We present a framework for modeling a dry geophysical mass of granular material – a debris or volcanic avalanche or landslide – flowing over an erodible surface. We also describe a computing environment that incorporates topographical data into a parallel, adaptive mesh computational algorithm that solves the model equations.

Due to preferential orientations of fibers, such as collagen or myocytes, the modeling of the mechanics of myocardial tissue leads to anisotropic and highly nonlinear material models. For micro-anatomically realistic geometries the computational effort to handle these sophisticated models is very challenging and demands the usage of strongly scalable parallel algorithms. In this context we ment...

We present a framework for modeling a dry geophysical mass of granular material – a debris or volcanic avalanche or landslide – flowing over an erodible surface. We also describe a computing environment that incorporates topographical data into a parallel, adaptive mesh computational algorithm that solves the model equations.

Approach • Complete reverse kinetics and Computational Fluid Dynamics (CFD) modeling to optimize reactor geometry for forward reactions • Measure forward kinetics in quartz & Inconel reactors to determine reactor wall catalysis • Measure forward kinetics in reactor packed with membrane material to determine catalytic activity • Measure membrane H2 permeability in presence of clean syngas compon...

Within organisational learning literature, mental models are considered a vehicle for both individual and organizational learning. By (and making them explicit), basis formation of shared the level organization is created, which after its can then be adopted by individuals. This provides mechanisms These have been used as an adaptive computational network model. The model illustrated not too co...

Machine learning atomistic potentials (MLPs) trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction its computational cost.