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 (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).

Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning.

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone.

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.

Synergy between Diastolic Mitral Valve Function and Left Ventricular Flow Aids in Valve Closure and Blood Transport during Systole.

Highly resolved three-dimensional (3D) fluid structure interaction (FSI) simulation using patient-specific echocardiographic data can be a powerful tool for accurately and thoroughly elucidating the biomechanics of mitral valve (MV) function and left ventricular (LV) fluid dynamics. We developed and validated a strongly coupled FSI algorithm to fully characterize the LV flow field during diastolic MV opening under physiologic conditions. Our model revealed that distinct MV deformation and LV flow patterns developed during different diastolic stages.

SPARSE-A subgrid particle averaged Reynolds stress equivalent model: testing with closure.

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 subparticle cloud scale on the averaged motion and velocity of the cloud. The SPARSE (subgrid particle averaged Reynolds stress equivalent) model is based on a combination of a truncated Taylor expansion of a drag correction function and Reynolds averaging. It reduces the required number of computational parcels to trace a cloud of particles in Eulerian-Lagrangian methods for the simulation of particle-laden flow.