Mean-flow adjoint analysis via HLBM enables gradient computation for drag and dissipation objectives in laminar-to-turbulent regimes around porous cylinders, with auto-diff generating LES adjoint kernels at Re=3900.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.
citing papers explorer
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Mean-Flow Adjoint Sensitivity Analysis of Unsteady Flow Around Porous Cylinders Using a Homogenized Lattice Boltzmann Method
Mean-flow adjoint analysis via HLBM enables gradient computation for drag and dissipation objectives in laminar-to-turbulent regimes around porous cylinders, with auto-diff generating LES adjoint kernels at Re=3900.
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Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Filtering algorithms reconstruct trajectories of in-silico particles in a stirred tank reactor from noisy IMU data with errors below 10%.