Abstract:
Throughout history, humans have strived to enhance transportation and energy efficiency
while mitigating environmental damage. The discovery of vortex flow in combustion
technology has been pivotal, leading to ongoing research into its properties, especially in
terms of the shape of the rotational areas it forms. This study delves into the use of artificial
intelligence to predict vortex flow properties. Using experimental data, including descriptive
and positional information, as inputs, and horizontal, vertical and kinetic energy as outputs
across different locations within the combustion chamber, the model effectively captures the
spatial features of the swirl flow field. It accurately predicts the velocity density distribution
and vortex center position, which is in good agreement with experimental results.
Furthermore, the generated prediction model shows promising accuracy over previous data
sets, successfully reconstructing the vortex flow field and making inductive predictions on
new data with a certain degree of generalizability. Ultimately, this study underscores the
potential for many engineering applications to benefit from the prediction model developed
here.
