Determination of longitudinal aerodynamic derivatives using flight data from an icing research aircraft

Ranaudo, Richard J.; Reehorst, Andrew L.; Bond, Thomas H.; Batterson, J. G.; Omara, Thomas M.

doi:10.2514/6.1989-754

Cited by 22 publications

(12 citation statements)

References 2 publications

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“…Ranaudo et al 10 attempted to quantify the impact of icing on longitudinalstability and control characteristicsof the aircraft. They used the modi ed stepwise regression (MSR) method for parameter identi cation to determine some of the longitudinal stability and control derivatives of the Twin Otter with and without simulated ice on the horizontal tail.…”

Section: Nomenclaturementioning

confidence: 99%

Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Johnson

Rokhsaz

2001

Journal of Aircraft

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A method of using arti cial neural networks (ANNs) and Kohonen self-organizing maps (SOMs) to detect airframe ice is proposed and investigated. It is hypothesized that ANN systems trained on the aircraft dynamics in real time would converge to different connection weights for iced and clean aircraft. Kohonen SOMs are proposed for detecting these differences automatically and, therefore, recognizing airframe ice accretion. This approach is shown to be capable of acting in an advisory role for the ight crew. The delity of the approach is shown to depend on the level of atmospheric turbulence, as well as on the magnitude of the elevator input. Nomenclature[A] = state matrix [B] = control matrix C L 1 = steady-state trim lift coef cient C m = pitching moment coef cient C x = force coef cient along x axis C z = force coef cient along z axis N c = mean aerodynamic chord D = nondimensionaltime derivative, . N c=2V c /.d=dt/ i yy = nondimensionalmass moment of inertia, 8I yy =N c 3 ½ S _ q = nondimensionalpitch rate, q N c=2V c S = wing area t = time u = forward speed perturbation _ u = nondimensionalperturbation in forward speed, u=V c fug = control input vector V c = velocity of the center of mass W = aircraft weight w = vertical speed perturbation fxg = state variable vector ®= angle of attack and nondimensional perturbation in vertical speed, w=V c ± e = elevator de ection angle ± f = ap de ection angle 2 1 = steady-state pitch angle µ = perturbation in pitch angle ¹ = nondimensionalmass, 2m=N c½ S ½ = air density ¾ = turbulence intensity

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Section: Nomenclaturementioning

confidence: 99%

Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Johnson

Rokhsaz

2001

Journal of Aircraft

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“…To model different icing cases, such as wing ice, tail ice, and full aircraft icing, different coefficient icing factors are used ( Table 1). The aerodynamics and flight mechanics group used NASA flight test data of a Twin Otter in icing [15][16][17] to calculate and estimate the coefficient icing factors. These icing factors are meant to be used in all flight conditions and are not a function of flight conditions such as airspeed and angle of attack.…”

Section: Icing Modelmentioning

confidence: 99%

Icing Encounter Flight Simulator

Deters

Dimock

Selig

2006

Journal of Aircraft

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The Icing Encounter Flight Simulator is one part of the Smart Icing Systems project at the University of Illinois at Urbana-Champaign. From the Smart Icing Systems project, an ice management system was designed that would sense and characterize ice, notify the pilot, and if necessary take measures to ensure the safety of the aircraft. The icing simulator was used as a platform to integrate and test different components of the Smart Icing System. To create an Icing Encounter Flight Simulator, functionality and Smart Icing System components were added to the FlightGear flight simulator. Functionality added to FlightGear include a reconfigurable aircraft model and an icing model, and Smart Icing System components added include an envelope protection system and a glass cockpit enhanced with ice management system features. To ensure a real-time simulation, computationally intensive processes were distributed over several desktop computers linked together through a local network. In order to demonstrate the effectiveness of the Smart Icing System components, two fictional but historically motivated icing scenarios were developed and tested with a simulated DeHavilland DHC-6 Twin Otter, specifically, a tailplane stall event during a steep descent and a roll upset event during an emergency approach. Introduction A S part of the Smart Icing Systems (SIS) project [1,2] at the University of Illinois at Urbana-Champaign, the Icing Encounter Flight Simulator (IEFS) [3][4][5] integrates various SIS components in a simulated aircraft icing environment. The SIS project was started to investigate measures that would help keep the aircraft safe during an icing encounter. To do this an ice management system (IMS) was devised that would sense and characterize the presence of ice, notify the pilot, and ensure the safety of the aircraft. To test and demonstrate the IMS, the IEFS was designed to integrate the different aspects of the SIS project such as the flight dynamics model, autopilot [6], aircraft icing model [7,8], icing characterization routine [9], envelope protection system (EPS) [10], and human factors [11].Instead of creating a new flight simulator for this project, it was decided to adapt an existing simulator. The simulator chosen was the FlightGear flight simulator (FGFS) x for its open source and modular code. Added to FlightGear were a reconfigurable aircraft model, autopilot, and an icing model. Other aspects of the SIS such as the ice weather model, EPS, ice protection system (IPS), neural-networkbased icing characterization, and the IMS-enhanced glass cockpit were integrated into the IEFS. Several components of the IEFS became too computationally intensive to run on a single desktop PC, and so to ensure real-time simulations, the IEFS was divided into different modules to be run over a local area network. The resulting distributed simulator contains six modules: flight dynamics model (FlightGear with the autopilot and icing model), SIS support code (ice weather model, EPS, and IPS), neural-network-based icing charac...

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“…The results obtained from the Twin Otter flight with simulated tailplane icing were used to estimate most of the derivatives. 8 ' 9 The drag and other non-tail related derivatives were obtained from flight test results in which the entire aircraft was subjected to icing. 10 Where values were not available, they were estimated.…”

Section: Clean and Iced Aircraft Modelsmentioning

confidence: 99%

Aircraft flight dynamics with simulated ice accretion

Pokhariyal

Bragg

Hutchison

et al. 2001

39th Aerospace Sciences Meeting and Exhibit

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The effect of ice accretion on aircraft performance and control during trim conditions was modeled and analyzed. A six degree-of-freedom computational flight dynamics model was used to study the effect of ice accretion on the aircraft dynamics. The effects of turbulence and sensor noise were modeled and filters were developed to remove unwanted noisy data without affecting the short period and phugoid modes. This study is part of a larger research program to develop smart icing system technology. The goal of the study reported here was to develop techniques to sense the effect and location of ice accretion on aircraft performance and control during trimmed flight. Control surface steady and unsteady hinge-moments were modeled as a potential aerodynamic performance sensor. Microburst and gravity wave atmospheric disturbances were modeled and their effects on the aircraft performance and control were compared to that of an icing encounter. The simulations showed that atmospheric disturbances could be differentiated from icing encounters. The hinge-moment sensors proved very useful in identifying the wing versus tail location of aircraft icing.

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Determination of longitudinal aerodynamic derivatives using flight data from an icing research aircraft

Cited by 22 publications

References 2 publications

Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Icing Encounter Flight Simulator

Aircraft flight dynamics with simulated ice accretion

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