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Exact difference representations of interface (cell face) pressures and work terms and of the elements of the strain rate tensor in a cell are shown.
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In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein DNA binding specificity computationally.
The scheme involves generating samples of curvature estimates from the multitude of height functions that can be generated from VOF representations of interfaces, and applying quality statistics based on interface orientation and smoothness to choose optimal candidates from the samples.
However, for patterns in which sharp interfaces form and propagate, automata simulations can exhibit undesirable properties, including spurious anisotropy and poor representation of interface curvature effects.
The key feature of the scheme is the use of level set method for the representation of interface between domains with different values of coefficients.
The representation of interfaces by means of the algebraic moving-least-squares (AMLS) technique is addressed.
In this case interface reconstruction methods are used to construct approximate representation of interfaces between materials.
The implicit representation of interfaces allows for seamless transitions through topological changes.
These representations are a formalization of traditional systems engineering block diagrams to enable a more precise representation of interfaces, and other aspects of system structure.
However, since MAPPIS uses a physical, all-atom-based representation of interfaces, it may be sensitive to small differences caused by conformational changes in the interface surface.
The representation of the interface as a vortex sheet eliminates numerical diffusion, and by coupling the tracked interface with a stationary grid (using the well-known vortex-in-cell method) the high cost associated with traditional vortex methods is reduced.
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