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Examples show that there is no significant increase in computational effort respect to a pure hyperelastic model.
Here a criterion for applying these enhanced LS schemes is proposed to aid in determining when the increase in computational effort is better utilized.
These aspects are quite often neglected in design and assessment both with linear and nonlinear models due to the modelling complexity and inherent increase in computational effort.
The computational effort associated with N-gram analysis is often referred to as the 'Curse of dimensionality' and was first coined by Bellman in 1961 to describe the exponential increase in computational effort associated with adding extra dimensions to a domain space.
In addition to the rapid increase in computational effort with increase in the size of the fracture network, the spread in the distribution of fracture parameters, such as length and transmissivity, dramatically influences the convergence behavior of the system of linear equations.
Hence, when the number of threads is very large, the resulting speed up is smaller than the increase in computational effort.
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The reduction in computational effort, as compared to the classical incremental method, becomes more significant as the sliding distance increases.
A 5 times reduction in computational effort is observed.
However, including these calculations leads to more than an order of magnitude increase in the computational effort required.
Here we describe how the performance of warping can be substantially improved by extending the method from one- to two-dimensional images, with only a moderate increase in the computational effort.
The standard boundary element method (BEM) is then modified to take account of the form of singularity, without an appreciable increase in the computational effort and at the same time keeping a uniform discretization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com