Application of FFBP neural network to the prediction of swirling flow

Since ancient times, man has been seeking to improve in the field of transportation and energy consumption, as the combination of optimal energy consumption and not polluting the environment in exchange for obtaining a better return made him constantly searching for optimal methods in the field of anxiety and industry based on combustion. The swirl or vortex flow is one of the most important discoveries of the last century seeking to develop combustion, as research is still focused on it in many aspects, whether in terms of the shape of the rotational areas it forms. This study aims to determine the extent to which artificial intelligence can predict the characteristics of the Swirl flow. The model adopted experimental data of the Swirl flow, both descriptive and positional data as inputs and horizontal, vertical, and kinetic energy as outputs for several positions within the combustion chamber. The results indicate that the model can capture the spatial characteristics of the vortex flow field and the combined Learning of the relationship between the input and output parameters. The results of the velocity density distribution and the position of the vortex center of the vortex flow field agree well with the experimental results. The generated prediction model has good prediction accuracy based on previously observed datasets and can reconstruct the vortex flow field. In addition, it can perform inductive prediction on a previously unseen dataset and has some generalization ability. Finally, this study suggests that many potential architectures have applications based on the induction prediction model created here.