Modelling the damage and deformation process in a plastic bonded explosive microstructure under tension using the finite element method

Modelling the deformation and failure processes occurring in polymer bonded explosives (PBX) and other energetic materials is of great importance for processing methods and lifetime storage purposes. Crystal debonding is undesirable since this can lead to contamination and a reduction in mechanical properties. An insensitive high explosive (PBX-1) was the focus of the study. This binary particulate composite consists of (TATB) filler particles encapsulated in a polymeric binder (KELF800). The particle/matrix interface was characterised with a bi-linear cohesive law, the filler was treated as elastic and the matrix as visco-hyperelastic. Material parameters were determined experimentally for the binder and the cohesive parameters were obtained previously from Williamson et al. (2014) and Gee et al. (2007) for the interface. Once calibrated, the material laws were implemented in a finite element model to allow the macroscopic response of the composite to be simulated. A finite element mesh was generated using a SEM image to identify the filler particles which are represented as a set of 2D polygons. Simulated microstructures were also generated with the same size distribution and volume fraction only with the idealised assumption that the particles are a set of circles in 2D and spheres in 3D. The various model results were compared and a number of other variables were examined for their influence on the global deformation behaviour such as strain rate, cohesive parameters and contrast between filler and matrix modulus. The overwhelming outcome is that the geometry of the particles plays a crucial role in determining the onset of failure and the severity of fracture in relation to whether it is a purely local or global failure. The model was validated against a set of uniaxial tensile tests on PBX-1 and it was found that it predicted the initial modulus and failure stress and strain well

Invariant causal prediction for block MDPs

Generalization across environments is critical to the successful application of reinforcement learning (RL) algorithms to real-world challenges. In this work we propose a method for learning state abstractions which generalize to novel observation distributions in the multi-environment RL setting. We prove that for certain classes of environments, this approach outputs, with high probability, a state abstraction corresponding to the causal feature set with respect to the return. We give empirical evidence that analogous methods for the nonlinear setting can also attain improved generalization over single- and multi-task baselines. Lastly, we provide bounds on model generalization error in the multi-environment setting, in the process showing a connection between causal variable identification and the state abstraction framework for MDPs