The present study aims at assessing a flexible and light modeling methodology specifically oriented to the short-term prediction of the dynamics of the void fraction of experimental two-phase flows. The proposed strategy consists in the assessment and optimisation of a generalised NARMAX model for the input-output identification of the dynamics of the experimental time series of the void fraction, detected through a high resolution resistive probe during an extensive experimental campaign. Such a model has been implemented by means of Multilayer Perceptron artificial neural networks, trained using input-output data detected during experiments expressing different flow patterns. Reported results show that a satisfactory agreement is reached between simulated and experimental data, showing that the model is able to predict two-phase flow dynamics.

Assessment of an identification strategy for the prediction of the dynamics of two-phase flows

FICHERA, Alberto;PAGANO, ARTURO
2015-01-01

Abstract

The present study aims at assessing a flexible and light modeling methodology specifically oriented to the short-term prediction of the dynamics of the void fraction of experimental two-phase flows. The proposed strategy consists in the assessment and optimisation of a generalised NARMAX model for the input-output identification of the dynamics of the experimental time series of the void fraction, detected through a high resolution resistive probe during an extensive experimental campaign. Such a model has been implemented by means of Multilayer Perceptron artificial neural networks, trained using input-output data detected during experiments expressing different flow patterns. Reported results show that a satisfactory agreement is reached between simulated and experimental data, showing that the model is able to predict two-phase flow dynamics.
2015
Two-phase flow; NARMAX identification strategy; predictive neural model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/98895
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