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Therefore, it is concluded that appropriately constructed ANN and regression models can be developed and used for the rigid boundary channel design.
The results show that one of the most probable diffusion mechanisms (with an activation energy of 0.77 eV) is Cu-interstitial migration along a grain boundary channel.
The left-hand boundary (channel inlet) is kept at a constant temperature (Dirichlet boundary condition) and set to a dimensionless value of zero.
A second-order extrapolation similar to the one given in[17] is used to obtain the values of the unknown distribution functions for the right-hand side boundary (channel outlet) as follows: g i N x, t = 2 g i N x − 1, t − g i N x − 2, t for i = 3, 6, and 7. (12).
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For comparison of the models, a powerful tool, the artificial neural network (ANN) is used for modelling incipient deposition of sediment in rigid boundary channels.
Based on external morphological characteristics and boundaries, channel systems are comprised of a weakly confining type and a non-confining type.
Although most rivers have alluvial boundaries, channels eroded into bedrock form a distinctive subset.
The investigation of resistant-boundary channels is an exciting and under-explored area of fluvial geomorphology that challenges to rethink traditional assumptions based on alluvial channels.
Thus, the boundary between channel sets given by cases 2 and 3 is given by S ∗ = log ( z max Λ ∗ ) − ( 1 − Λ ∗ z max ) = − log + Ω − 1 = t (30).
The index affects: (a) the lateral growth rate of two neighboring grains toward each other to bond together to resist cracking, and (b) the length of the grain-boundary liquid channel through which feeding has to occur to resist cracking.
An innovative approach to take into account the boundary effects of channel banks, water surface, and channel bed was also proposed.
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CEO of Professional Science Editing for Scientists @ prosciediting.com