The accurate prediction of turbulence skin friction is one of the essential problems in engineering design and academic field, and the data-based design of turbulence model has shown great potential and prospects in this field. In this paper, we propose a data-driven method in turbulence model correction with constrain of physics knowledge. The prior physics knowledge is gained through skin friction decomposition in turbulence flow and a spatially-varying factor is introduced to the production term of S-A model as the design variable. The objective function contains the prior physics knowledge. The discrete adjoint method is used to solve the derivatives of the objective function with respect to the design variable, and the distribution of the factor is effectively obtained through iterations. The turbulence channel flow is taken as the case to verify the effectiveness of this data-based turbulence model correction and analyze the influence of the physics knowledge constrains on prediction accuracy. The results show that the introduction of physics knowledge constrains can further improve the accuracy of turbulence skin friction prediction.
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