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This study investigates the capacity of artificial neural network (ANN) models for representing and modelling the velocity distributions of combined open channel flows.
This is the case in the level set methods used for computing and analyzing the motion of an interface in two or three dimensions by modelling the velocity vector field through Euler-Lagrange or Hamilton-Jacobi PDE's [77], [78], [79], [80].
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We modeled the velocity characteristics of the flow around the sensor and through the backside pore using the finite element method (FEM).
The Green's functions were modeled using the velocity model estimated by Kalafat et al. (1987).
Such formulation is very useful since it allows modeling the characteristic velocity profile in the outlet.
In this model, the velocity is contributed by two parts.
For further understanding and verification of numerical models the velocity field is measured by "micro Particle Image Velocimetry" (μ-PIV).
For the coverage prediction without building model, the velocity MAE using the adaption algorithm was reduced on each trajectory by an average of 60.40 %.
In addition, the constant parameters of the model, the velocity and the activation energy constants, associated to the kinetic model, were calculated.
In the three dimensional model, the velocity distribution of the gas flow is obtained by solving the momentum equation.
First, in the Wake II model the velocity is modified to include the pipe's encounter with the wake flow when the velocity reverses.
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