Simulating Irrotational Gusts in a Low-Speed Wind Tunnel for Aerodynamic Data Assimilation

Abstract Large-amplitude flow disturbances such as gusts can drastically change the aerodynamic loads on machines. Developing active flow control strategies to mitigate these gusts is challenging because the flow response depends non-linearly on the gust parameters. Wind-tunnel experiments and computational models can explore this parameter space but suffer from individual limitations such as a sensor's limited field of view or modeling uncertainties. However, combining both tools through data assimilation can alleviate some issues and generate richer data sets. For accurate results, it is important that the computational model can realistically predict the flow in the wind tunnel. For this purpose, we present a computational model (a digital twin) for a low-speed aerodynamics wind tunnel with irrotational gust generation using suction at the top of the test section. The model couples a viscous flow solution without wind-tunnel walls with a potential flow to correct the normal velocity at the walls and model the suction. We demonstrate the simulation of gust responses using three examples and compare different airfoils and approximations to show their effects. In the last example, we illustrate how to apply this model for data assimilation with experiments by inferring the time-varying gust strength from synthetic velocity measurements.