Multi-Channel Nonlinearity Mitigation Using Neural-Network-Based Feedback Cancellation with Channel Decision Passing Algorithm
- Award ID(s):
- 2148354
- PAR ID:
- 10618006
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8717-9
- Page Range / eLocation ID:
- 286 to 290
- Format(s):
- Medium: X
- Location:
- Springfield, MA, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
The turbulent channel flow database is produced from a direct numerical simulation (DNS) of wall bounded flow with periodic boundary conditions in the longitudinal and transverse directions, and no-slip conditions at the top and bottom walls. In the simulation, the Navier-Stokes equations are solved using a wall {normal, velocity {vorticity formulation. Solutions to the governing equations are provided using a Fourier-Galerkin pseudo-spectral method for the longitudinal and transverse directions and seventh-order Basis-splines (B-splines) collocation method in the wall normal direction. De-aliasing is performed using the 3/2-rule [3]. Temporal integration is performed using a low-storage, third-order Runge-Kutta method. Initially, the flow is driven using a constant volume flux control (imposing a bulk channel mean velocity of U = 1) until stationary conditions are reached. Then the control is changed to a constant applied mean pressure gradient forcing term equivalent to the shear stress resulting from the prior steps. Additional iterations are then performed to further achieve statistical stationarity before outputting fields.more » « less
An official website of the United States government

