Data-driven modelling of the Reynolds stress tensor using random forests with invariance

Keyphrases

• Random Forest 100%
• Data-driven Modeling 100%
• Invariance 100%
• Tensor-based 100%
• Reynolds Stress Tensor 100%
• Random Forest Algorithm 50%
• Machine Learning Algorithms 50%
• Reynolds Stress Anisotropy Tensor 50%
• Modeling Tools 25%
• Easy-to-implement 25%
• Class-specific 25%
• Turbulence Model 25%
• Turbulence 25%
• Anisotropy Tensor 25%
• Mean Flow 25%
• Continuation Method 25%
• Reynolds-averaged Navier-Stokes 25%
• Flow-limited 25%
• Novel Machine 25%
• Turbulence Closure 25%
• DNS Data 25%
• Backward-facing Step Flow 25%
• Navier-Stokes Solver 25%
• Training Algorithm 25%
• Square Duct Flow 25%
• Turbulence Anisotropy 25%
• Reynolds-averaged Navier-Stokes Equations 25%
• Data-driven Turbulence Modeling 25%
• Galilean Invariance 25%
• Unseen 25%
• Experimental Dataset 25%
• Neural Network Method 25%

Engineering

• Random Forest 100%
• Reynolds Stress 100%
• Reynolds-Averaged Navier-Stokes 50%
• Machine Learning Algorithm 50%
• Good Agreement 25%
• Experimental Data Set 25%
• Square Duct 25%
• Step Flow 25%
• Facing Step 25%
• Neural Network Approach 25%
• Reference Data 25%