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

Abstract

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 investigation of the effect of the opening mode on the pressure relief process of engine nacelle

“…The nacelle pressure relief process is well known to involve complex transient flow behavior. However, the pressure relief door in many of the above studies is stationary, which cannot reflect the relationship between the plenum compartment pressure and the opening angle of the PRD overtime during the actual pressure relief process 21 , 22 . Therefore, a new calculation method is proposed in this paper.…”

Employing computational fluid dynamics technique for analyzing the PACK-1300XY with methanol and isopropanol mixture

“…Among the numerical studies for the fluid flow throughout the packings, computational fluid dynamic (CFD) is an efficient method to reduce the economic expense of experimental measurements 29 – 32 . Moreover, another benefit of this computational method is the capability of estimation for the effect of diverse factors 33 – 35 . To serve this purpose, several researchers developed precise discretization methods and the solution algorithms, i.e., providing an effective computational scheme, to introduce an accurate model for the simulation of the flow in the packings which the combination of in-house scripts and software would be used to this end 36 .…”