activity
20162022
most citedModeling meso-scale energy localization in shocked HMX, Part I: machine- learned surrogate model for effect of loading and void size

45 citations · 157 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2022★ 42 cited

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen +2

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 m…

physics.comp-ph2019★ 17 cited

Structure-Property Linkage in Shocked Multi-Material Flows Using A Level-Set Based Eulerian Image-To-Computation Framework

S Roy, N Rai, O Sen +1

Morphology and dynamics at the meso-scale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upsca…

physics.comp-ph2019★ 37 cited

Modeling meso-scale energy localization in shocked HMX, Part II: training machine-learned surrogate models for void shape and void-void interaction effects

S. Roy, N. K. Rai, O. Sen +3

Surrogate models for hotspot ignition and growth rates were presented in Part I, where the hotspots were formed by the collapse of single cylindrical voids. Such isolated cylindric…

physics.app-ph2019

Scaling Law for Criticality Conditions in Heterogeneous Energetic Materials under Shock Loading

A. Nassar, N. K. Rai, O. Sen +1

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 an…

physics.comp-ph2018★ 45 cited

Modeling meso-scale energy localization in shocked HMX, Part I: machine- learned surrogate model for effect of loading and void size

Anas Nassar, Nirmal K. Rai, Oishik Sen +1

This work presents the procedure for constructing a machine learned surrogate model for hotspot ignition and growth rates in pressed HMX materials. A Bayesian Kriging algorithm is…

physics.flu-dyn2016★ 16 cited

SPARSE: A Subgrid Particle Averaged Reynolds Stress Equivalent Model: Testing with A Priori Closure

Sean Davis, Gustaaf Jacobs, Oishik Sen +1

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the sub-…