Abstract
Computing gas and liquid interactions based on interfacial force models require a proper turbulence model that accurately resolve the turbulent scales such as turbulence kinetic energy and turbulence dissipation rate with cheap computational resources. The k − ε turbulence model can be a good turbulence predictive tool to simulate velocity components in different phases and approximately picture the turbulence eddy structure. However, even this average turbulence method can be expensive for very large domains of calculation, particularly when the number of phases and spices increases in the multi-size structure Eulerian approach. In this study, with the ability of artificial learning, we accelerate the simulation of gas and liquid interaction in the bubble column reactor. The artificial learning method is based on adaptive neuro-fuzzy inference system (ANFIS) method, which is a combination of neural cells and fuzzy structure for making decision or prediction. The learning method is specifically used in a cartesian coordinate such as Eulerian approach, while for the prediction process, the polar coordinate is applied on a fully meshless domain of calculations. During learning process all information at computing nodes is randomly chosen to remove natural pattern learning behavior of neural network cells. In addition, different r and theta are used to test the ability of the learning stage during prediction. The results indicate that there is great agreement between ANFIS and turbulence modeling of bubbly flow within the Eulerian framework. ANFIS method shows that neural cells can grow in the domain to provide high-resolution results and they are not limited to the movement or deformation of source points such as Eulerian method. In addition, this study shows that mapping between two different geometrical structures is possible with the ANFIS method due to the meshless behavior of this algorithm. The meshless behavior causes the stability of the machine learning method, which is independent of CFD boundary conditions.
Highlights
Computing gas and liquid interactions based on interfacial force models require a proper turbulence model that accurately resolve the turbulent scales such as turbulence kinetic energy and turbulence dissipation rate with cheap computational resources
For training of CFD data, 70% of full data is selected during training and we evaluated the accuracy of data in the training
For evaluation of training method, we compared training data set with predicted values with adaptive neuro-fuzzy inference system (ANFIS) method, while for testing method, we compared all data set against Artificial Intelligence (AI) results (Fig. 7)
Summary
Computing gas and liquid interactions based on interfacial force models require a proper turbulence model that accurately resolve the turbulent scales such as turbulence kinetic energy and turbulence dissipation rate with cheap computational resources. Beside the interfacial force model, turbulence modeling can participate in computing velocity components and eddy sizes, and it can compute different phases and the interaction between eddies due to coalescence and break-up of gas bubbles This filtration mechanism can provide an accurate prediction of turbulence flow behind the multiphase behavior in the bubble column reactor[2,13,15]. Machine learning can be used for prediction of local values, and for store big data set in a short memory and categorize many operational conditions in a fuzzy logic structure As these algorithms are growing, we need to find out the best way to verify training process for local data set. This method is used for the AI mesh refinement process for the higher resolution of gas fraction in the reactor
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