Experimental Validation of Sparse Sensor Placement Optimization for Flight-By-Feel of a Delta Wing

The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm is a data-reducing approach for extracting maximum information from a low-order sparse approximation of a dense dataset for use in continuous prediction of one or more system parameters. The SSPOP algorithm can work directly with discrete data, such as the calculated velocity at nodes in a computational fluid dynamics (CFD) model, and is simpler and faster to implement than conventional gradient-based optimization methods. This research is the first experimental validation of an SSPOP-selected design point (DP), or set of sensor locations, for a flight-by-feel (FBF) flow-sensing system on a wing. We evaluate the absolute and relative computational and experimental performance of five three-sensor DPs on a NACA 4415, 45-degree-swept delta wing for predicting the angle of attack (AoA) from airflow velocity and pressure measurements. The experimental results from artificial hair-cell airflow velocity sensors (AHS) qualitatively validate the computer models but are subject to large errors. The pressure sensor experimental results quantitatively validated the models, with the SSPOP DP error of 0.703 degrees AoA nearly matching the optimum DP error of 0.692 degrees, confirming that SSPOP finds a near-optimal sensor placement solution for flow sensors.