Learning Air-Data Parameters for Flush Air Data Sensing Systems

Abstract

An adaptive scattered data approximation scheme was developed to calibrate the Flush Air Data System (FADS) of a surface vessel. An array of pressure sensors were mounted flush with the deckhouse periphery and the airdata parameters were extracted from the pressure measurements. The developed Galerkin derived selfadaptive greedy function approximation scheme gave reliable and robust surrogates for predicting wind speed and direction. The resulting surrogates were also used to evaluate the sensitivity to each of the flush mounted pressure sensor. Fault tolerance of the proposed surrogates were also studied with respect to pressure sensor failure.