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In the present research, dual-nozzle swirl generator was installed at the blocked end of the straight tube to inject flow tangential to the wall of the pipe with various injection inclinations compared to the longitudinal axis. This causes a swirl flow inside the pipe. Altering some decisive variables, including injection angle, side of nozzle cross-section, pipe diameter, and flow rate, caused to attain various cases with unique hydraulic-geometrical characteristics. Classes ‘A…-D50-N…-M…’ and ‘A…-D20-N…-M…’ bear the main characteristics of the cases embodied in their sub-classes. Entropy generation (St), Nu*, NE, Heat Transfer Improvement (HTI) index and ${\eta}_{w-s}$ were used as effective tools for the basement of the system based on the irreversibility concept. The results showed that all of the cases of class ‘A…-D50-N…-M…’ cause lower frictional-contributed and higher thermal-contributed entropy generation compared to the class ‘A…-D20-N…-M…’. The highest values of the HTI index are 1.5 and 1.763 obtained by ‘A30-D20-N5-M0.4’ and ‘A90-D20-N6.25-M0.1’, respectively.

Hydrogen is a clean energy and has many applications in petroleum refining, glass purification, pharmaceuticals, semiconductors, aerospace applications and cooling generators. Therefore, it is very important to store it in various ways. One of the new and cheap methods to store hydrogen is storing in the brine groundwater. In this method, the hydrogen gas is injected into the brine, in which storing capacity has a direct relationship with the pressure, temperature and salt concentration of the saltwater. In the present study, an artificial neural network (ANN) was used to estimate and optimize the hydrogen solubility (HS) in the saltwater with conventional best algorithms such as the feedback propagation, genetic algorithm (GA) and radial basis function. The optimization is implemented based on available experimental data bank based on the variation of the pressure, working temperature and salt concentration. The results and assessments of different optimization ANN algorithm show that the GA has the most usable and accurate estimation and prediction for HS in the saltwater. Also, the amounts of the relevancy coefficient (${R}_c$) that correspond to the sensitivity of HS on the input parameters demonstrate that the salt concentration and pressure have the minimum and maximum ${R}_c$, respectively. That is, the least and most effect on the output values.