# NEURAL-NETWORK METAMODELLING FOR THE PREDICTION OF THE PRESSURE DROP OF A FLUID PASSING THROUGH METALLIC POROUS MEDIUM

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### ) using BPNN as a function of temperature inside the porous medium corresponds to a decrease when the temperature increases (FIG. 7). The density decreases due to the thermal rise and since the mass flow rate inside the medium is kept constant, the mean fluid velocity increases. Thus, the outlet pressure decreases; which means that the pressure drop increases (for a given curve corresponding to a fixed inlet pressure). This is clearly understandable when paying attention to the Brinkman equation. It is thus very important to note that the ANN approach is able to reproduce physical variations.

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2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0

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in = 0 .4 M P a P

in = 0 .6 M P a P

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T ( K ) Model 1 predicted P out (MPa)

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### =0.6 MPa and T=573.15 K). This means that the pressure is divided by a factor 2 at high temperature while for about half of this temperature, the pressure losses only 33%. To divide the inlet pressure of 0.6 MPa by a factor 2, the temperature should be about 873.15 K. As a consequence, it can be concluded physically, that the temperature has a higher effect than the pressure itself. The thermal effect much increases the fluid velocity within the porous media (more than what the pressure does). This is the thermodynamic consequence of pressure and temperature parameters on the fluid density. This could be a way to get information of phenomena within the porous media where no direct measure seems to be possible for the fluid properties.

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T = 2 9 1 . 1 5 K T = 5 7 3 . 1 5 K T = 8 7 3 . 1 5 K T = 1 1 7 3 . 1 5 K

Model 1 predicted P in - P out (MPa)

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