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Open image in new window Fig. 1 Summary of statistical models for flow regime identification.
The present models for flow maldistribution in the tube-side deal only with the maximum possible velocity deviation.
By combining two models for flow induced crystallization developed in previous work, all parameters but one are a priori fixed.
Open image in new window Fig. 17 Workflow for statistical models formulation using pressure data Open image in new window Fig. 18 Statistical models for flow regime identification.
There exist a few time series models for flow simulations and water resources management (Fernandez and Salas 1990; Machiwal and Jha 2006).
Reported studies on the use of SARIMA models for flow prediction mainly suffers from a drawback that, they used a huge historical database for model development.
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After a thorough literature review, some of the basic analytical models for flow-induced oscillations are presented, ending with the latest sloshing model.
The WANN model for flow prediction was more accurate than the ANN.
Literature [18 20] proposed an ARMA model for flow predictions, which applies to periodic data acquisition.
In this paper we present a new model for flow in fractured porous media.
The conceptual 2D systems were simulated using the HYDRUS model for flow and the POLYMIN model for reactive transport.
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