A MULTIVARIABLE COMPUTATIONAL FLUID DYNAMICS ANALISYS METHOD BASED IN BAYESIAN NETWORKS APPLIED ....

In recent years, computational fluid dynamics has been utilised to model and derive numerical approximations of the physical conditions within bioreactors. Despite the fact that this technique has improved the accuracy and realism of the models, it is still important to verify their accuracy in relation to real-world data acquired from sensors. However, while discretizing a domain control to calculate the energy balance, one of the issues that arise is the inability to handle multiple variables and parameters at the same time. Other tools are then required to analyse these models. Bayesian networks have been used to analyse multivariable models in recent years. To show the relationships between methane and CO2 concentrations, we developed a Computational Fluid Dynamics model that was examined using Bayesian Networks. Calculating conclusions in dependent probability distributions has allowed these relationships to be quantified.

Please see the link :- https://www.ikprress.org/index.php/JACSI/article/view/3901