Smart icing systems for aircraft icing safety

Ice accretion affects the performance and control of an aircraft and in extreme situations can lead to incidents and accidents. However, changes in performance and control are difficult to sense. As a result, the icing sensors currently in use sense primarily ice accretion, not the effect of the ice. No processed aircraft performance degradation information is available to the pilot. In this paper, the Smart Icing System research program is reviewed and progress towards its development reported. Such a system would sense ice accretion through traditional icing sensors and use modern system identification methods to estimate aircraft performance and control changes. This information would be used to automatically operate ice protection systems, provide aircraft envelope protection and, if icing was severe, adapt the flight controls. All of this would be properly communicated to and coordinated with the flight crew. In addition to describing the basic concept, this paper reviews the research conducted to date in three critical areas; aerodynamics and flight mechanics, aircraft control and identification, and human factors. In addition, the flight simulation development is reviewed, as well as the Twin Otter flight test program that is being conducted in cooperation with NASA Glenn Research Center.

[1]  Jackson E. Bruce Manual for a Workstation-Based Generic Flight Simulation Program (LaRCsim) Version 1.4 , 1995 .

[2]  William R. Perkins,et al.  DETECTION AND CLASSIFICATION OF AIRCRAFT ICING USING NEURAL NETWORKS , 2000 .

[3]  Tamer Basar,et al.  SENSOR INTEGRATION FOR INFLIGHT ICING CHARACTERIZATION USING NEURAL NETWORKS , 2001 .

[4]  F. Hawkins Human factors in aviation. , 1979, Journal of psychosomatic research.

[5]  Nadine B. Sarter,et al.  Smart icing systems for aircraft icing safety , 2002 .

[6]  William R. Perkins,et al.  An Interdisciplinary Approach to Inflight Aircraft Icing Safety , 1998 .

[7]  Nadine B. Sarter,et al.  Supporting Decision Making and Action Selection under Time Pressure and Uncertainty: The Case of In-Flight Icing , 2001, Hum. Factors.

[8]  Thomas P. Ratvasky,et al.  NASA/FAA Tailplane Icing Program Overview , 1999 .

[9]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[10]  Christopher D. Wickens,et al.  12 – Aviation Displays , 1988 .

[11]  Christopher D. Wickens,et al.  The Structure of Attentional Resources , 1980 .

[12]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[13]  Jackson E. Bruce Results of a Flight Simulation Software Methods Survey , 1995 .

[14]  Nadine B. Sarter,et al.  Peripheral Visual Feedback: A Powerful Means of Supporting Effective Attention Allocation in Event-Driven, Data-Rich Environments , 2001, Hum. Factors.

[15]  K. Hossain,et al.  Envelope protection and atmospheric disturbances in icing encounters , 2002 .

[16]  Devesh Pokhariyal,et al.  Aircraft flight dynamics with simulated ice accretion , 2001 .

[17]  John D. Lee,et al.  Trust, self-confidence, and operators' adaptation to automation , 1994, Int. J. Hum. Comput. Stud..

[18]  Bonnie M. Muir,et al.  Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems , 1994 .

[19]  William R. Perkins,et al.  H∞ Parameter Identification for Inflight Detection of Aircraft Icing: The Time-Varying Case☆ , 2000 .

[20]  C. Wickens Engineering psychology and human performance, 2nd ed. , 1992 .

[21]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[22]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[23]  Michael B. Bragg,et al.  Characterizing the Effect of Ice on Aircraft Performance and Control from Flight Data , 2002 .

[24]  Petros G. Voulgaris,et al.  Parameter Identification for Inflight Detection of Aircraft Icing , 1999 .

[25]  Petros G. Voulgaris,et al.  Parameter identification for inflight detection and characterization of aircraft icing , 2000 .

[26]  Michael B. Bragg,et al.  Effect of ice accretion on aircraft flight dynamics , 2000 .

[27]  Marc Rauw,et al.  FDC 1.2 - A Simulink Toolbox for Flight Dynamics and Control Analysis , 2001 .

[28]  N. Sarter,et al.  Supporting decision-making and action selection under time pressure and uncertainty: The case of inflight icing , 2001 .