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Abstract
In this paper, a novel zonal machine learning (ML) approach for Reynoldsaveraged NavierStokes (RANS) turbulence modelling based on the divideandconquer technique is introduced. This approach involves partitioning the flow domain into regions of flow physics called zones, training one ML model in each zone, then validating and testing them on their respective zones. The approach was demonstrated with the tensor basis neural network (TBNN) and another neural net called the turbulent kinetic energy neural network (TKENN). These were used to predict Reynolds stress anisotropy and turbulent kinetic energy respectively in test cases of flow over a solid block, which contain regions of different flow physics including separated flows. The results show that the combined predictions given by the zonal TBNNs and TKENNs were significantly more accurate than their corresponding standard nonzonal models. Most notably, shear anisotropy component in the test cases was predicted at least 20% and 55% more accurately on average by the zonal TBNNs compared to the nonzonal TBNN and RANS, respectively. The Reynolds stress constructed with the zonal predictions was also found to be at least 23% more accurate than those obtained with the nonzonal approach and 30% more accurate than the Reynolds stress predicted by RANS on average. These improvements were attributed to the shape of the zones enabling the zonal models to become highly locally optimized at predicting the output.
Original language  English 

Journal  Physics of Fluids 
Early online date  2 May 2023 
DOIs  
Publication status  Epub ahead of print  2 May 2023 
Keywords
 Reynoldsaveraged NavierStokes
 Turbulence modelling
 Machine Learning
 Tensor basis Neural Network
 Divideandconquer
 Reynolds stress
 Separated flows
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Fundamental Understanding of Turbulent Flow over FluidSaturated Complex Porous Media
Mahmoudi Larimi, Y. & Revell, A.
1/04/23 → 30/09/26
Project: Research