FAA William J. Hughes

Technical Center 

Aircraft Braking and

Runway Friction

Research 

Somil R. Shah | Airports REDAC Meeting | 2-3 March 2021 
 

 

 

 

 

 

 

Background and Motivation 

• The accumulation of environmental contaminants decreases the braking friction 

available between the tire and the pavement, and can significantly increase 

landing rollout distances 

• Being able to predict when an aircraft has an increased risk of experiencing 

significantly degraded braking and determining the contributing factors is 

an important area of research for preventing overruns 

• Recent landing overruns on contaminated runways have raised questions 

regarding the current stopping performance requirements, methods, 

regulations, and guidance material 

• Existing guidance and regulations make assumptions regarding the 

braking capability of aircraft landing on contaminated runways, which may 

not be fully validated in modern operating conditions 

• Aircraft are landing faster and in more marginal airports, elevating the risk 

of runway overruns if other precursors are present 
C 

Q) 

0.4 

0.2 

0 

T re Pressure 

• 50 psi 

• 100 psi 

• 200 psi 

• 300 psi 

20 40 

Tire Pressure (psi) Maximum Brakin~ Coefficient (tire-to-ground) 

50 μ t/n =-0.0350 (.:!._)

3 

+0.306 (.:!._)

2 

-o.ss1(~ )+0.883 ecM.u 100 LOO 1 OQ 

100 μ 11g =-0.0437 (~)

3 

+o.320 (.:!._)

2 

- o.sos(.:!._)+o.so4 MAX 100 lOO 100 

200 μ1,g =-0.0331(_:!._)

3 

+o.2s2 (y_)

2 

-o.6ss(.:!._)+o.692 ' MAX 100 100 lOO 

( V )

3 

( V )

2 

300 μt/ gMAX = - 0.0401 ( V ) 100 +0.263 100 -0,611 100 +0.614 

60 80 100 120 140 160 180 

Speed (V, knots) 

 

Current Models – Wet Runway 

Tire Pressure 

• 50 psi 

• 100 psi 

• 200 psi 

• 300 psi 

μt/gMAX = maximum tire-to-ground braking coefficient 

V = airplane true ground speed (knots) 
0.30 

0.28 

0.26 

0.24 

0.22 

0.20 

0.18 

0.16 

::i. "' 0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

O.D2 

0.00 

DCA10RA069 : Embraer EMB-145, United Express flight 8050, Ottawa, Ontario, 06/16/2010 

Wheel braking coefficient comparisons 

I I I I I I I 

Tire pressure : 160 psi 

140 135 130 125 120 115 110 105 100 95 90 85 80 

Ground speed. kts 

figm·e 15. μ, comparisons for the United Express flight 8050 accident in Ottawa, Onta,io, 06/16/2010. 

Example of Wet Runway Issues 
 

 

 

 

 

 

 

NTSB Recommendations 

• A-16-023: Continue to work with industry to develop the technology to outfit 

transport-category airplanes with equipment and procedures to routinely 

calculate, record, and convey the airplane braking ability required and/or 

available to slow or stop the airplane during the landing roll. 

• A-16-024: If the systems described in Safety Recommendation A-16-23 are 

shown to be technically and operationally feasible, work with operators and the 

system manufacturers to develop procedures that ensure that airplane-based 

braking ability results can be readily conveyed to, and easily interpreted by, 

arriving flight crews, airport operators, air traffic control personnel, and others 

with a safety need for this information. 

• The NTSB encourages the FAA to perform flight tests on representative 

domestic and international runways that support turbine-powered airplane 

operations in order to validate the wet-ungrooved and wet-grooved wheel 

braking coefficient models in Section 25.109(c). 
 

 

 

 

Airports/Tech Center Work Using 

B727 

• Direct measurement of wheel braking through the attachment of strain gauges 

• Aircraft not airworthy – limited to ground testing up to a certain ground speed 

• Objectives 

1. What friction levels can be achieved 

in winter weather conditions? 

2. What methods can be used to 

quantify friction levels? 

3. What is the behavior of anti-skid 

systems in these low friction 

conditions? 

4. What items can civil transport 

aircraft measure, or be equipped to

measure, to determine the available

friction level? 
 

 

 

 

 

Current Work – Research 

Questions 

• The friction available between a tire and the runway depends on 

many different variables, such as ground speed, tire inflation pressure, 

rainfall intensity, and pavement texture/grooving 

• Which variables have the largest effect on aircraft wheel braking and 

under what conditions may an aircraft experience significantly 

reduced wheel braking compared to current models? 

• Is it possible to predict the wheel braking capability to be expected 

for an aircraft about to land on a contaminated runway and provide 

such information to the flight crew to aid decision-making? 
 

 

 

 

 

 

Involved Organizations 

Aviation Research Division 

Software and Systems Branch 

(ANG-E27) 

• Research requirement to conduct 

wet runway wheel braking flight 

testing 

• ~$1.8M funding 

• Machine learning effort with 

Georgia Tech 

• Airports provided ~$300K of 

funding 

Airport Technology Branch 

(ANG-E26) 

• Past work using Boeing 727 (R&D 

40) for runway friction research 

• Evaluating trapezoidal grooving 

• Flight testing 

• Finite element modeling 

(Rutgers University) 

• Machine learning effort with MIT 

• Planning drainage experiments 

• ~$300K dedicated for aircraft 

braking and runway friction 
 

 

 

 

 

 

 

High-Level Research Plan 

• The problem is complex – many different

variables which are difficult to acquire,

measure, and/or control 

• Historically, things were made simple or 

not considered at all 

• Today, we have the ability to measure

more variables to more detail and 

analyze more data to higher complexity 

• The research team believes that multiple, 

complementary studies are required 

• Primary efforts have been divided into three 

main components: 

• Physical flight testing 

• Machine learning 

• Variable estimation/drainage studies 

Big Data Analytics 
 

 

 

 

 

 

 

 

 

ine contributing factors for significantly 

ng on contaminated runways and determine the relationships 

ying physical principles 

Flight Testing 

• Purpose: acquire high-fidelity data to determ

reduced wheel braki

between them and the underl

• Contracted with Enroute Computer Solutions (ECS) to lease an aircraft and conduct 

flight tests at NASA Wallops Flight Facility where water depths can be closely 

monitored and controlled 

• Completed phase 1 of effort involving the completion of a flight test plan and

coordination with NASA Wallops 

• Evaluating phase 2 proposal involving execution of flight test plan and data 

analysis 

• Following phase 2, make recommendations for future work and on updating

models, standards, guidance material, etc. 

• Limitations: flight testing is expensive, logistically complicated, takes time, and limited

in scope, so what else can we do? 
 

 

 

 

 

 

 

 

 

 

Machine Learning 

• The research team has also been exploring the use of machine learning to 

complement flight testing efforts through academic partners 

• Premise: use readily available big data (aircraft, weather, NOTAMs, etc.) to identify 

degraded braking, determine contributing factors, and predict when degraded braking 

may occur – exploratory research 

• Collaborating with MIT and using data from Aviation Safety Technologies (AST): realtime analysis of aircraft braking thanks to aircraft-based sensors 

• Database of 4.9 million landings between Feb 2017 and Jun 2019 

• Data fused with pilot braking action reports, weather conditions, runway

parameters, etc. to determine relationships 

• Also collaborating with Georgia Tech, using aircraft data from a partner airline 

• Many datasets are being fused and analyzed holistically that have not been 

combined in this way before 
 

 

 

 

 

 

 

 

 

MIT Machine Learning

Approach 

What is performance? (Truth data) 

Which landing conditions? (features) 

Weather Runway 

contamination 

Aircraft 

parameters 

Runway surface 

& geometry 

Pilot Braking Reports 

Aviation Safety Technologies 

(AST) sensor data 

Modelling 

• Using Machine Learning 

classifiers of different 

levels of complexity: 

1. Random Forest 

2. Artificial Neural Net 

Feature importance 

• Using model specific 

methods to infer relative 

importance of features 

Evaluation 

• Is the model performing 

well? (accuracy, AUC, 

sensitivity) 

• What can be done to 

improve performance? 

Iterative process – results of initial, exploratory machine learning work will influence and

guide future machine learning work as well as flight testing and variable estimation efforts 
~.-

' .. 

_, -:o 10 ,c 30 SC •• 

 

 

 

Variable Estimation 

• Purposes: (1) learn to measure, control, and/or estimate relevant variables 

for testing purposes, and (2) validate existing drainage and water depth 

models 

• Gather pavement properties: friction (CFME) values, pavement surface 

texture readings (micro- and macro-texture), etc. and determine how 

variables are related 

• With the addition of cross slope, possibly procure rain simulation equipment 

and run at various rainfall intensities to evaluate film thickness (water depth) 

models 

• Drainage model may be useful for machine learning efforts described on 

the previous slide 
 

 

 

 

 

 

 

 

Trapezoidal

Grooving 

• Purposes: assess the performance of trapezoidal-shaped runway grooving 

relative to FAA Standard Grooving for maintaining skid-resistance and 

preventing hydroplaning 

• Research Path: (1) develop finite element models for simulating aircraft tirewater-pavement interaction with both trapezoidal shaped and FAA Standard 

Grooving, and (2) develop reduced-scale laboratory test platform for 

simulating aircraft braking on wet pavement conditions with both 

trapezoidal shaped and FAA Standard Grooving 

• Research effort currently being conducted by staff at Rutgers University 
 

 

 

 

Trapezoidal 

Grooving Status 

• Two different simulation 

models were created to look at • Test platform design has been the interactions of an aircraft finalized and currently under tire and a water surface construction 

Water penetration into tire

contact patch at 140 knots 
 

 

 

 

 

 

 

 

Concluding Remarks 

1. Contaminated runway wheel braking research is inherently complex and very 

multidisciplinary 

2. Historically, a number of simplifications and assumptions were made to make

things work, and they did for the most part, but as the limits of air travel are being 

pushed, such simplifications may not hold true in certain modern operating 

conditions, motivating the need to reassess and potentially update current models

and regulatory/guidance material 

3. The research team believes that a multi-faceted approach is needed, involving a 

mix of full-scale testing and machine learning 

4. Current work primarily revolves around wet runway wheel braking, although 

applications to other contaminants are being considered wherever possible 
 

 

FAA William J. Hughes

Technical Center 

Aircraft Braking and

Runway Friction

Research 

Questions and Discussion 
 

 

FAA William J. Hughes

Technical Center 

Aircraft Braking and

Runway Friction

Research 

Backup Slides 
 

 

 

 

 

 

 

 

 

 

Some Terminology 

• Wheel Braking Coefficient – ratio of deceleration force from braked wheels relative to 

the sum of the normal forces acting on the wheels 

• Aircraft Braking Coefficient – ratio of the deceleration force from the braked and 

unbraked wheels relative to the sum of the normal force acting on the aircraft 

• Tire-to-Ground Friction Coefficient – deceleration force of a single tire divided by the 

normal force on that tire 

• Maximum Tire-to-Ground Friction Coefficient – the highest amount of wheel braking 

achievable given tire, pavement, and environmental conditions 

• Pilot Braking Action Report (PIREP) – qualitative assessment of the slipperiness of the

runway made by assessing the deceleration of the aircraft relative to the amount of

brake pressure commanded 
μ m,.'I' 

,.,, 

O.,>; 

i 0.JS 

U.l 

, ... lJ11stahle Region 

o~ - - ~----'----~- -~~-- ~----'-------- o 0.05 O.J 0,J5 O.~ 1125 0..3 O.i5 D 4 

Slip Ratio 

 

 

 

 

 

 

 

 

 

a.s 

Some More Terminology 

• Slip Ratio – one minus the ratio of the 

circumferential speed of a rotating wheel • Mu-Slip Curve – relationship between

to it’s translational speed. slip ratio and the tire-to-ground friction 

coefficient • = 0 (freely rolling) 

• = 1 (locked wheel) 

• Anti-Skid System Efficiency – percentage 

of the maximum tire-to-ground friction 

coefficient that an anti-skid system is 

able to achieve 

• Friction Limited Condition/Braking – a 

wheel braking condition where the ability

of the brakes to supply a decelerating 

force is limited by amount of friction 

available given tire, pavement, and

environmental conditions