scholarly journals Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Artificial Intelligence ◽  
Differential Evolution ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Research Area ◽  
Population Number ◽  
Inference System ◽  
Porous Pipe ◽  
Velocity Prediction

AbstractUtilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

scholarly journals 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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Pso Algorithm ◽  
Cfd Modeling ◽  
Swarm Optimization ◽  
Inference System ◽  
Mesh Density

AbstractHerein, 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.

Sustainability Assessment Methodology for Concrete Manufacturing Process: A Fuzzy Inference System Approach

2013 ◽  
Vol 845 ◽  
pp. 814-818 ◽  
Author(s):  
Pouyan Rezvan ◽  
Amir Hossein Azadnia ◽  
Mohd Yusof Noordin ◽  
Seyed Navid Seyedi
Keyword(s):  
Portland Cement ◽  
Fuzzy Inference System ◽  
Manufacturing Process ◽  
Sustainability Assessment ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Three Dimensions ◽  
Manufacturing Processes ◽  
Slag Cement ◽  
Inference System

Sustainability assessment of concrete manufacturing processes has recently received great attention among scholars and practitioners. While most of the studies on sustainability assessment of concrete manufacturing processes focus on economic and environmental issues, those which consider all three dimensions of sustainability (social, economic, and environmental) simultaneously are rather limited. In this study, a hybrid approach of fuzzy inference system and analytical hierarchy process (AHP) is proposed in order to evaluate the sustainability level of concrete manufacturing processes based on Life Cycle Assessment (LCA) principals. AHP is applied to weight the selected sustainability elements and sub elements. Afterward, fuzzy inference system is used to evaluate the sustainability level of concrete manufacturing processes. The practicality and applicability of the proposed approach are examined by conducting sustainability assessments of four different concrete manufacturing processes: (1) 100% of Portland cement (2) 35 % slag cement and 65% Portland cement (3) 50% slag cement and 50% Portland cement (4) 20% fly ash and 80% Portland cement. The results disclose the more sustainable concrete manufacturing process which is 50 % of Slag cement and 50% Portland cement.

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