Real-time estimation of the heat transfer coefficient of pitot tubes undergoing freezing

Abstract

Purpose The purpose of this paper is to identify freezing in pitot tubes at real-time, by means of the estimated heat transfer coefficient (HTC) at the tip of the probe. The prompt identification of such freezing is paramount to activate and control mechanisms for ice removal, which in turn are essential for the safety of the aircraft and its passengers. Design/methodology/approach The proposed problem is solved by means of an inverse analysis, performed within the Bayesian approach of inverse problems, with temperature measurements assumed available along the pitot probe over time. A heat conduction model is used for describing the average temperature of the pitot tube, which is then rewritten in the form of a state estimation problem. The model is linear and time invariant, so that the inverse problem can be solved using the steady-state Kalman filter (SSKF), a computationally efficient algorithm. Findings The results show that the SSKF is fully capable of recovering the HTC information from the temperature measurements. Any variation of the HTC – either smooth or discontinuous – is promptly detected with high accuracy. Computational effort is significantly lower than the physical time, so that the proposed methodology is fully capable of estimating the HTC at real-time. Originality/value The methodology herein solves the proposed problem not only by estimating the HTC accurately but also doing so with a very small computational effort, so that real-time estimation and freezing control become possible. To the best of the authors’ knowledge, no likewise publications have been found so far.