Establishment of Mapping Relationship between Plasma Actuator Parameters and Body Force based on Neural Networks

The velocity field and pressure field induced by Dielectric-Barrier-Discharge (DBD) plasma actuation (DBD) are accurately measured based on PIV and transient differential pressure sensors. A micro lens is mounted on PIV system to capture the critical region of plasma-induced flow. The transient differential pressure sensors with 0.1 Pa resolutions, respond effectively to the inconspicuous change of pressure gradient in the low speed flow. The neural network model is established, revealing the complex nonlinear mapping of external and internal information, while trained by large numbers of experimental samples. The electrical parameters of the plasma generator, the shape parameters of plasma actuator and coordinate parameters are input into the neural network as external information. On the other hand, the velocity field and pressure field serve as internal information to be output. The obtained velocity field and pressure field through the neural network model are regarded as the source terms and are substituted into momentum equilibrium equations and Navier-Stokes equations to calculate the body force field, thus building the mapping relationship between extrinsic plasma actuator parameters and the body force.