Flush Air Data Sensing Based on Dimensionless Input and Output Neural Networks With Less Data

Abstract

Neural networks have the ability to deal with the flush air data sensing (FADS) system of various vehicles. However, the demand for large quantities of training data limits its application. To overcome the problem, this paper develops a FADS algorithm called dimensionless input and output neural networks FADS (DIO-NNFADS) to estimate air data states. The DIO-NNFADS is utilized to approximate the aerodynamic model defined by dimensional analysis, which decouples the freestream static pressure. Thus, trained by less data from a single flight profile, the DIO-NNFADS can achieve good accuracy in the entire flight envelope, effectively reducing the training data for neural networks. The Mach number, angle of attack, angle of sideslip, and the pressure coefficients are directly output by the DIO-NNFADS. And the static pressure and dynamic pressure are solved by the equations composed of the measured pressures and pressure coefficients. The proposed FADS algorithm is verified on a simplified supersonic model through numerical simulation. Results show that the algorithm can estimate the Mach number within the relative error of 2.9%, static pressure and dynamic pressure within the relative error of 6.2%, and the angle of incidence within the absolute error of 0.4° in the entire flight envelope. Besides, the optimal size of the training data set for the DIO-NNFADS is discussed. Furthermore, the influence of port layout and selection is analyzed, and the algorithm also shows good performance for a port layout without stagnation point.