Gas is often emanated from the sea bed during a subsea oil and gas blowout. The size of a gas bubble changes due to gas dissolution in the ambient water and expansion as a result of a decrease in water pressure during the rise. It is important to understand the fate and transport of gas bubbles for the purpose of environmental and safety concerns. In this paper, we used the numerical model, VDROP‐J to simulate gas formation in jet/plume upon release, and dissolution and expansion while bubble rising during a relatively shallow subsea gas blowout. The model predictions were an excellent match to the experimental data. Then a gas dissolution and expansion module was included in the VDROP‐J model to predict the fate and transport of methane bubbles rising due to a blowout through a 0.10 m vertical orifice. The numerical results indicated that gas bubbles would increase the mixing energy in released jets, especially at small distances and large distances from the orifice. This means that models that predict the bubble size distribution (BSD) should account for this additional mixing energy. It was also found that only bubbles of certain sizes would reach the water surfaces; small bubbles dissolve fast in the water column, while the size of the large bubbles decreases. This resulted in a BSD that was bimodal near the orifice, and then became unimodal.
A novel approach to modeling prediction of phase equilibrium is presented. The method,
evolutionary polymorphic neural network (EPNN), is developed by the authors on the basis of
artificial neural networks and evolutionary computing. The system poly(ethylene glycol) (PEG)/potassium phosphate/water at pH = 7 was selected to demonstrate the performance of the model.
The results were favorable as compared to a traditional neural network modeling approach and
the experimental data set. Seven distinct data sets of varying PEG molecular weights were
used in this work. Of the seven, five were used for training, while the remaining two were
employed as the test cases. Following the training, a networked symbolic equation system evolved,
which, in addition to reproducing the data, can also be used to improve understanding of the
phase diagram mechanism through the discovered parameters.
The closed form solution to the conjugated boundary value problem posed by a counter current hemodialyzer facilitates the estimation of the overall mass transfer coefficient. Comparison of the proposed model results with published experimental data shows good agreement for Urea and Creatinine clearances over a published range of blood and dialyzate flow rates. This model predicts clearances with a maximum error of less than 4% for both Urea and Creatinine when blood flow is 75% of the dialyzate flow. However, when both blood and dialyzate flows are identical the model over predicts the experimental data by 1.47% in the case of Urea and 4.75 for Creatinine flows of 300 ml/min. Although the concentration profile is an infinite series involving confluent hypergeometric functions, 2 terms of the series were sufficient (Mathematica notebook program) to produce these results. Overall mass transfer coefficients can now be deduced from the Sherwood numbers and provide possible improvement over currently used area coefficients.
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