| Title: | Pressure and temperature predictions of Al(2)O(3)/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS |
| Author(s): | Babanezhad M; Behroyan I; Marjani A; Shirazian S; |
| Address: | "Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam. Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Viet Nam. Department of Artificial Intelligence, Shunderman Industrial Strategy Co., Tehran, Iran. Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran. Department of Computational Fluid Dynamics, Shunderman Industrial Strategy Co., Tehran, Iran. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. azam.marjani@tdtu.edu.vn. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. azam.marjani@tdtu.edu.vn. Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia" |
| DOI: | 10.1038/s41598-020-79689-x |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| Abstract: | "Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al(2)O(3)/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses" |
| Notes: | "PubMed-not-MEDLINEBabanezhad, Meisam Behroyan, Iman Marjani, Azam Shirazian, Saeed eng Research Support, Non-U.S. Gov't England 2021/01/10 Sci Rep. 2021 Jan 8; 11(1):60. doi: 10.1038/s41598-020-79689-x" |
