dvoid is obtained for two different energetic material HMX and TATB.
It is shown that the criticality of different energetic materials is
significantly dependent on their reactive properties
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Artificial intelligence (AI) is rapidly emerging as an enabling tool for
solving various 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 (S-P-P)
linkage estimation, and optimization/design exploration. We provide a
perspective view of these methods in terms of their potential, practicality,
and efficacy towards the realization of materials-by-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
Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
The sensitivity of heterogeneous energetic (HE) materials (propellants,
explosives, and pyrotechnics) is critically dependent on their microstructure.
Initiation of chemical reactions occurs at hot spots due to energy localization
at sites of porosities and other defects. Emerging multi-scale predictive
models of HE response to loads account for the physics at the meso-scale, i.e.
at the scale of statistically representative clusters of particles and other
features in the microstructure. Meso-scale physics is infused in
machine-learned closure models informed by resolved meso-scale simulations.
Since microstructures are stochastic, ensembles of meso-scale simulations are
required to quantify hot spot ignition and growth and to develop models for
microstructure-dependent energy deposition rates. We propose utilizing
generative adversarial networks (GAN) to spawn ensembles of synthetic
heterogeneous energetic material microstructures. The method generates
qualitatively and quantitatively realistic microstructures by learning from
images of HE microstructures. We show that the proposed GAN method also permits
the generation of new morphologies, where the porosity distribution can be
controlled and spatially manipulated. Such control paves the way for the design
of novel microstructures to engineer HE materials for targeted performance in a
materials-by-design framework
A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
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
employ deep learning to model the mesoscale energy localization of
shock-initiated EM microstructures upon which prediction results are used to
supply reaction progress rate information to the macroscale SDT simulation. 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
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