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Accelerating CFD Using Machine-Learned Surrogate Modeling (Lid-Driven Cavity)

Accelerating CFD Using Machine-Learned Surrogate Modeling (Lid-Driven Cavity). Supported by ONR Award N00014-17-1-2178. Key Personnel. Rice University Center for Computing at the Margins Prof. Robert “Corky” Cartwright Prof. Krishna Palem (PI) Mohamed Abdelrahman (Ph.D. Student)

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Accelerating CFD Using Machine-Learned Surrogate Modeling (Lid-Driven Cavity)

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  1. Accelerating CFD Using Machine-Learned Surrogate Modeling (Lid-Driven Cavity) Supported by ONR Award N00014-17-1-2178

  2. Key Personnel • Rice University Center for Computing at the Margins • Prof. Robert “Corky” Cartwright • Prof. Krishna Palem (PI) • Mohamed Abdelrahman (Ph.D. Student) • Particle Flow & Tribology Laboratory • Prof. C. Fred Higgs III • Dr. PrathameshDesai (Lab Manager)

  3. Problem Definition Boundary (West, East, Top, Bottom) Condition (No Slip) • Main physics • Fluid Dynamics • Eulerian formulation • 3D Navier-Stokes equations U Top East West Bottom

  4. Project Aim • Replacing CFD model with a much faster and nearly accurate ML surrogate model through: • Using Principal component analysis (PCA) or Proper Orthogonal Decomposition (POD) to reduce the dimensionality of the CFD. • Using RNN as a surrogate and capture the relationship between inputs and outputs. • For RNN to cover all possible Reynolds numbers (1-1000), train it on a few representative numbers (100,250,500,750,1000) and test it on others (150,450,650). RNN to be re-trained if used to predict behaviors of Reynolds numbers outside the (1-1000) range. • The higher the Reynolds number, the more turbulence of the fluid. Thus, it is more difficult to train an accurate RNN surrogate model (might need multiple RNNs).

  5. Reducing Dimensionality of CFD using Principal Component Analysis (PCA) Dimensionality of CFD with PCA = 250 Dimensionality of raw CFD = 15625

  6. RNN Simulations for Lid-driven Cavity and Different Reynolds Numbers • An RNN (Recurrent Neural Network) was used as a surrogate model to simulate the x-Velocity (abbreviated Vx) component of the fluid. Two Reynolds numbers (abbreviated Re) were used (100,1000). • The RNN was trained on 70% of each dataset, and tested to predict the remaining 30% of the dataset. • The videos on the next slide compare CFD simulation data with RNN surrogate model output for Vx along the timesteps for Re 100 and 1000. • Reynolds number • The predicted x-Velocity (Vx) component of the fluid for M timesteps (M>N) • Reynolds number • The x-Velocity (Vx) component of the fluid along N timesteps

  7. RNN Simulations for Lid-driven Cavity and Different Reynolds Numbers

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