arXiv subjects

Pradeep K. Seshadri

Publications and source records attributed to Pradeep K. Seshadri.

3 recordsLinked to original sources

arXiv2026

High-fidelity simulations of shock initiation of an energetic crystal-binder system due to flyer impact

Meso-scale simulations of energy localization at hotspots provide closure models for multiscale frameworks of shock-to-detonation transition (SDT). Validation of such meso-scale calculations is challenging as direct comparison with experiments is constrained both by limitations of data acquisition in the experiments (e.g., of temperature fields) and modeling over-simplifications in the simulations. To address the latter problem and bring modeling closer to experiments, we advance a high-fidelity meso-scale computational framework for interface-resolved reactive calculations of shock initiation in plastic-bonded explosives (PBXs). Accurate resolution of shock and interfacial dynamics is achieved through higher-order (5th-order WENO) schemes, and sharp interface treatments are implemented for physically accurate material-material interactions. Recently obtained atomistics-consistent material models are used for HMX, with the grid resolution taken down to atomistic scale (O(nm)). The crystal geometries are obtained directly from experiments via nano-CT imaging. The impacting flyer plate, energetic crystal and binder are tracked as distinct phases, and flyer-binder impact and separation are simulated, capturing the flyer deformation and the effects of relief waves from the flyer surface. By combining these high-fidelity modeling components, we evaluate how closely simulations can approach experimental data. Overall, this work provides as assessment of which aspects of numerical treatment and material modeling have the greatest impact on meso-scale simulations of flyer-induced initiation of PBXs, and points to where further improvements are necessary.

physics.comp-ph↗

arXiv2023

PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials

The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure 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 EM with a 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.

cond-mat.mtrl-sci↗

arXiv2023

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 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.

cond-mat.mtrl-sci↗