Support of Icing Flight Tests with Near Real-Time Data Analysis

Deutscher Luft- und Raumfahrtkongress 2016 DocumentID: 420029 SUPPORT OF ICING FLIGHT TESTS BY NEAR REAL-TIME DATA ANALYSIS C. Raab, P. Ohme, C. Deiler DLR, Institute of Flight Systems Lilienthalplatz 7, 38108 Braunschweig, Germany Abstract Flight testing of aircraft with altered aerodynamic configuration is a safety critical and time consuming task. For the evaluation of the aircraft characteristics under SLD icing conditions, flight tests with artificial ice shapes were performed. These flight tests were supported by online algorithms for the estimation of aerodynamic parameters. Results were available in near real-time onboard the aircraft or already during the debrief- ing on ground. Pre-flight data from wind tunnel experiments could be confirmed already during the flight using these online analysis tools, thus the flight tests could be performed in shorter time and more safe. This paper will introduce the developed analysis tools and will present results from the flight test campaign. Superscripts NOMENCLATURE Center of Gravity , , Translational accelerations along m/s² Neutral Point the body axes Aerodynamic moment reference point , , Aerodynamic force coefficients in - body axes Acronyms , , Aerodynamic moment coeffi- - cients in body axes AOA Angle of Attack Aerodynamic lift coefficient - AOS Angle of Sideslip Aerodynamic drag coefficient - ATRA Advanced Technologies Research Aircraft , , Roll, pitch and yaw moments of kgm² CFD Computational Fluid Dynamics inertia CG Center of Gravity Product of inertia kgm² DLR German Aerospace Center / Deutsches Mean aerodynamic chord m Zentrum für Luft- und Raumfahrt FAA Federal Aviation Administration , , Engine thrust moments in body Nm axes FTI Flight Test Instrumentation Total aircraft mass kg GUI Graphical User Interface , , Roll, pitch and yaw rate rad/s IAS Indicated Airspeed MAC Mean Aerodynamic Chord Dynamic pressure N/m² N1 Engine low pressure shaft rotation speed Half of the wing span m NP Neutral Point, Aerodynamic Center � Reference wing area m² PID Parameter Identification Average spoiler deflection rad RAPIT Rapid Aerodynamic Parameter Identifica- Differential spoiler deflection rad tion Tool , , Engine thrust forces in the body N SLD Supercooled Large Droplets axes Sys-ID System-Identification Time s UDP User Datagram Protocol Airspeed m/s Angle of attack rad Angle of sideslip rad 1. INTRODUCTION , , Aircraft bank, pitch and heading rad Flight safety can be seriously affected by aircraft icing angle because the aerodynamic characteristics may change ,, Ψ Elevator, aileron and rudder rad tremendously under icing conditions. As ice formations deflection built up on the aircraft wing and the horizontal tail, aircraft Ventral rudder deflection rad performance as well as stability and control can be deteri- Standard deviation - orated [14]. A massive increase in drag along with a higher fuel consumption leads to a lower flight range. Ice on Subscripts the aircraft wings results in flow separation at a lower 0 Initial or trim value angle of attack, thus the stall speed is higher than under , Left hand, right hand clean aircraft condition. Ice formation on the horizontal tail Reference value reduces longitudinal stability and controllability because of a higher tendency for stall. In a same way, local flow separation on the wing can change abruptly aileron hinge moments, causing a loss in lateral control or results in an ice- induced roll upset. Especially aircraft with reversible flight controls are particularly threatened by this phenomenon. ©2016 1 Deutscher Luft- und Raumfahrtkongress 2016 The negative effects of inflight icing are responsible for a prop aircraft. Flight data were parsed automatically into significant number of aircraft accidents [10]. Icing and its time slices which were batch processed by a least- influence on aircraft stability and control is a long-term squares algorithm for the estimation of aerodynamic de- research subject [2, 23 - 25]. In order to train pilots how to rivatives. Batch processing of flight test data was also operate an aircraft under icing conditions, accurate simula- used in reference [3] to develop a global nonlinear aero- tor models are needed [8, 25]. Certification authorities dynamic model onboard an Aermacchi MB-326M Impala tackle the problem of aircraft icing by imposing rules on jet aircraft. Two different types of aerodynamic models the manufacturers [18], where they have to show that the were investigated for in the online identification process: a aircraft is tolerant to icing or is equipped with suitable ice fuzzy logic model and one consisting of multivariate or- protection systems. These systems must be designed in a thogonal splines. In [15] aerodynamic stability and control way that they are able to detect icing conditions and acti- derivatives of a subscale aircraft were successfully deter- vate ice protection systems to allow the aircraft to exit mined using Fourier transform regression which is another safely from areas with icing conditions. Icing envelopes method for the online estimation of aerodynamic parame- have been developed, defining atmospheric conditions for ters. It works in the frequency domain and results are the certification process. Nonetheless, in recent times it available in almost real time during the flight maneuver. became apparent that aircraft icing may occur outside the defined envelope. As a result, the FAA updated their rules At the DLR Institute of Flight Systems two online parame- [17] on aircraft icing standards in October 2014 by adding ter estimation tools were developed: RAPIT (Rapid Aero- new requirements which cover also icing caused by su- dynamic Parameter Identification Tool) and FITLAB-Online percooled large droplets (SLD). SLDs are defined as water [4]. RAPIT works in the frequency domain and allows an droplets greater than 50 microns. Even like smaller water estimation of aerodynamic stability and control derivatives droplets they are liquid below temperatures of 0°C and in near real-time during the flight test maneuvers and start to freeze when in contact with the cold aircraft sur- compares them to reference values [21] . FITLAB-Online face. In difference to smaller water droplets, SLDs can uses a Maximum Likelihood method [11] in the time do- dissolve or break up before freezing. Because of this main for the estimation of aerodynamic derivatives. Flight splashing phenomenon, SLDs may form ice aggregations maneuver data are automatically cut into time slices which beyond the traditional ice protection systems. are then batch processed for the estimation of aerodynam- A lack of research in understanding the SLD icing effects ic parameters. Compared to the RAPIT method, the on aircraft and new certification rules were the starting FITLAB-Online algorithm allows the parameter estimation point for a research initiative between the German Aero- of non-linear aerodynamic models valid for the whole flight space Center (DLR) and the Brazilian aircraft manufactur- envelope. Results are available within minutes after the er EMBRAER. The main focuses of these investigations maneuver and can already be analyzed during the debrief- are the effects of SLD icing on the aircraft aerodynamics ing after flight. and developing certification strategies for future aircraft. EMBRAER provided a Phenom 300 prototype as test This paper shows the application of the two aforemen- aircraft, while the DLR Institute of Flight Systems provided tioned tools to flight tests with an EMBRAER Phenom 300 its expertise on system identification. One of the project aircraft equipped with artificial SLD ice shapes. The devel- goals was to develop a flight mechanical model which oped online parameter estimation tools were used to adequately reproduces the aircraft behavior under differ- monitor the aerodynamic characteristics of the aircraft in ent icing conditions [5]. System identification flight tests flight and to quickly identify and evaluate the differences to were planned and performed for aerodynamic model de- the clean aircraft configuration which was analyzed by velopment. This research effort was complemented by a using existing flight test data. To the best of the authors’ joint research project between DLR and TU-Braunschweig knowledge this is the first time that the impact of SLD ice called “SuLaDI” which among other icing topics has the shapes on the aircraft aerodynamic characteristics are aim to develop online parameter estimation algorithms analyzed in both longitudinal and lateral direction in near which are able to provide information about the current real-time during flight test. aerodynamic characteristics. Parameter estimation has been a valuable tool for the In chapter 2, the two online analysis tools are introduced support of flight testing for a long time [20]. Accurate in- and the estimation methods are explained briefly. The formation about aerodynamic stability and control deriva- flight test setup and the performed maneuvers are ex- tives is needed to evaluate the handling characteristics of plained in chapter 3. Results from the online evaluation of the aircraft and to design appropriate control laws. Meth- the flight test data are presented in chapter 4. This in- ods permitting a quick comparison of the actual aircraft cludes also a comparison of aerodynamic model parame- with the original design may speed up the certification ters determined from the flights with SLD icing shapes with process

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