  This paper proposes a phenomenological Reynolds Averaged Navier-Stokes (RANS)
calculation model based on physical constraints. In this model part of the
source terms in the e equation was replaced with the deep learning model, using
the standard k-e model as a template. The simulation results of this model
achieved a high error reduction of 51.7 % compared to the standard k-e model.
To improve the adaptability and accuracy compared to the convergence of the
abnormal flow regime, the coordinate technology proposed in this study was used
in the modelling process. For the training data, the k-field and e-field were
automatically corrected using this approach when the flow state deviated from
the theoretical assumption. Based on the coordinate technology, a deep learning
model for the source term of the equation was built, and the simulation error
was reduced by 6.2 % compared to the uncoordinated one. From the results, the
proposed coordinate technology can effectively be adapted to the underdeveloped
flow state and assist in the more accurately modelling of the phenomenological
RANS calculation model under a complex flow state.
