Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Numerical Study of Enhanced Oil Recovery Using In Situ Oxy-Combustion in a Porous Combustion Tube

In situ combustion (ISC) in a one-dimensional combustion porous tube has been modeled numerically and presented in this article. The numerical model has been developed using the cmg stars (2017.10) software and it was used to model especial cases for validation against published experimental data. A comprehensive chemical reaction scheme has been developed and used to simulate the ISC process in the lab scale. Moreover, co-injection of oxygen with carbon dioxide (O2/CO2); and co-injection of enriched air (O2/N2) have been further investigated. In the case of using (O2/N2) as an oxidizer, increasing the oxygen ratio from 21% to 50% leads to increasing the oil recovery factor from 31.66% to 66.8%, respectively. In the case of using (O2/CO2) as an oxidizer, increasing the oxygen ratio from 21% to 50% leads to increasing the oil recovery factor from 35.77% to 70.3%, respectively. It was found that the co-injection of (O2/CO2) gives higher values of the oil recovery factor compared with that given when oxygen-enriched air (O2/N2) is injected for ISC. The change in the produced cumulative hydrogen and hydrogen sulfide is considered small whether using (O2/CO2) or (O2/N2) as an oxidizer.

On the Heat Flow Through a Porous Tube Filled with Incompressible Viscous Fluid

We studied the non-isothermal flow of an incompressible viscous fluid through a porous tube. Motivated by filtration problems, Darcy’s law was incorporated on the walls of the tube and the flow was pressure driven. The main goal was to investigate the thermodynamic part of the system, assuming that the hydrodynamic part is known. In view of the applications we wanted to model, the fluid inside the tube was supposed to be cooled (or heated) by the surrounding medium. Using asymptotic analysis with respect to the small parameter (being the ratio between the tube’s thickness and its length), we constructed the explicit second-order approximation for the temperature distribution of the fluid. Numerical examples are provided to compare the obtained solution with the one derived for a rigid tube and also to show the corrections due to higher-order terms.