Abstract
Many numerical methods have been used to simulate the fluid flow pattern in different industrial devices. However, they are limited with modeling of complex geometries, numerical stability and expensive computational time for computing, and large hard drive. The evolution of artificial intelligence (AI) methods in learning large datasets with massive inputs and outputs of CFD results enables us to present completely artificial CFD results without existing numerical method problems. As AI methods can not feel barriers in numerical methods, they can be used as an assistance tool beside numerical methods to predict the process in complex geometries and unstable numerical regions within the short computational time. In this study, we use an adaptive neuro-fuzzy inference system (ANFIS) in the prediction of fluid flow pattern recognition in the 3D cavity. This prediction overview can reduce the computational time for visualization of fluid in the 3D domain. The method of ANFIS is used to predict the flow in the cavity and illustrates some artificial cavities for a different time. This method is also compared with the genetic algorithm fuzzy inference system (GAFIS) method for the assessment of numerical accuracy and prediction capability. The result shows that the ANFIS method is very successful in the estimation of flow compared with the GAFIS method. However, the GAFIS can provide faster training and prediction platform compared with the ANFIS method.
Highlights
Artificial intelligence (AI) has been frequently used in the prediction of physical and industrial processes[1,2,3,4]
By comparing R when the number of inputs is three and two, which indicates an increase in R in the testing and training, when the number membership functions (MFs) = 4, percentage of adaptive neuro-fuzzy inference system (ANFIS) intelligence is %99.5, which is a significant achievement in the ANFIS intelligence
For ANFIS learning processes, we considered data as inputs and outputs that were extracted from the computational fluid dynamics (CFD) simulations
Summary
Artificial intelligence (AI) has been frequently used in the prediction of physical and industrial processes[1,2,3,4]. In the multiphase flow application, Pourtousi et al.[16] employed the ANFIS method to learn CFD data from different heights of a bubble column reactor and predicted the new data set of the flow pattern for different heights and sparger (gas distributor) specifications. One of the main advantages of this accurate fluid flow pattern prediction is the CFD method does not require to save each time step and store the data In this case, AI plays a role as an assistance tool to provide non-existing data, which sometimes needs large computational time and hard drive for storing data. We consider the prediction of flow pattern in the domain and represent new features of flow characteristics based on the predictions ability
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