Abstract
This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.
Highlights
Based on the studies on thermal features, it is indicated that a weak thermal conductivity is exhibited by all today’s liquid coolants utilized as heat transfer fluids compared to solid metals
This study aims to include the finite volume method (FVM) results from computational fluid dynamics (CFD) into the learning process of a machine learning system
For CFD data loss, where the CFD data are unavailable for specific boundary conditions, the artificial intelligence (AI) algorithm could predict the lost data based on the pattern of the existing data
Summary
Based on the studies on thermal features, it is indicated that a weak thermal conductivity is exhibited by all today’s liquid coolants utilized as heat transfer fluids compared to solid metals. The findings indicated that the suspended nanoparticles considerably improved the conventional base fluid’s heat transfer behavior, and there was good consistency between their friction factor and the water. They proposed novel convective heat transfer correlations to predict the nanofluid’s heat transfer coefficients for both turbulent and laminar flow circumstances. Almost all investigations focused on the CFD model accuracy for the prediction of heat and fluid flow variables. The CFD is known as a robust method for predicting heat transfer and fluid dynamics parameters, this method requires a lot of computational time and expenses for complex CFD problems (i.e., large geometries, turbulent flows, etc.). The prediction of the velocity profile of Al2O3/ water turbulent flow in a heated pipe is the main objective of this study
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