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For further understanding and verification of numerical models the velocity field is measured by "micro Particle Image Velocimetry" (μ-PIV).
Parameters considered in the experiments include the rate of water injected into the models, the velocity and the temperature of the humidified gas in the cathode channels, the types of flow field, and the temperature of the models.
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In this model, the velocity is contributed by two parts.
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.
In this model, the velocity is assumed to be almost constant except for a possible acceleration (maneuvering) with distribution ( mathcal {N}(0;sigma _{p}^{2})).
In this model, the velocity of mobile node is assumed to be correlated over time and modeled as a Gauss-Markov stochastic process [34].
This study investigates the capacity of artificial neural network (ANN) models for representing and modelling the velocity distributions of combined open channel flows.
The velocity, v, of this wave front is given by
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