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One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model

The importance of heavy oils in the world market for petroleum has increased very quickly in the last years. The reserves of heavy oils in the world are estimated at 3 trillion barrels, while reserves of light oils have reduced progressively in the last decade. The high oil viscosity creates major problems in the production and transportation of the oil. This situation leads to the high pressure and power required for its flow, overloading and damaging the equipment, increasing the cost of production. Due to the need to develop new alternatives that will make the production and transport of heavy oil economically viable, this work has the objective to study, numerically, the behavior of isothermal multiphase flow (heavy oil and water), type "core flow", in pipelines, using the software CFX ® 3D. The pressure drop was determinated to a core-flow in a pipe with 7 in. diameter, 2.7 Pa.s oil viscosity and water at environment temperature. Results of the pressure, velocity and volume fraction distributions of the phases are presented and analyzed. It was verified that the pressure drop was reduced 58 times when compared to that obtained with oil flow alone in the pipe.

One of the main problems associated with the transport and manipulation of multiphase

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