On the improvement of the extrapolation capability of an iterative machine-learning based RANS Framework

A bounded normalization method is proposed to improve the extrapolation capability of the recently developed iterative ML based turbulence modeling framework (Liu et al., 2021), whose performance degrades when the Reynolds number is beyond the training range. The bounded normalization method constrains the input feature into a finite range, and the learning target is also re-normalized with the eddy viscosity from a traditional turbulence model. Consequently, a certain level of physical similarity can be maintained when the Reynolds number goes beyond the training range. Tests in channel flows demonstrates an improved performance against the conventional normalization method. The improved model is further tested in a spatially developing boundary layer with a varying local Reynolds number, and the result shows that the model can also predict the skin-friction and velocity profiles with a favorable accuracy, even if the model is trained under a different flow configuration.