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This paper proposes a phenomenological Reynolds Averaged Navier-Stokes (RANS)calculation model based on physical constraints. In this model part of thesource terms in the e equation was replaced with the deep learning model, usingthe standard k-e model as a template. The simulation results of this modelachieved 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 theabnormal flow regime, the coordinate technology proposed in this study was usedin the modelling process. For the training data, the k-field and e-field wereautomatically corrected using this approach when the flow state deviated fromthe theoretical assumption. Based on the coordinate technology, a deep learningmodel for the source term of the equation was built, and the simulation errorwas reduced by 6.2 % compared to the uncoordinated one. From the results, theproposed coordinate technology can effectively be adapted to the underdevelopedflow state and assist in the more accurately modelling of the phenomenologicalRANS calculation model under a complex flow state.

Construction method for general phenomenological RANS turbulence model