Atmospheric Icing Sensors for UAV’s

1 Taimur Rashid, Umair N. Mughal & Muhammad S. Virk

Atmospheric Icing Research Team, Narvik University College, 8505 Narvik Norway 1 Email: 502006@student.hin.no

Abstract— this paper exhibits significance of ice sensory in

unmanned aerial vehicle (UAV) as remote monitoring unit and

discuss the significant factors incorporated in the autopilot

design implementation for reliable operation. UAVs’ ice

monitoring methodologies are discussed and suitable options

are evaluated.

Keywords—ice sensor; unmanned aerial vehicle; autopilot,

design implementation; control system

I. BACKGROUND

The continual increasing interest in the high north regions

has opened up diversified technological ventures. With

global warming aspect, the access to most remote areas has

been a possibility in relation to development. Despite of the

remoteness, harsh cold climate is proving to be hindrance in

research and development activities. Severe ice accretion

events, whether in the hilly mountainous areas or in the

plain plateaus are continuously probing the competence of

installations at remote location.

Atmospheric icing is typically categorized based on the

icing deposition and accretion process. The first is

precipitation icing which involves wet snow and freezing

precipitation and secondly In-Cloud icing known as

rime/glaze along with fog [1]. Usually icing events occur

when temperature is around 0°C and the relative humidity is

more than 95% [2]. Reliable measurements in icing

conditions are eminent for further usage of data in various

applications.

The remote monitoring of icing conditions in harsh

climate is a viable solution to provide efficiency and

reliability. The remote monitoring of high altitudes can be

optimally achieved through the physical installations with

mountable sensors. Such type of systems installed in cold

regions has intermittent and unreliable behavior due to

various parameters influencing directly or indirectly [3].

This is purely because of the fact that the installed system in

the cold environment is subjected to various types of

metrological conditions [4]. The factors influencing static

monitoring icing sensors are greatly affected by the outside

environment parameters coupled with inherent design

problems causing the non reliability to the system.

Furthermore difficulty in equipment access and logistics

issues adds to the unpredictability nature of seamless

operations. However the remote monitoring solution is

ideally suited for the ice monitoring systems in cold climate

of intense nature, see Fig. 1. It reduces the shortcomings of

the site access, logistics maintenance, troubleshooting and

in time equipment serviceability.

Fig. 1. Operation of UAV in Cold Environment, [7]

II. INTRODUCTION

A. UAV Classification

Over the past few decades UAVs are performing the role of remote monitoring sensory systems in various disciplines. The effective utilization of system maintenance and reliability, virtually eliminating the life risk can be enticing to utilize the platform in customized area. The utility of small, medium and large scale unmanned aerial vehicles is continuously on the rise. The European Unmanned Vehicle Association identifies five main categories of UAVs [5].

Close range aerial vehicles can operate in a range of less than 25 km. These are light weight hand launched aircrafts. Short range UAV operate within a range of 25-100 km and are designed to fly within a limited area. Medium range UAVs can perform flight within a range of 100-200 km. The aerodynamic design and control system feedback incorporation to auto pilot is more advanced as performance parameters are towards the higher side. Long range UAVs can perform mission within the range of 200-500 km. and utilize further advanced technology and incorporate advanced payload image sensory equipment along with complex autopilot design. The remote link is established between the ground control station and airborne platform.

Due to advancement in embedded technology, all of the types can accommodate the Infra Red imaging device which can be the effective towards the icing rate computation. The endurance of the UAVs is dependent upon the size and aerodynamic design, which can be extended to the remote operation or monitoring from less than a half an hour till few tens. The commercial UAV market includes research

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(metrological, geological etc), search and rescue, fire protection, law enforcement, communication and disaster management [6].

III. UAV AND ENVIRONMENTAL VULNERABILITIES

At present, a common icing avoidance strategy is simply

not to fly when icing is forecast. Consequently, UAV

missions in cold seasons and cold regions can be delayed for

days when icing conditions persist. The atmospheric icing

continues to enforce the efforts in UAV technology to go for

all-weather types for operating in the cold regions as the

discussed approach significantly limits the UAV missions as

a result of icing, The vital element in realizing this objective

is to comprehend influence of a small scaled geometry and a

lower speed on the ice accretion process [8].

The environmental variables directly influence the UAVs

performance. Though human safety and life risk is

minimum in unmanned systems but environmental factors

degrades the system performance and successful rate of the

missions. Successful mission probability is highly

dependent on the following factors under environmental

conditions [9].

i. On board sensor sustainability

ii. Vehicle reliability and performance

iii. On board autonomous system

The atmospheric icing influence under the environmental

factors umbrella, can be categorized in the following

elements

i. Icing physics (Ice load correlated with

meteorology)

ii. Aerodynamics effects (function of icing

conditions flight duration, airfoil and vehicle

speed)

iii. Vehicle operations (depending upon mission

profile)

The small, short and medium and long range UAVs are most likely to be subjected to be affects of the harsh weather conditions in terms of atmospheric icing conditions. Considering the remote operations performed by long range UAVs that can fly for greater endurance and at higher altitude can be avoided by severe icing impact and load. On the other hand all types of UAVs are vulnerable to the condensation of the icing due to super cooled droplets.

The ice accretion relies heavily on temperature, liquid water content and droplet size. Cloud liquid water content (LWC) is the density of liquid water in a cloud stated in grams of water per cubic meter (g/m3). LWC is the key to determine amount of water available for icing. Usually values of 1.7 g/m3 can be found in cumuliform clouds even if usually LWC usually ranges from 0.3 g/m3 to 0.6 g/m3. The aerodynamic behavior of the aerofoil subjected to the icing conditions is affected by additional layer of deposited ice as a result of the icing condensations or icing events on the surface. The smaller scale UAVs would be subjected to

more icing as smaller structures tend to gather a larger amount of ice rather than bigger, in case of non-dimensional icing shape comparison [10]. This implies that subjected to the same conditions, ice accretion is more dangerous for small structures than for bigger. The rime and glaze ice accreted on the UAV wing can change its aerodynamic profile. Rime ice is caused as a result of super cooled droplets freezing immediately after impacting the wing surface. The milky white color and the opaque appearance are due to air entrapped by water droplets. Secondly, glaze shown in fig 2 is caused by super cooled water droplets flowing on aircraft surface (run- back) and freezing at a location which is different from the impact area. It is transparent and has irregular shape characterized by one or two horns generated by the run-back freezing. In addition to this, the ice accretion could block the Pitot tube responsible for providing static and dynamic pressure resulting into the airspeed. This could result in erroneous control signals generated by the flight computer consequently adversely effects the aircraft performance causing the accident.

Fig. 2. Icing profile on wings [11]

The change in aerodynamic profile can lead to the reduced lift to drag ratio which could entirely change the aerodynamic profile of the structure. The resultant effect could reach to stall conditions, specifically during the take-off and landing procedure which are critical for the aerial vehicle. Fig 3 shows the effect of ice on coefficient of lift Cl and coefficient of drag Cd. ΔClmax is reduction in maximum lift coefficient and Δαstall is the reduction in the angle of attack at which the stall occurs. Also Cl versus Cd for clean, rough and large icing aerofoil surface is given which clearly indicated the adverse influence under icing conditions which needs to be avoided.

Fig. 3. Lift and Drag coefficient subject to large icing [12]

(Rime Ice) (Glaze Ice)

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Environmental Influence

The autopilot of the UAV incorporates all the

parameters and in flight planning algorithms. The UAV

control system guarantees for the mission planning and

enable the autopilot to make aerodynamic corrections based

on the external environmental variables. In case of ice

accretion on the wings of the UAV, a situation will arise

when error is introduced varying the aerodynamic behavior

control algorithm will not be able to incorporate. As a result,

the flight parameters together with the external variables

will be fed to the autopilot erroneously as shown in fig 4.

Fig. 4. Erroneous control feedback subjected to icing

IV. PROPOSED TECHNIQUES

The ice detection based on the in cloud and in flight icing

should be differentiated based on the sensing techniques.

The sensor capable of predicting the icing conditions with

the certain atmospheric conditions can be utilized to the

great extent in strengthening the algorithms. The control

architecture of the autonomous vehicle systems includes the

feedback from all the different sensors. The feedback

control algorithm takes the input from the sensors and

environmental conditions. The erroneous conditions

occurring in the flight parameters are corrected by

introducing additional feedback system for the icing rate

signals. At the primary stage of the research, icing sensors

can be evaluated based on commercial of the shelf COTS

available sensors. These must be surface mounted device

sensors on the UAVs. This scenario demands effective

integration and aerodynamic installation of the

electromechanical icing sensors. The operation of this

technique is expected to be based on the ability to

differentiate the icing type and icing physics parameters

related to icing rate, load etc. as depicted in fig 5.

Fig. 5. Ice sensor capability for UAVs

The second proposed methodology is based on passive

detection of ice during in flight operations with the aid of

Infra-red (IR) sensors. The benefit of Infra-red sensors is

that they are readily part and parcel of the payload package

of the UAV avionics systems and IR imagery can be

analyzed through image processing techniques. This

methodology will require the infrared analysis techniques

implementation through image processing to detect the

gradient of ice and generate a sensory signal value (fig 6).

Fig. 6. Infrared ice detection UAV implementation

This ice detection signal will serve as an additional

feedback to the autopilot system which will correct the

autopilot parameters in the icing conditions. In both the

methodologies fig 7 will represent the overall solution.

Fig. 7. Operation of UAV in cold environment [7]

Input Data

From

Sensors

With Errors

Auto Pilot

Computer Control

Surface

Aircraft

Motion

Conventional

Sensors

Auto Pilot

Computer

Control

Surface

Aircraft

Motion

Conventional

Sensors

Icing Sensor/IR

image processing

input

+

-

+

-

Input Data

From

Sensors

with icing

corrections

+

-

Icing Sensor

Icing type

Interpretation

Icing Physics

Interpretation

On Board IR Payload

Imagery

Imagery Analysis

Computing Algorithm

(Image Processing)

Result

Icing Parameters

On Board Control System

Input

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V. ICE SENSING – KEY BRIDGING ELEMENT IN COLD

CLIMATE AS COGNITIVE INTERCOMMUNICATION

UAVs under mission profile behaves as a key intra

communicating system with the outside environmental

factors and human machine interface. The pre-programming

capability of the UAV mission profile via humans indicates

the strong cognitive relationship, which adheres to the

artificial intelligence of the system decision making based

on the pre-loaded mission profile and feedback control

algorithms. The performance of the UAV in cold climate

atmospheric impacts the mission profile and whole

cognitive intra communication processes. This might lead to

the system failure where arises the need of a bridging

element able to cater for these specific conditions. The

techniques discussed to detect and somewhat measure the

ice to an extent, based on which the complete system should

attain the sufficiency of artificial intelligence and create and

effective inter-communication amongst the on-board

avionics (fig 8).

Fig. 8. Ice sensing techniques- Bridge to cognitive intra-communication

VI. DISCUSSION

The intended work is an effort towards grasping the

significance of ice sensing techniques as a key cognitive

intercommunication tool which could ultimately serve as a

bridge towards the On board and ground based human intra-

communication. The implication of two discussed

techniques could be utilized as an individual or hybrid based

solution towards ice forecasting, which could enable the

small and medium scaled UAVs towards the all-weather

capability. The artificial intelligence platform at on board

UAVs can be considered as a key player which is further

assisted by several on board sensing equipment providing

their feedback through the control system. However the

cognitive info communication loop can be affected by harsh

cold environment for the existing flight system without

incorporation of the additional sensing methodology.

The techniques can be further developed to improve the

flight performance and the decision making of the system in

fully autonomous mode and human assisted mode, hence

improving the intra communication amongst the system

modules.

REFERENCES

[1] ISO 12494: “Atmospheric icing of structures”, ISO/TC 98/SC3,

2000-07-20

[2] S. Fikke, G. Ronsten, A. Heimo, S. Kunz, M. Ostrozlik, P.-E. Persson, J. Sabata, B. Wareing, B. Wichura, J. Chum, T. Laakso, K. Säntti, L. Makkonen, COST-727, “Atmospheric icing on structures: Measurements and data collection on icing: state of the art”, Publication of MeteoSwiss, 75, 110 pp. 2006,

[3] M. S. Virk, T. Rashid, M. Y. Mustafa, “Atmospheric ice monitoring

system operation at remote locations in cold region”, volume 4, 2013 [4] Foder., M.H. ISO 12494 – Atmospheric icing on structures and how

to use it, 11th International Offshore and Polar Engineering

Conference, Norway, 2001 [5] “European Unmanned Vehicles Systems Association”, Voice of

Forum for the Unmanned Vehicles Systems Community, EUROUVS, 1998

[6] Z. Sarri, “Survey of UAV applications in civil market”, June 2001

[7] http://icestories.exploratorium.edu/dispatches/antarctic-projects/unmanned-aerial-vehicles

[8] K. Szilder, S. McIlwain , “In-flight icing of UAVs – the influence of flight speed coupled with chord size”, Canadian Aeronautics and Space Journal, 2012, 58(02): 83-94, 10.5589/q12-007

[9] R. A. Siquig, “Impact of icing on unmanned aerial vehicle (UAV) operation”, Naval Air Development Center, Warminste, Report Number PR 90:015:442, 1991

[10] G. Mingione, M. Barocco, “Flight in icing conditions – Summary”, French DGAC

[11] E. Harold Addy, “Ice accretions and icing effects for modern airfoils”, NASA/TP—2000-210031, 2000

[12] AGARD, “Effects of adverse weather on aerodynamics, Proceedings of the AGARD Fluid Dynamics Specialists Meeting”, AGARD-CP-496, ISBN 92-835-0644-8, 1991

Ground

Communication

System

Human

Machine

Interface

UAV Ground

Segment

UAV On Board

Segment

Reference

Database

(Comparison)

Parameters Display

& Override

assistance

Intra Communication

Link Autonomous

system

Flight

Control

system

Payload Camera

System

On Board

Communication

System

Sensors Icing

Sensor IR image

processing

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