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 debriefing 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 𝐶𝐶𝐶𝐶 𝑥𝑥 𝑦𝑦 𝑧𝑧 Aerodynamic moment reference point 𝑎𝑎 , 𝑎𝑎 , 𝑎𝑎 Aerodynamic force coefficients in - 𝑁𝑁𝑁𝑁 body axes 𝑋𝑋 𝑌𝑌 𝑍𝑍 Acronyms𝑅𝑅𝑅𝑅 𝐶𝐶 , 𝐶𝐶 , 𝐶𝐶 Aerodynamic moment coeffi- - cients in body axes AOA Angle of Attack 𝑙𝑙 𝑚𝑚 𝑛𝑛 𝐶𝐶 𝐶𝐶 𝐶𝐶 Aerodynamic lift coefficient - AOS Angle of Sideslip Aerodynamic drag coefficient - ATRA Advanced Technologies Research Aircraft 𝐿𝐿 , 𝐶𝐶 , Roll, pitch and yaw moments of kgm² CFD Computational Fluid Dynamics 𝐷𝐷 𝐶𝐶 inertia CG Center of Gravity 𝑥𝑥𝑥𝑥 𝑦𝑦𝑦𝑦 𝑧𝑧𝑧𝑧 𝐼𝐼 𝐼𝐼 𝐼𝐼 Product of inertia kgm² DLR German Aerospace Center / Deutsches Mean aerodynamic chord m Zentrum für Luft- und Raumfahrt 𝑥𝑥𝑥𝑥 , 𝐼𝐼 , Engine thrust moments in body Nm FAA Federal Aviation Administration 𝜇𝜇 𝑙𝑙 axes FTI Flight Test Instrumentation 𝑥𝑥 𝑦𝑦 𝑧𝑧 𝑀𝑀 𝑀𝑀 𝑀𝑀 Total aircraft mass kg GUI Graphical User Interface , , Roll, pitch and yaw rate rad/s IAS Indicated Airspeed MAC Mean Aerodynamic Chord 𝑚𝑚 Dynamic pressure N/m² N1 Engine low pressure shaft rotation speed 𝑝𝑝 𝑞𝑞 𝑟𝑟 Half of the wing span m NP Neutral Point, Aerodynamic Center 𝑞𝑞� Reference wing area m² PID Parameter Identification 𝑠𝑠 Average spoiler deflection rad RAPIT Rapid Aerodynamic Parameter Identifica- 𝑆𝑆 Differential spoiler deflection rad 𝑎𝑎𝑎𝑎𝑎𝑎 tion Tool 𝑠𝑠,𝑝𝑝 , Engine thrust forces in the body N 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 SLD Supercooled Large Droplets 𝑠𝑠𝑝𝑝 axes 𝑥𝑥 𝑦𝑦 𝑧𝑧 Sys-ID System-Identification 𝑇𝑇 𝑇𝑇 𝑇𝑇 Time s UDP User Datagram Protocol Airspeed m/s 𝑡𝑡 Angle of attack rad 𝑉𝑉 Angle of sideslip rad 1. INTRODUCTION , 𝛼𝛼, Aircraft bank, pitch and heading rad Flight safety can be seriously affected by aircraft icing 𝛽𝛽 angle because the aerodynamic characteristics may change 𝜙𝜙,𝜃𝜃, 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 𝜂𝜂 𝜉𝜉 𝜁𝜁 Ventral rudder deflection rad performance as well as stability and control can be deteri- Standard deviation - 𝑉𝑉 orated [14]. A massive increase in drag along with a high- 𝜁𝜁 𝜎𝜎 er 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 𝐿𝐿𝐿𝐿 𝑅𝑅𝑅𝑅 a higher tendency for stall. In a same way, local flow sepa- 𝑟𝑟𝑟𝑟𝑟𝑟 ration 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.

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]ITLAB. F -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 and increase flight test safety. Usually flight data the ones determined from flights with the clean aircraft. are recorded onboard the aircraft and analyzed later on Finally a conclusion and an outlook on future research the ground because the system identification algorithms activities are given in chapter 5. are computationally demanding. With increasing compu- ting power and advanced data acquisition systems in the late 1990s, real-time estimation algorithms became practi- 2. ONLINE ANALYSIS TOOLS cally usable onboard the aircraft. A sequential total least- 2.1. RAPIT squares method was applied in reference [13] to estimate aerodynamic parameters of a twin-engine turboprop air- RAPIT is based on a parameter estimation method called craft. An extended Kalman filter was used to correct the ‘Fourier transform regression’ being explained briefly in sensor measurements for errors. In reference [27] the the following sections. RAPIT already demonstrated a estimation of aerodynamic stability and control derivatives successful estimation of aerodynamic parameters during a was demonstrated successfully on board a C-12C turbo- flight test campaign with the DLR research aircraft ATRA

[22]. Here the software tool was installed onboard an 2.1.2. Aerodynamic Derivative Model Airbus A320 and system identification maneuvers were performed. A set of six linear equations for the total force and moment coefficients defines the aerodynamic model. Each equa- 2.1.1. Total Aerodynamic Coefficients tion consists of a summation of a bias term and of derivative terms describing different influences on the particular The first step in the estimation process is the determina- aerodynamic force or moment. The model equations ap- tion of the total aerodynamic force and moment coeffi- proximate the total aerodynamic force and moment coefficients. They are calculated from the measured transla- cients derived from measurements with the Eqs. (1) to (8). tional accelerations at the aircraft CG and the angular rate The aim of the subsequent parameter estimation process measurements. Engine thrust and moments, calculated is to determine the derivative terms in the model equations using an engine model, are subtracted from the total forc- so that the aerodynamic coefficients, derived from meas- es and moments to determine the aerodynamic ones. Eqs. urements, matches the result of model outputs. Eqs. (9) (1) to (3) describe the calculation of the aerodynamic force to (11) determine the total force and moment coefficients coefficients in the aircraft body-fixed axes. for the longitudinal aircraft motion. They are functions of the angle of attack , pitch rate , elevator deflection , (1) = ( ) ( ) and the average spoiler deflection : 𝐶𝐶𝐶𝐶 𝑋𝑋 𝑥𝑥 𝑥𝑥 𝛼𝛼 𝑞𝑞 𝜂𝜂 𝐶𝐶 𝑚𝑚𝑎𝑎 − 𝑇𝑇 ⁄ 𝑞𝑞�𝑆𝑆 𝑎𝑎𝑎𝑎𝑎𝑎 (2) = ( ) (9) 𝑠𝑠𝑝𝑝 𝐶𝐶𝐶𝐶 = + + + + 𝑌𝑌 𝑦𝑦 𝑦𝑦 ⁄ 𝜇𝜇 (3) 𝐶𝐶 = �(𝑚𝑚𝑎𝑎 − 𝑇𝑇 )� (𝑞𝑞�𝑆𝑆) 𝑞𝑞𝑙𝑙 𝐶𝐶𝐿𝐿 𝐶𝐶𝐿𝐿0 𝐶𝐶𝐿𝐿𝐿𝐿𝛼𝛼 𝐶𝐶𝐿𝐿𝐿𝐿 𝐶𝐶𝐿𝐿𝐿𝐿𝜂𝜂 𝐶𝐶𝐿𝐿𝐿𝐿𝐿𝐿 𝑠𝑠𝑝𝑝𝑎𝑎𝑎𝑎𝑎𝑎 𝐶𝐶𝐶𝐶 (10) 𝑉𝑉 𝐶𝐶𝑍𝑍 𝑚𝑚𝑎𝑎𝑧𝑧 − 𝑇𝑇𝑧𝑧 ⁄ 𝑞𝑞�𝑆𝑆 = + + + + is the wing area, the measured dynamic pressure, 𝜇𝜇 , , 𝑞𝑞𝑙𝑙 are the measured linear accelerations at the 𝐶𝐶𝐷𝐷 𝐶𝐶𝐷𝐷0 𝐶𝐶𝐷𝐷𝐷𝐷𝛼𝛼 𝐶𝐶𝐷𝐷𝐷𝐷 𝐶𝐶𝐷𝐷𝐷𝐷𝜂𝜂 𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷 𝑠𝑠𝑝𝑝𝑎𝑎𝑎𝑎𝑔𝑔 𝑆𝑆aircraft𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 CG𝐶𝐶𝐶𝐶 and , ,𝑞𝑞� are the engine force components (11) 𝑉𝑉 𝑎𝑎𝑥𝑥 𝑎𝑎𝑦𝑦 𝑎𝑎𝑧𝑧 = + + + in body-fixed axes.𝑥𝑥 𝑦𝑦The𝑧𝑧 lift and drag coefficients result 𝜇𝜇 𝑇𝑇 𝑇𝑇 𝑇𝑇 𝑅𝑅𝑅𝑅 + 𝑞𝑞𝑙𝑙 from rotating the force coefficient around the angle of 𝐶𝐶𝑚𝑚 𝐶𝐶𝑚𝑚0 𝐶𝐶𝑚𝑚𝑚𝑚𝛼𝛼 𝐶𝐶𝑚𝑚𝑚𝑚 𝐶𝐶𝑚𝑚𝑚𝑚𝜂𝜂 𝑉𝑉 attack 𝑚𝑚𝑚𝑚𝑚𝑚 𝑎𝑎𝑎𝑎𝑎𝑎 (4) = cos + sin The equations approximate the lift,𝐶𝐶 drag𝑠𝑠𝑝𝑝 and pitch mo- 𝛼𝛼 𝐿𝐿 𝑍𝑍 𝑋𝑋 ment coefficients derived from measurements with Eqs. (5) 𝐶𝐶 = −𝐶𝐶 𝛼𝛼 𝐶𝐶 𝛼𝛼 (4), (5), and (7). Similarly, the coefficients for the lateral motion, as described in Eqs. (12) to (14), are functions of 𝐷𝐷 𝑋𝑋 𝑍𝑍 The aerodynamic𝐶𝐶 moment−𝐶𝐶 𝑐𝑐𝑐𝑐 𝑐𝑐coefficients𝛼𝛼 − 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠 𝛼𝛼 , , in the angle of sideslip , roll rate , yaw rate , aileron de- body-fixed axes are computed from the 𝐶𝐶𝐶𝐶angular𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶rates flection , rudder deflection , asymmetric spoiler deflec- 𝑙𝑙 𝑚𝑚 𝑛𝑛 𝛽𝛽 𝑝𝑝 𝑟𝑟 , , , rotational accelerations , , , moments𝐶𝐶 𝐶𝐶 of 𝐶𝐶inertia tion and ventral rudder deflection . Spoiler deflec- 𝜉𝜉 𝜁𝜁 , , , and the engine moment components at the tions show up in the equations for the longitudinal as well 𝑠𝑠𝑝𝑝𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝜁𝜁𝑉𝑉 𝑝𝑝aircraft𝑞𝑞 𝑟𝑟 center of gravity ,𝑝𝑝̇ 𝑞𝑞̇ 𝑟𝑟̇, . The necessary as the lateral aircraft motion. They are used as speed 𝐼𝐼𝑥𝑥𝑥𝑥 𝐼𝐼𝑦𝑦𝑦𝑦𝐼𝐼𝑧𝑧𝑧𝑧 𝐼𝐼𝑥𝑥𝑥𝑥 brakes or support roll maneuvering. The average spoiler rotational accelerations are obtained𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 by𝐶𝐶𝐶𝐶 numerical differ- 𝑥𝑥 𝑦𝑦 𝑧𝑧 deflection is the mean of the LH (left hand) and RH (right entiation of the angular rates𝑀𝑀 in the𝑀𝑀 frequency𝑀𝑀 domain. hand) spoiler deflections, whereas the asymmetric spoiler

defection is determined from the difference of the LH and (6) 1 = ( + ) + RH spoiler deflections. The equations for the lateral mo- 𝐶𝐶𝐶𝐶 tion approximate the side force, roll and yaw moment 𝑥𝑥𝑥𝑥 𝑥𝑥𝑥𝑥 𝑧𝑧𝑧𝑧 𝑦𝑦𝑦𝑦 𝐶𝐶𝑙𝑙 ∙ �𝐼𝐼 𝑝𝑝̇ − 𝐼𝐼 𝑝𝑝𝑝𝑝 𝑟𝑟̇ �𝐼𝐼 − 𝐼𝐼 �𝑞𝑞𝑞𝑞 coefficients derived from measurements with Eqs. (2), (6), 𝑞𝑞�𝑆𝑆𝑆𝑆 𝐶𝐶𝐶𝐶 𝑥𝑥 and (8). (7) 1 − 𝑀𝑀 � = + ( ) + ( ) (12) 𝐶𝐶𝐶𝐶 2 2 = + + + + 𝑚𝑚 𝑦𝑦𝑦𝑦 𝑥𝑥𝑥𝑥 𝑧𝑧𝑧𝑧 𝑥𝑥𝑥𝑥 𝐶𝐶 𝜇𝜇 ∙ �𝐼𝐼 𝑞𝑞̇ 𝐼𝐼 − 𝐼𝐼 𝑝𝑝𝑝𝑝 𝐼𝐼 𝑝𝑝 − 𝑟𝑟 𝑞𝑞�𝑆𝑆𝑙𝑙 𝑌𝑌 𝑌𝑌0 𝑌𝑌𝑌𝑌 + 𝑌𝑌𝑌𝑌 𝑝𝑝𝑝𝑝+ 𝑌𝑌𝑌𝑌 𝑟𝑟𝑟𝑟+ 𝑌𝑌𝑌𝑌 𝐶𝐶𝐶𝐶 𝐶𝐶 𝐶𝐶 𝐶𝐶 𝛽𝛽 𝐶𝐶 𝐶𝐶 𝐶𝐶 𝜉𝜉 𝑦𝑦 − 𝑀𝑀 � 𝑉𝑉 𝑉𝑉 𝑉𝑉 (8) 1 𝐶𝐶𝑌𝑌𝑌𝑌𝜁𝜁 𝐶𝐶𝑌𝑌𝜁𝜁 𝜁𝜁𝑉𝑉 𝐶𝐶𝑌𝑌𝑌𝑌𝑌𝑌𝑠𝑠𝑝𝑝𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = + ( ) + (13) = + + + + + 𝐶𝐶𝐶𝐶 𝑛𝑛 𝑧𝑧𝑧𝑧 𝑥𝑥𝑥𝑥 𝑦𝑦𝑦𝑦 𝑥𝑥𝑥𝑥 𝑅𝑅𝑅𝑅 + 𝑝𝑝𝑝𝑝 + 𝑟𝑟𝑟𝑟 𝐶𝐶 ∙ �𝐼𝐼 𝑟𝑟̇ 𝐼𝐼 𝑝𝑝̇ − 𝑞𝑞𝑞𝑞 �𝐼𝐼 − 𝐼𝐼 �𝑝𝑝𝑝𝑝 𝐶𝐶𝑙𝑙 𝐶𝐶𝑙𝑙0 𝐶𝐶𝑙𝑙𝑙𝑙𝛽𝛽 𝐶𝐶𝑙𝑙𝑙𝑙 𝐶𝐶𝑙𝑙𝑙𝑙 𝐶𝐶𝑙𝑙𝑙𝑙𝜉𝜉 𝐶𝐶𝑙𝑙𝑙𝑙𝜁𝜁 𝑞𝑞�𝑆𝑆𝑆𝑆 𝐶𝐶𝐶𝐶 𝑉𝑉 𝑉𝑉 𝑧𝑧 𝑙𝑙𝜁𝜁𝑉𝑉 𝑉𝑉 𝑙𝑙𝑙𝑙𝑙𝑙 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 Equations (6) to (8) determine− 𝑀𝑀 the� aerodynamic moment 𝐶𝐶 𝜁𝜁 𝐶𝐶 𝑠𝑠𝑝𝑝 (14) = + + + + coefficients at the aircraft CG. Using the lever arms be- 𝑅𝑅𝑅𝑅 + 𝑝𝑝+𝑝𝑝 𝑟𝑟+𝑟𝑟 tween a geometrically fixed point and the current location 𝐶𝐶𝑛𝑛 𝐶𝐶𝑛𝑛0 𝐶𝐶𝑛𝑛𝑛𝑛𝛽𝛽 𝐶𝐶𝑛𝑛𝑛𝑛 𝐶𝐶𝑛𝑛𝑛𝑛 𝐶𝐶𝑛𝑛𝑛𝑛𝜉𝜉 of the aircraft CG, the moment coefficients are trans- 𝑉𝑉 𝑉𝑉 𝑛𝑛𝑛𝑛 𝑛𝑛𝜁𝜁𝑉𝑉 𝑉𝑉 𝑛𝑛𝑛𝑛𝑛𝑛 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 formed to a common body-fixed reference point RP at Each derivative term in Eqs. 𝐶𝐶(9) 𝜁𝜁to (14)𝐶𝐶 𝜁𝜁has 𝐶𝐶a dedicated𝑠𝑠𝑝𝑝 25 % MAC. This processing step is necessary to make the influence on the static and dynamic characteristics of the later estimated moment derivatives comparable to results aircraft [9]. For example, values for the lift curve slope from flight tests with the clean aircraft configuration. The and the pitching moment derivative define the air- 𝐿𝐿𝐿𝐿 total aerodynamic force and moment coefficients present- craft’s static stability margin. Other important properties𝐶𝐶 𝑚𝑚𝑚𝑚 ed in the Eqs. (1) to (8) are evaluated each time new are control derivatives like e.g. the aileron𝐶𝐶 effectiveness measurements from the aircraft sensors are available. and the yaw damping .

𝐶𝐶𝑙𝑙𝑙𝑙 𝐶𝐶𝑛𝑛𝑛𝑛

2.1.3. Fourier Transform Regression model. However, the missing aerodynamic zero coefficients are of minor concern for the flight test, because The equations in section 2.1.2 can be stated for each new these terms do not affect the task of monitoring the air- measurement that is acquired. Written in matrix form, they craft’s control and stability characteristics. A least-squares lead to an overdetermined system of equations. This sys- solution is computed for each transformed regression tem of equations constitutes a linear regression problem: equation (Eqs. (9) - (14)) and the aerodynamic derivatives On the left side are the aerodynamic coefficients being are estimated. For each estimated parameter the standard determined from the acceleration and angular rate meas- deviation is calculated from the covariance matrix of the urements Eqs. (1-8), on the right side are the modeled least-squares solution as described in references [10] and aerodynamic derivatives being multiplied by measured [12]. Least𝜎𝜎-squares estimation is repeated at fixed time flight mechanical quantities and control deflections Eqs. intervals using the Fourier transformed regression equa- (9-14). The derivatives in Eqs. (9-14) have to be deter- tions. During the performed flight tests, estimates for the mined in a way that the model results in a ‘best fit’ for the aerodynamic derivatives were updated every second. calculated aerodynamic coefficients. This parameter estimation problem can be solved by equation error meth- 2.1.4. Operation and User Interface ods. The challenge of online parameter estimation is however, that both sides of the equation are updated each RAPIT is an online parameter estimation tool implemented time new measurements are acquired. with the software development environment LabVIEW. It One suitable method for online parameter estimation is was designed to run on a laptop equipped with a dual core Fourier transform regression which is implemented in the 2.90 GHz processor and 8 GB RAM under MS Windows 7. RAPIT software. It was developed by Morelli [13, 12] and A graphical user interface allows easy operation of the solves the equation error problem in the frequency do- software. The parameter estimation process is started and main. For this purpose the aerodynamic coefficients de- stopped by the user to optimally cover the flight maneu- termined with Eqs. (1-8) and the flight mechanical meas- vers of interest. RAPIT is connected to a MySQL database urements ( , , , , , ) as well as the control deflections which contains

( , , , , ) on the right side are Fourier trans- - software setup values, formed. This𝛼𝛼 𝛽𝛽transformation𝑝𝑝 𝑞𝑞 𝑟𝑟 𝑉𝑉 is done with a recursive algo- 𝜂𝜂 𝜉𝜉 𝜁𝜁 𝑠𝑠𝑝𝑝𝑎𝑎𝑎𝑎𝑎𝑎 𝑠𝑠𝑝𝑝𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 - reference values for each aerodynamic derivative rithm which is applied to the left-hand and right-hand which in this case were the results from flight tests terms each time the regression equations are updated. with the clean aircraft configuration, Working in the frequency domain has the advantage of - all aerodynamic derivatives determined during each having a fixed memory size for data operations, because parameter estimation run. only the Fourier transformed values at the specified frequencies need to be updated and stored. The algorithm As the database contains all results from different test allows limiting the evaluation of the Fourier transformation points, it is possible to compare the estimated aerodynam- to the frequency range of interest. For flight tests with the ic derivatives during the flight test. This feature was used Phenom 300 the Fourier transformation was evaluated for e. g. to monitor the aileron effectiveness with decreasing a frequency band from 0.05 to 2 Hz with intervals of 0.01 air speed as mentioned in section 3.3. The database can Hz. Flight data were obtained at a rate of 11.4 Hz. Dis- be accessed by other software tools like MS Excel, permit- carding all frequencies smaller than 0.05 Hz after the ting a more in-depth analysis of the results on ground. Fourier transformation removes also bias and drift in the Tabs on the graphical user interface allow the selection of measured values. As a consequence it is not possible to different data views. The user can observe the execution estimate the trim or zero coefficients in the aerodynamic of flight tests by taking a look at time series plots of the

Figure 1: Lift curve and drag polar during flight with artificial ice shapes. Current values (blue crosses), test point maneuver history (green) and clean aircraft model reference (red).

Figure 2: Aircraft stability and control monitor view. measured sensor data. A tabular view lists all derivatives be positive. The current static margin is indicated on a used in Eqs. (9) to (14) with their estimated value and the measuring scale below the aircraft drawing. Visibility of the standard deviation which is updated each time a new least stability and control indications is controlled by a limit for squares estimated has been executed. the standard deviation of the estimated aerodynamic de- Quick looks of the estimation results assist the flight test rivatives. The indication is greyed out for values of the engineer in evaluating the maneuver quality and support standard deviation greater than 6 %. In Figure 2 this is the him on the decision of how to proceed with the flight test case for the yaw damping deviation. program. Total lift and drag coefficients and are continuously computed according to Eqs. (4) and (5) be- 2.2. FITLAB-Online 𝐿𝐿 𝐷𝐷 cause they are required for the parameter 𝐶𝐶estimation𝐶𝐶 pro- The second herein presented online parameter estimation cess. These values are compared in real-time to an air- tool is able to estimate parameters of an arbitrarily com- craft performance model determined from flight data with plex nonlinear 6-DOF aircraft simulation model in time the clean aircraft configuration. Figure 1 shows a data domain. These models are usually used in simulators or view with this comparison during a flight with artificial ice for flight dynamics evaluation and are able to cover the shapes: Current values for the lift and drag state of the complete flight envelope. By comparing the measured and aircraft are marked by blue crosses. Model predictions for simulated outputs the estimation method guarantees, that the lift curve and the drag polar of the clean aircraft are the resulting model behavior is comparable to the real represented by the red lines. Green lines mark the time aircraft. The tool uses an adapted version of the DLR history of the actual lift and drag coefficients recorded parameter estimation software FITLAB [26] and has sev- during maneuvers at the current test point. Differences in eral extensions to allow the online capability. An overview the performance between clean and iced aircraft configu- of the online estimation tool [4] is given in Figure 3. It ration are quickly identified using these two graphs and contains several individual parts used to receive, search the lift situation near high angle of attack can be moni- and preprocess data as well as estimating the requested tored. During flight test, awareness of the current aircraft model parameters. stability and control state is important for the flight crew, because a significant degradation may lead to an unsafe situation. The data view shown in Figure 2 shows information about the current aerodynamic characteristics and the deviation from the clean aircraft configuration as reference. Dial gauges - similar to engine indications - repre- sent values for a comparison of the main aircraft stability and control characteristics. For the stability and damping terms the difference between the reference and the estimated value is shown as percentage in numbers and also graphically as a red surface on the green dial. Concerning the control efficiency indications for elevator and aileron, a value of 100 % means that the estimated control derivative equals the value for the clean aircraft as reference. If the estimated value is smaller than the reference, the loss is shown as a red surface. Another important indicator is the static stability margin which is the relative distance between the CG and the aerodynamic neutral point NP. For Figure 3: Tool chain of time domain online parameter a statically stable aircraft the location of the CG must be estimation. forward of the NP so that the static margin should always

In the following, the different software parts are explained: = ( ) ⋆ 1Base 1Base Data acquisition and transfer: Flight data are received via 𝑎𝑎 𝑐𝑐 𝛼𝛼 − 𝛼𝛼 UDP and stored in a shared memory allowing access to containing the influence of the angle of attack via the pa- each part of the software. rameter , allows to change the aircraft stall point due to the icing ⋆effects. A further detailed derivation of these Data Parser: The stored flight data are searched by a equations𝛼𝛼 is outlined in [7]. Concerning the Eqs. (15) to parser algorithm. It browses the collected flight data for (17) of the aerodynamic ∆-model this results in 4 addition- maneuvers being useful for parameter estimation and al parameters , , and ,being determined marks the detected segments with indication of start and with the online system identification algorithm.∗ 𝐶𝐶𝐶𝐶0 𝑘𝑘1 𝑘𝑘2 𝛼𝛼 end time. 𝑘𝑘 𝑑𝑑 𝑑𝑑 𝑘𝑘

Preprocessing: Unit conversions in order to match the measured flight data with the inputs expected by the flight mechanic model is done in a preprocessing step.

Output Error Method (OEM) and Simulation Model: The Output Error Method conventionally used for time domain parameter estimation applications is implemented in FITLAB and described in detail in several publications such as [11]. Arbitrary nonlinear complex simulation models can be used with the FITLAB core. For the current implementation, a model formulation representing the aircrafts longitudinal motion under icing conditions is used and briefly described in section 2.2.1 as well as in reference [7]. Figure 4: Illustration of aerodynamic model with ∆-model extension. 2.2.1. Aerodynamic Model The FITLAB-Online parameter estimation application for To cover changes in the lateral dynamics, several parame- the Phenom 300 SLD icing flight tests uses the same ters of a decoupled lateral motion model were estimated aerodynamic model approach as presented in [7]. There- and compared the base model parameter values. fore a basic aircraft model using clean configuration flight test data was developed prior to the icing flight tests. For 2.2.2. User Interface covering the aerodynamic icing effects a ∆-model was The main user interfaces for the time domain parameter formulated which can be added when required (see Figure estimation during flight test consist of two GUIs. The data 4). The forces and moments determined by the aerody- parser GUI is shown and explained in Figure 5. namic model are used in the equations of motion of a flight mechanical model. Outputs from this model like accelerations, angular rates, Euler angles, AOA and AOS are approximated to measurements from flight test maneuvers by estimating the aerodynamic parameters. For the icing flight tests the objective was mainly to estimate the longitudinal ∆-model parameters and keeping the base model fixed. For example the condensed ∆-model for drag used in the flight test is given by the following equations:

= + +

𝐷𝐷 Ice 𝐶𝐶𝐶𝐶0 𝐷𝐷0 Base 𝐿𝐿 Base 𝑘𝑘1 (15) ∆𝐶𝐶 𝑘𝑘 ∙ 𝐶𝐶 𝐶𝐶 ∙ 𝑑𝑑 ( ) + ( ) Λ 2 𝑑𝑑𝑘𝑘2 ∗ 𝜕𝜕𝐶𝐶𝐷𝐷 𝐿𝐿 Base �Ice 𝛼𝛼 𝐶𝐶 ∙ Δ𝑋𝑋 𝑘𝑘 � �Base 𝑒𝑒 𝜋𝜋 𝜕𝜕𝑋𝑋� The change of the non-dimensional flow separation point given by

( ) ∗ �Ice 𝛼𝛼 (16) Δ𝑋𝑋tanh𝑘𝑘( ) tanh ( ) tanh( ) = Figure 5: GUI flight data parser. 2(1 + tanh( 2 ) tanh( ) ) 𝑎𝑎1Ice − 𝑎𝑎1Base 𝑎𝑎1Ice 1Base 1Ice After connecting to the flight data shared memory, the − 𝑎𝑎 𝑎𝑎 parser can be configured by selecting a search data chan- with the auxiliary variables nel; certain settings and controls for detecting elevator, aileron and rudder step inputs are predefined [6]. Automat- = ically found segments are continuously listed on the right (17) ⋆ ∗ table of the GUI window. They can be activated for 𝑎𝑎1Ice −𝑐𝑐1Base 𝛼𝛼 𝑘𝑘𝛼𝛼

Figure 6: FITLAB-Online GUI for controlling the parameter estimation process and plotting the results. estimation by pressing the “Activation” button. engine aircraft with a horizontal stabilizer in T-tail configuration. The Phenom 300 has a MTOW of 8.3 t and a wing- The second GUI running in parallel for controlling FITLAB- span of 16 m. It is equipped with a conventional reversible Online is shown in Figure 6. This interface combines all flight control system. The anti-ice systems include leading required parts for parameter estimation, which are the edges of the wings and horizontal tail as well as the engine intakes. For the flight tests artificial ice shapes made - selection of free and fixed parameters, of plastic material were glued to the aircraft skin. Figure 8 - control of parameter and/or bias updating, shows these shapes on the wing upper and lower surface. - estimation / simulation start and Four different aircraft configurations were flown during the - plotting of parameter estimation results. flight test program:

The annotations in Figure 6 explain the different parts of 1) Clean: The aircraft without any modifications. the GUI and show how to control the software for running an essential parameter estimation task. 2) Low-Risk SLD: Artificial ice on the radome, winglets and on the wing upper and lower surface without the wing area of the ailerons (SLD I). 3. FLIGHT TEST 3) Incremental SLD: Artificial ice on the radome, winglets 3.1. Test Aircraft and on the wing upper and lower surface including the wing area of the ailerons (SLD II). 4) Nominal SLD: Artificial ice on the radome, engine pylons, winglets and on the wing upper and lower surface including the wing area of the ailerons (SLD III). Flight tests with the clean aircraft were performed end of 2014, whereas tests with the artificial ice configurations were performed end of 2015. The aircraft was equipped with standard air data sensors and an inertial reference platform. Flight data could be processed onboard with a laptop connected to an onboard flight data network. During flight tests with the clean aircraft the flight crew consisted

Figure 7: EMBRAER Phenom 300 test aircraft. of two pilots, two flight test engineers and one experi- mental engineer. Flight data could also be sent to a telem- EMBRAER provided a prototype of the Phenom 300 busi- etry station on ground. The telemetry was used during all ness jet as test aircraft depicted in Figure 7. It is a twin flights with artificial ice shapes because they were classi-

fied as high risk flights and only a minimum crew of two check with former flight data. For the determination of the pilots and one flight test engineer was allowed only. total aerodynamic coefficients, engine forces and moments were required. These were computed by an engine model using engine rotation speed N1, Mach number and pressure altitude as input. A weight and balance routine calculated the current aircraft weight, CG position and moments of inertia. Aircraft ramp weight and CG position as well as the current fuel content were needed for this calculation. The flight data client gathered the calculated data as well as the sensor measurements and sends it to RAPIT and FITLAB-Online using a multicast UDP. It was possible to install the described software setup on ground in a telemetry room as well as directly onboard the aircraft. That way, the online analysis tools could also be operated by a flight test engineer in the aircraft during flight, in cases the telemetry data connection to ground was not available.

Telemetry Server

Flight Data Bus

Data Conditioning

Sensor Calibration

Engine Model

Weight and Balance Calculation

FLIGHT DATA CLIENT

Multicast UDP

Figure 8: Artificial shapes for SLD icing on the upper and lower wing surface, outer wing and radome. RAPIT Fitlab-Online

3.2. Flight Test Setup Figure 9: Schematic overview of the online analysis tools software setup. A prerequisite for the online processing of flight data was the creation of a suitable interface to the existing flight data acquisition environment of EMBRAER. Figure 9 3.3. Flight Test Program shows an overview of the software setup that was used during the flight test trials. All data measured by the air- System-Identification maneuvers were performed at three craft sensors and signals from the different aircraft sys- different altitudes and two different velocities according to tems were acquired by a central telemetry server. The the test point definitions listed in Table 1, in order to inves- telemetry server provided a continuous data stream with a tigate the aircraft handling characteristics and its perfor- sample rate of 88 ms which could be accessed by a com- mance. The maneuvers consisted of the following step munication interface. Two laptops running the online anal- inputs into the aircraft control surfaces: ysis tools were connected to the server by a standard Ethernet network cable. Pre-processing was necessary - Elevator-3211. before the flight data could be used by the online estima- - Single elevator step input to excite the phugoid motion tools. For this task a “Flight Data Client” was pro- tion. grammed which was executed on one of the laptops con- - Bank-to-bank maneuver with ailerons and activated nected to the telemetry server. The client acquired select- roll spoilers. ed measurements from various sensors e. g. air data - Bank-to-bank maneuver only with ailerons. system, inertial reference platform and engine rotational - Ruder doublet maneuver with ventral rudder damping. speed. If necessary, measurements were corrected with - Rudder doublets maneuver without ventral rudder calibration equations determined from a data compatibility damping.

All maneuvers started from a trimmed horizontal flight area near the ailerons with the ice shapes in configuration condition specified by the test point definition. The low SLD II increased the drag further to over 70 %. Ice shapes speed limit of 170 kt VIAS was established after analyzing on the engine pylons as in configuration SLD III raised the wind tunnel experiments, which resulted in an increase of drag further by additional 1.3 %. A comparison of the total the stall speed by a factor of nearly 1.3 for configurations lift coefficients showed no significant difference to the with artificial ice shapes. Monitoring of the aileron effec- clean aircraft situation. The lift is nearly 2 % less in config- tiveness was critical during flights with artificial ice shapes. uration SLD I and over 3 % less in configuration SLD III. For this reason a special maneuver was performed prior to Although the lift characteristics showed no major deterio- the typical System-Identification maneuvers. The aircraft ration, the deviation from the linear region must be ob- was accelerated to 210 kt VIAS and bank-to-bank maneu- served in more detail for lower speeds i.e. higher angles of vers with low amplitudes were performed. RAPIT was attack. On the current flights, however the lowest speed used to estimate the aileron control effectiveness deriva- was limited to 170 kt VIAS for safety reasons. tive. After a successful estimation process the aircraft decelerated by 10 kt and the bank-to-bank maneuver was 4.1.2. Aileron Effectiveness repeated. The estimated aileron control effectiveness was Monitoring of the lateral controllability was of particular monitored and the procedure was repeated until the air- interest for the flight test crew because wind tunnel exper- craft reached a velocity of 170 kt. iments with the SLD ice shapes showed flow separation on the outboard wing area near the ailerons at high angles No. VIAS Altitude of attack. For this reason, first a low risk configuration (SLD I) with no ice shapes in front of the ailerons was 1 170 kt 10000 ft flown and then a configuration (SLD II) with the wings fully 2 220 kt 10000 ft equipped with shapes. As already mentioned in section 3.3, at the start of each test program short aileron step 3 170 kt 15000 ft inputs with decreasing airspeed were flown to check the 4 220 kt 15000 ft aileron effectiveness with RAPIT. Time series plots in Figure 10 show the results determined with RAPIT during 5 170 kt 20000 ft one aileron step excitation with the SLD I configuration. 6 220 kt 20000 ft

Table 1: Test points for flights with artificial ice shapes.

4. RESULTS AND DISCUSSION 4.1. RAPIT 4.1.1. Lift-Drag Situation Right after take-off the aircraft lift and drag status was closely monitored using the scopes depicted in Figure 1 and Figure 2. Before the initiation of the flight maneuvers, the aircraft approached a stationary state with constant altitude and velocity. The determined total lift and drag coefficients at this trimmed condition were compared to an aerodynamic model identified from flight tests with the clean aircraft, considering the same AOA, Mach number and stabilizer trim. The results of this comparison are listed in Table 2.

Dev. f. Dev. f. AOA, deg Clean, % Clean, % 𝑪𝑪𝑳𝑳 𝑪𝑪𝑫𝑫 SLD I 3.5 -1.8 57.3 SLD II 3.5 -2.9 71.7 SLD III 3.6 -3.2 73.0

Table 2: Measured lift and drag deviations from the clean aircraft at 15000 ft and 170 kt VIAS.

Lift and drag coefficients were determined at a trimmed flight condition of 15000 ft altitude and 170 kt VIAS and Figure 10: Online estimation of the aileron efficiency then averaged over 25 measurements. The artificial SLD with RAPIT during an aileron step input. ice shapes significantly increased the total aircraft drag. 𝐶𝐶𝑙𝑙𝑙𝑙 The ‘Low-Risk’ configuration SLD I showed already a drag The first graph shows the control surface deflections of increment of nearly 60 % compared to the clean aircraft. aileron, rudder and roll spoilers, the second graph the roll This resulted also in higher fuel consumption, cutting the available flight test time by 50 %. Covering the wing outer and yaw rate and the third one the angle of sideslip during

the maneuver. In the last graph the progress of the esti- 4.1.3. Influence on Yaw Stability mation of the aileron control derivative is shown. Red The massive gain in drag on the aircraft wings affects the stars mark the estimated aileron control derivative at each 𝑙𝑙𝑙𝑙 lateral stability derivatives, especially those concerning the time point. Error bars around the estimates𝐶𝐶 indicate a yaw axis. In order to identify changes in the yaw stability 95 % interval calculated from the standard deviation. All several bank-to-bank, rudder doublet and steady heading estimates are normalized to a reference value determined sideslip maneuvers were performed at the test points. In from tests with the clean aircraft. An estimate with a value Table 3 yaw stability derivatives are listed, estimated by of one equals the reference being marked with a blue line. RAPIT directly after these maneuvers with configuration The estimation algorithm started processing flight data five SLD III. The results show that the weathercock stability seconds before the first estimation. Then a least-squares derivative has increased moderately by 6.7 %. A rela- estimation was conducted every second as can be seen tive error of only 2.2 % indicates a good quality for this from the plot. The algorithm only used the measurements 𝐶𝐶𝑛𝑛𝑛𝑛 acquired during the maneuver and had no a-priori infor- estimate. More noticeable are the changes in the yaw mation about the derivative. Before the maneuver the damping derivative which has increased due to the ice estimated values were far off the reference line. After the shapes by nearly 50 % compared to the value of the clean 𝐶𝐶𝑛𝑛𝑛𝑛 maneuver the estimates converged towards a constant aircraft. The roll-yaw coupling derivative also changed value, close to the reference line. In this case the estimat- significantly by decreasing more than 60 %.𝑛𝑛𝑛𝑛 However both ed was slightly higher than in the clean aircraft configu- estimates show large relative errors of nearly𝐶𝐶 26 % for ration, which cannot be explained in detail. and 10 % for . One reason for the poor quality of the𝑛𝑛𝑛𝑛 𝐶𝐶𝑙𝑙𝑙𝑙 estimates could be that the linear model is not able cover𝐶𝐶 𝑛𝑛𝑛𝑛 all the nonlinear𝐶𝐶 effects of the ice shapes on the lateral stability. The influence of the SLD ice shapes on yaw stability are analyzed in more detail in section 4.2.2, using the maximum likelihood algorithm on the total flight data acquired during each flight.

Rel. ∆ from Derivative Error, % Clean, % 2.2 6.7

𝑪𝑪𝒏𝒏𝒏𝒏 25.5 -60.8 𝑪𝑪𝒏𝒏𝒏𝒏 9.5 47.6% 𝒏𝒏𝒏𝒏 Table 3: Estimated𝑪𝑪 yaw stability derivatives for configuration SLD III and their deviation from the clean aircraft configuration. Figure 11: Estimated aileron control efficiency at different airspeeds and for different SLD configurations. 4.2. FITLAB-Online During flight test FITLAB-Online was mainly used for Figure 11 shows the results of the estimated aileron deriv- monitoring the lift and drag characteristics of the aircraft ative for different SLD configurations. The values were with different artificial ice shape installations (see section again normalized with the derivative using the clean air- 3.1). The data parser worked well and nearly all relevant craft as reference. For each SLD configuration the esti- time slices with Sys-ID maneuvers in longitudinal as well mated derivative is shown for 170 and 210 kt VIAS. The as in lateral motion were detected and marked error indicator marks the 95 % interval for the estimated automatically. After finishing all maneuvers a complete value, calculated from the standard deviation. Taking a evaluation for the longitudinal motion was performed look at the bars the following tendency can be seen: All quickly. Lift curves and drag polars for the actual icing configurations were the wings were fully covered with the case in comparison to the clean aircraft case could be ice shapes showed lower aileron effectiveness. It de- discussed with the overall flight test team already in the creased to nearly 93 % in the SLD III configuration. In flight debriefing. First lateral motion evaluations were general the results show that the deterioration of the ailer- performed directly afterwards so that a comprehensive on effectiveness is not very pronounced. The differences view of the aircraft characteristics under the tested ice between the estimates for airspeed of 170 kt and 210 kt shapes was available shortly after touchdown. are also not very significant. The confidence intervals are much larger than the differences between the SLD config- 4.2.1. Lift-Drag Situation urations. For that reason the online estimation can only Figure 12 to Figure 17 show some FITLAB-Online results give a tendency which has to be examined further with for longitudinal motion. The time history plots (Figure 12 advanced algorithms like described in section 4.2.2. Dur- and Figure 14) show exemplary three elevator-3211 inputs ing the tests at different airspeeds the AOA ranged only and have the same color scheme as the lift curve slopes from 1.8 to 3.8 deg, at higher AOA the control degradation and drag polars. The blue lines show the measured flight may become larger. However RAPIT confirmed within test data, the black dashed lines show the clean aircraft seconds after the maneuvers the results from wind tunnel simulation model and the red lines show the newly tests which also showed a degradation of 7 % in aileron identified icing models. Figure 12 and Figure 13 illustrate effectiveness [19]. the results for the SLD I icing case. Due to flight safety reasons the pilots handled the aircaft very carefully especially during the first SLD flight (AOA range from 0 to

Figure 12: SLD I time histories of long. aerodynamic coef- Figure 14: SLD III time histories of long. aerodynamic ficients for elevator-3211 multistep input ma- coefficients for elevator-3211 multistep input neuvers (blue: flight test measurement / black: maneuvers (blue: flight test measurement / model clean / red: identified icing model). black: model clean / red: identified icing model).

Figure 13: SLD I lift curve and drag polar (blue: flight test Figure 15: SLD III lift curve and drag polar (blue: flight test measurement / black: model clean / red: identi- measurement / black: model clean / red: identified icing model). fied icing model).

5 deg). It can be seen clearly that the aircraft has operated truely identifiable with the parameters which were set free only in the linear region of the lift curve and no for estimation. Apart from that, a difference between the measureable nonlinear lift curve slope change compared clean and icing lift characteristic is not measurable. to the clean aircraft was observed. The drag polar has changed drastically and shows a drag at zero lift increase of about 100% compared to the clean aircraft case. The Parameter SLD I SLD III estimated drag parameter values of the icing ∆-model (see section 2.2.1) can be found in Table 4. Overall this ice 0.980 1.182 modeling approach achieves an excellent match between 𝑪𝑪𝑪𝑪𝑪𝑪 -0.048 -0.074 flight data and model output. Figure 14 and Figure 15 𝒌𝒌 show the similar time histories, lift curve slope and drag 𝒅𝒅𝒌𝒌𝒌𝒌 0.661 1.474 polar, but in this case for the flight test with configuration 𝒌𝒌𝒌𝒌 SLD III. 𝒅𝒅 -0.281 -0.304 Despite a more extensive icing condition the covered AOA ∗ Table𝒌𝒌 4𝜶𝜶: Estimated icing drag Δ-model parameters. range from -1 deg to 7 deg is larger so that a slight nonlinearity in the upper AOA region is visible, but was not

change in the drag polar curvature for the SLD icing cases compared to the clean aircraft case are illustrated impressively. 4.2.2. Lateral Motion For the analysis of changes in lateral motion derivatives all times slices with lateral maneuvers were used as input for the estimation process. The evaluation of the lateral motion for the flight in SLD III configuration are exemplary presented in Figure 17 by time history plots of the six most important aircraft motion and control quantities. Overall four maneuvers are shown, two with aileron and two with rudder step inputs. The control surface deflections of the left aileron and rudder are illustrated in the lower two time histories. The upper four time histories show the aircraft roll and yaw rates and , the bank angle and the angle of sideslip . The line color code corresponds to the longi- 𝑝𝑝 𝑟𝑟 𝜃𝜃 Figure 16: Drag versus AOA comparison: Clean Configu tudinal motion plots presented in the previous section. 𝛽𝛽 ration – SLD I – SLD III. Due to the massive drag increase in the icing flight cases the yaw damping behavior has changed significantly com- The increase of the drag at zero lift component is very pared to the clean aircraft, the yaw rate amplitudes are significant: It is about 120% higher compared to the clean much smaller. As mentioned in section 2.2.1 for the lateral aircraft (see Table 4). In difference to the SLD I case the motion Sys-ID was performed with a decoupled lateral curvature of the SLD III drag polar is significantly higher motion model. During the Sys-ID process only the deriva- compared to the clean aircraft curve. Overall the same tives which indicated a significant contribution to a good icing model approach is appropriate for finding a good match between flight test data and model outputs (see red match between flight test and simulation model output. For and blue lines in Figure 17) were newly estimated for summarizing the longitudinal motion results cases icing. In Table 5 the final results of this estimation process compared to the clean aircraft simulation. Figure 16 shows for the SLD III configuration are listed. The estimated roll the simulation model drag coefficient versus AOA for both damping coefficient shows almost no deviation from icing cases. Here again the zero drag increase and the the clean aircraft parameter value. The weathercock sta- 𝐶𝐶𝑙𝑙𝑙𝑙 Measurement Model CLEAN-Configuration Identified Icing Model

Figure 17: Lateral maneuver time histories / aileron and rudder step inputs.

bility increased by nearly 3 %, presumably because of the results from wind tunnel results. The growth in drag on the aircraft wings led to significant changes in the yaw the increasing𝑛𝑛𝑛𝑛 wing drag, whereas the aileron control effectiveness𝐶𝐶 derivative decreased by nearly 6 %. dynamics. A massive increase in yaw damping was detected with both online parameter estimation tools, where- 𝑙𝑙𝑙𝑙 𝐶𝐶 Rel. as no major changes in the weathercock stability were SLD III Δ Clean, Std. Derivative Error, determined. The yaw-roll coupling dynamics will be scope - Clean % Dev. % of further investigations using more detailed offline parameter estimation algorithms. 0.0068 -0.7 0.2 0.0021 Experiences gained with the online parameter estimation tools during the flight test campaign confirmed their use- 𝑪𝑪𝒍𝒍𝒍𝒍 -0.1678 -333.8 4.5 0.0053 fulness for the support of the flight test crew. Flight safety 𝑪𝑪𝒍𝒍𝒍𝒍 0.0079 -5.8 0.3 0.0004 was increased because pre-flight information about the aerodynamic characteristics could be confirmed during the 𝒍𝒍𝒍𝒍 0.0053 2.8 0.1 0.0001 𝑪𝑪 first maneuvers and critical performance parameters could 𝑪𝑪𝒏𝒏𝒏𝒏 0.0456 -108.8 12.5 0.0005 be monitored directly. Decisions on how to proceed could be taken much faster because information about the level 𝒏𝒏𝒏𝒏 𝑪𝑪 -0.1259 36.9 0.4 0.0016 of aerodynamic alteration was already available at the Table 5: Lateral model parameter change due to SLD III debriefing of the flight. Online processing of flight data in 𝑪𝑪𝒏𝒏𝒏𝒏 icing. order to make the flight test more safe and efficient will be the scope of ongoing research at DLR Institute of Flight More pronounced is the change in the yaw damping coef- System. ficient which increases by almost 40 % due to the increased wing drag with ice shaped attached. 𝑛𝑛𝑛𝑛 The most𝐶𝐶 significant deviations from the clean aircraft ACKNOWLEDGEMENT state can be seen in the results for the yaw-roll coupling The authors would like to thank the Flight Test Division at derivatives and showing a reduction of more than EMBRAER for giving the opportunity of being part in the 100 %. However the relative error for these estimates is SLD flight test program and the Flight Data Processing 𝐶𝐶𝑙𝑙𝑙𝑙 𝐶𝐶𝑛𝑛𝑛𝑛 quite high when compared to the other derivatives. 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