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Fang M, Chu X, Yu L, Fang Y, Hou L, Cheng X, Wang J. Influence of periodic pulse intake on the ventilation efficiency of positive pressure explosion-proof robot. Sci Rep 2024; 14:1433. [PMID: 38228843 PMCID: PMC10791746 DOI: 10.1038/s41598-024-52011-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/12/2024] [Indexed: 01/18/2024] Open
Abstract
The ventilation work is an important step to be completed before the start of the positive pressure explosion-proof robot. The existing explosion-proof technology uses constant pressure inflation, which will cause explosive gas to accumulate in the corner area of the cavity for a long time. In order to solve this problem, a ventilation method with periodic pulse intake is proposed. Based on the finite element method, the cleaning and ventilation process of the positive pressure explosion-proof robot is simulated and analyzed. The concentration of explosive gas in the robot cavity with time under constant pressure intake and pulse intake with different periods and amplitudes is compared. The simulation results show that the pulse intake is beneficial to the ventilation of the corner position. The period and amplitude of the pulse intake has an effect on the ventilation efficiency, when the period is the same, the greater the amplitude of the pulse intake, the higher the ventilation efficiency; when the amplitude is the same, the smaller the period of the pulse intake, the higher the ventilation efficiency. After experimental verification, the validity of the simulation results is proved. This study helps to improve the ventilation efficiency of positive-pressure explosion-proof robots and provides guidance for practical applications.
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Affiliation(s)
- Ming Fang
- School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, 241000, China
- R&D Center, Efort Intelligent Equipment Co., Ltd, Wuhu, 241007, China
| | - Xufeng Chu
- School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, China
| | - Liang Yu
- School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, China.
| | - Yu Fang
- R&D Center, Efort Intelligent Equipment Co., Ltd, Wuhu, 241007, China
| | - Liangliang Hou
- School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, 241000, China
| | - Xu Cheng
- School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, China
| | - Junlong Wang
- School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, China
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2
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Numerical investigation of the effect of the opening mode on the pressure relief process of engine nacelle. Sci Rep 2022; 12:20671. [PMID: 36450899 PMCID: PMC9712345 DOI: 10.1038/s41598-022-24419-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 11/15/2022] [Indexed: 12/05/2022] Open
Abstract
The pressure relief door (PRD) is a vital structure to ensure the safety and reliability of the engine. This paper established a zero-dimensional transient simulation mathematical model to study the plenum compartment pressure threshold and maximum opening angle effects on the nacelle pressure relief process under different opening modes. Then, a computational fluid dynamics model verified by experimental literature data was used to simulate the nacelle pressure relief process and to determine the influence of two different opening modes on the force and discharge characteristics of the PRD. The results of this study show that different opening modes strongly impact the nacelle pressure relief process. Reducing the nacelle compartment pressure threshold of the PRD opening can reduce the time required for the pressure relief process to reach the equilibrium stage. Reducing the maximum opening angle may increase the nacelle compartment pressure during the equilibrium stage. In addition, under the same nacelle compartment pressure thresholds and maximum opening angles, the pressure relief process under the vertical opening mode can reach a lower nacelle compartment pressure during the pressure relief equilibrium stage compared to that under the horizontal opening mode. Therefore, the vertical opening mode is better than the horizontal opening mode. This paper provides two lower calculation costs and high accuracy research models for studying the nacelle pressure relief process.
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3
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Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li-Tao Zhu
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xi-Zhong Chen
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S1 3JD, U.K
| | - Bo Ouyang
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Wei-Cheng Yan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - He Lei
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhe Chen
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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4
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Nagulapati VM, S Paramanantham S, Ni A, Raman SK, Lim H. Machine learning based prediction of subcooled bubble condensation behavior, validation with experimental and numerical results. NUCLEAR ENGINEERING AND DESIGN 2022. [DOI: 10.1016/j.nucengdes.2022.111794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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5
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Marcato A, Boccardo G, Marchisio D. From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04760] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Agnese Marcato
- DISAT - Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Gianluca Boccardo
- DISAT - Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Daniele Marchisio
- DISAT - Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
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6
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Prediction of molecular diffusivity of organic molecules based on group contribution with tree optimization and SVM models. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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7
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Parsaei M, Roudbari E, Piri F, El-Shafay AS, Su CH, Nguyen HC, Alashwal M, Ghazali S, Algarni M. Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment. Sci Rep 2022; 12:4125. [PMID: 35260785 PMCID: PMC8904475 DOI: 10.1038/s41598-022-08171-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 03/03/2022] [Indexed: 12/17/2022] Open
Abstract
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.
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Affiliation(s)
- Mozhgan Parsaei
- School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
| | - Elham Roudbari
- Department of Chemistry, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Piri
- Electrical Engineering Department, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran
| | - A S El-Shafay
- Department of Mechanical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia.
| | - Chia-Hung Su
- Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan.
| | - Hoang Chinh Nguyen
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam
| | - May Alashwal
- Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia
| | - Sami Ghazali
- Mechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia
| | - Mohammed Algarni
- Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
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8
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Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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9
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Implementation of AdaBoost and genetic algorithm machine learning models in prediction of adsorption capacity of nanocomposite materials. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118527] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Wei Y, Yu J, Du Y, Li H, Su CH. Artificial intelligence simulation of Pb(II) and Cd(II) adsorption using a novel metal organic framework-based nanocomposite adsorbent. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117681] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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11
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Zhu H, Zhu L, Sun Z, Khan A. Machine learning based simulation of an anti-cancer drug (busulfan) solubility in supercritical carbon dioxide: ANFIS model and experimental validation. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116731] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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12
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Ghanei-Nasab S, Hadizadeh F, Foroumadi A, Marjani A. A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05064-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm. Sci Rep 2021; 11:10623. [PMID: 34012076 PMCID: PMC8134435 DOI: 10.1038/s41598-021-90201-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 05/07/2021] [Indexed: 11/24/2022] Open
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.
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14
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Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow. Sci Rep 2021; 11:1505. [PMID: 33452362 PMCID: PMC7810899 DOI: 10.1038/s41598-021-81111-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/29/2023] Open
Abstract
Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.
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15
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Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system. Sci Rep 2021; 11:1308. [PMID: 33446789 PMCID: PMC7809283 DOI: 10.1038/s41598-020-80207-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/17/2020] [Indexed: 11/22/2022] Open
Abstract
Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.
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Babanezhad M, Behroyan I, Nakhjiri AT, Marjani A, Shirazian S. Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe. Sci Rep 2021; 11:902. [PMID: 33441682 PMCID: PMC7806621 DOI: 10.1038/s41598-020-79628-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/10/2020] [Indexed: 11/08/2022] Open
Abstract
Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
- Faculty of Electrical and Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
- Department of Artificial Intelligence, Shunderman Industrial Strategy Co., Tehran, Iran
| | - Iman Behroyan
- Department of Computational Fluid Dynamics, Shunderman Industrial Strategy Co., Tehran, Iran
- Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080
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17
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Babanezhad M, Behroyan I, Marjani A, Shirazian S. Pressure and temperature predictions of Al 2O 3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS. Sci Rep 2021; 11:60. [PMID: 33420204 PMCID: PMC7794232 DOI: 10.1038/s41598-020-79689-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/11/2020] [Indexed: 11/25/2022] Open
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 Al2O3/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.
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Affiliation(s)
- Meisam Babanezhad
- 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
| | - Iman Behroyan
- Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.,Department of Computational Fluid Dynamics, Shunderman Industrial Strategy Co., Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia
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