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Prediction of Runway Condition Rating (RCR) on Unpaved Runways Using Continuous 1 Friction Measurement Equipment 2

3 4

Jeb S. Tingle, P.E. (corresponding author) 5 Research Civil Engineer 6

U.S. Army Engineer Research and Development Center 7 3909 Halls Ferry Road 8 Vicksburg, MS 39180 9

(601) 634-2467 (phone) 10 (601) 634-4128 (fax) 11

[email protected] 12 13

Gregory J. Norwood, P.E. 14 Research Civil Engineer 15

U.S. Army Engineer Research and Development Center 16 3909 Halls Ferry Road 17 Vicksburg, MS 39180 18

(601) 634-3373 (phone) 19 (601) 634-4128 (fax) 20

[email protected] 21 22

Brian Cotter 23 Applied Research Associates 24

104 Research Road 25 Tyndall AFB, FL 32403 26

(850) 283-0446 27 [email protected] 28

Submission Date: 01 August 2016 29 30

Word Count: 3463 31 Figure Count: 7 32 Table Count: 2 33

Equivalent Word Count: 5713 34 35 36 37 38 39 40 41 42 43 44 45 46

Tingle, Norwood, and Cotter 2

1 ABSTRACT: 2 The objective of this research was to develop a correlation between the Runway Condition 3 Rating (RCR) and runway surface friction measurements for dry and wet unpaved runways 4 supporting C-17 aircraft operations. To achieve this objective, flight tests were conducted with 5 an instrumented C-17 on several unpaved runways of different soil types under a variety of 6 surface moisture conditions. A trailer-based continuous friction measurement device was used to 7 conduct continuous surface friction measurements along the runway immediately prior to and 8 following each C-17 landing event. These data were used to correlate the runway surface 9 friction values from the device to the RCR computed from aircraft performance data. An 10 accurate prediction of RCR allows predictions of aircraft stopping performance under adverse 11 weather conditions. 12 13 14

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INTRODUCTION 1 A major concern in operating aircraft on runways around the world in inclement weather is the 2 ability to accurately measure runway surface friction to predict aircraft stopping performance. In 3 one year, the Federal Aviation Administration (FAA) reported 769 instances of aircraft overruns 4 and lateral departures where adverse weather conditions were a contributing factor (WRFMR 5 working group 1995). Many international and federal agencies have devoted numerous research 6 efforts to develop tools for accurately measuring runway surface friction in all weather 7 conditions, correlating measured runway surface friction to aircraft performance, and 8 standardizing reporting methods. 9 The results of extensive research conducted by the National Aeronautics and Space 10 Administration (NASA), FAA, U.S. Air Force (USAF), and international agencies have divided 11 friction measuring equipment into two broad categories, continuous friction measuring 12 equipment (CFME) and decelerometers (DECs). CFME are devices that provide a continuous 13 measurement of the surface friction along a surface throughout the test period, while DECs 14 estimate the surface friction by correlating the rate of deceleration of a calibrated vehicle to the 15 stopping performance of the standardized vehicle. 16 The friction values produced by CFME and DECs have been shown to relate to aircraft 17 braking performance by NASA, FAA, and USAF (Horne et al. 1977). Test results from studies 18 with instrumented aircraft have shown that comparisons between the device measured friction 19 values and the effective aircraft braking friction can be related indirectly by distinguishing 20 between surfaces that have good or poor surface friction characteristics. Thus, rather than 21 reporting the on the characteristics of the runway surface on an operational basis, the runway 22 friction is measured periodically to ensure that the friction meets minimum standards. 23 This practice has served the aircraft industry well over the last few decades. However, 24 there are operational scenarios where real-time assessment of a runway’s surface friction 25 characteristics is desirable both in the public and military sectors. In the public sector, 26 permanent airport pavements are now designed with transverse cross slopes, grooved PCC, or 27 porous friction courses to rapidly remove water from the surface. However, reduced friction is 28 still a concern under snow and ice conditions as well as to assess the accumulation of rubber. For 29 the military, their permanent pavement systems are similarly designed to maintain runway 30 surface friction; however, contingency operations require operating aircraft in less ideal 31 conditions to meet mission requirements. Military aircraft are often forced to operate in 32 inclement weather and on unpaved runways where operating conditions are far less certain. In 33 these circumstances, a real-time method of measuring the runway surface friction characteristics 34 must be used on an operational basis to support specific mission objectives. 35 36 OBJECTIVE AND SCOPE 37 The objective of this research was to develop a correlation between the Runway Condition 38 Rating (RCR) and runway surface friction measurements for dry and wet unpaved runways 39 supporting C-17 aircraft operations. To achieve this objective, flight tests were conducted with 40 an instrumented C-17 on several unpaved runways of different soil types under a variety of 41 surface moisture conditions. A trailer-based CFME was used to conduct continuous surface 42 friction measurements along the runway immediately prior to and following each C-17 landing 43 event. These data were used to correlate the runway surface friction values from the CFME to 44 the RCR computed by the aircraft. An accurate prediction of RCR allows predictions of aircraft 45 stopping performance under adverse weather conditions. 46 47


DESCRIPTION OF AIRCRAFT 1 The C-17 Globemaster III aircraft was developed by the McDonnell Douglas Corporation (now 2 Boeing) and was designed to perform a wide variety of tasks including the ability to operate on 3 small, austere short landing strips with little or no ground support equipment. Table 1 lists 4 pertinent aircraft characteristics. Figure 1 illustrates the geometric dimensions of the aircraft, 5 and Figure 2 shows a spatial layout of the landing gear. The C-17 has four Pratt & Whitney 6 F117-PW-100 engines with a maximum flat-rated thrust of 40,440 pounds each. Features that 7 enhance the aircraft’s ability to operate on short fields include large externally blown flaps, full 8 span leading edge slats, spoilers, high sink rate landing gear, anti-skid braking, thrust reversers, 9 Head-Up Displays (HUDs), and a fly-by-wire control system. The aircraft also has the ability to 10 turn in a small radius (114 ft) and back up which enhances its ability to negotiate tight turns on 11 narrow runways. The aircraft landing gear system is designed for operations on either paved or 12 semi-prepared (SPRO)/unpaved runways. The landing gear has four independent main gear 13 units of three wheels each (12 total) and a dual-wheel nose gear with the capability of nose-14 wheel steering. The main landing gear (MLG) tires are designed specifically for the C-17 15 aircraft with the ability to absorb high-impact landings and the width to provide adequate 16 flotation on semi-prepared surfaces. Main landing gear tires used for this test were Michelin R1B 17 tires, PN 008-877-0. The tires are conventional bias-construction aircraft tires, size 50 x 21.0-20, 18 30-ply rating. The nose landing gear (NLG) tires were Michelin, PN 008-846. The NLG tires are 19 also bias-construction tires, size 40 X 16.0-14, 26-ply rating. The main gear tires are normally 20 inflated to 138 psi and 144 psi, unloaded and loaded, respectively. The nose gear tires are 21 normally inflated to 155 psi and 165 psi, unloaded and loaded, respectively. 22

The C-17 is equipped with an antiskid control system that provides maximum braking efficiency 23 for all types of runway conditions and prevents locking of the wheels when maximum braking is 24 applied. The antiskid is effective at wheel speeds greater than 15 knots. At speeds below 15 25 knots, the system decreases antiskid braking capability. Touchdown protection prevents each 26 brake from being applied until touchdown and wheel spin-up has occurred. To prevent 27 hydroplaning, the antiskid controller will not apply brake pressure to the forward MLG wheels if 28 the wheel speed is less than 50 percent of the ground speed. Temperature sensors on each brake 29 send temperature information to the antiskid control units. Whenever brake temperatures exceed 30 the hot brake threshold limit of 1,200 degrees Fahrenheit, the warning annunciator panel is 31 illuminated. 32

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1

2 Figure 1. C-17 aircraft dimensions 3

Parameter Dimension

Maximum Gross Takeoff Weight, lbs 585,000Maximum SPRO Weight, lbs 486,000Maximum Payload, lbs 170,400Typical Main Gear Tire Pressure, psi 142Typical Nose Gear Tire Pressure, psi 165Length, ft 174Width (Winglet Tips), ft 169.8Height, ft 55.1Cargo Compartment LxWxH, ftxftxft 88x18x12.3Wheelbase, ft 65.8Pass to Coverage Ratio (Channelized) 1.38Speed at 28,000 ft, knots 450

Table 1. C-17 Aircraft Characteristics


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Figure 2. Spatial dimensions of C-17 landing gear 27

Aircraft Instrumentation 28

The C-17 used in this research was instrumented to collect aircraft response data and calculate the RCR of 29 the aircraft during each braking event. The left forward MLG, right aft MLG, and NLG were 30 instrumented to obtain structural loads information for monitoring during testing and for future modeling 31 work. All four MLGs were instrumented for braking loads, antiskid currents, wheel speeds, brake 32 pressures, and shock strut pressures. The instrumentation was also used for real-time monitoring of gear 33 loads when operating in SPRO/unpaved environments to protect the aircraft structure during testing. The 34 instrumentation data that was collected to allow aircraft RCR calculations are as follows: 35

a. Inertial reference unit parameters, 36 b. Air data computer parameters, 37 c. Aerodynamic surface positions (flap/slat, elevator, spoiler, etc.), 38 d. Controller/handle positions (flap, slat, throttle, pedals, etc.), 39 e. Engine parameters (throttles, N1/EPR, reverser discretes), 40 f. Brake temperatures, loads (12 compensating links) and pressures, 41 g. Brake control valve pressure and position, 42 h. Antiskid currents, 43 i. Wheel speeds, 44 j. Shock strut air pressure and position for the NLG, 45 k. Shock strut air and oil pressures and position for the four MLGs, 46 l. Gross weight and fuel burned, 47 m. Atmospheric data and wind kit, and 48 n. Acceleration at the cg and pilot seat. 49

29 in.29 in.

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Axle CenterlineAxle Centerline Nose GearNose GearTiresTires

Main LandingMain LandingGear TiresGear Tires

PLAN VIEW OF CPLAN VIEW OF C--17 LANDING GEAR AND DIMENSIONS17 LANDING GEAR AND DIMENSIONS

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DESCRIPTION OF THE CONTINUOUS FRICTION MEASUREMENT EQUIPMENT 1 Several surface friction measuring devices were initially evaluated in the test program; however, 2 the trailer-based CFME device described in this paper was used at all test sites. The trailer-based 3 CFME used in this research consisted of a rugged, light and compact towed-trailer system as 4 shown in Figure 3. The CFME device used wass a braked-wheel, fixed-slip device that 5 continuously monitors drag and load (horizontal and vertical force). The device can be towed by 6 any vehicle at speeds up to 80 mph or operated in a walk-behind mode. Typically, the device 7 was tethered to a laptop computer where custom software was used to set the measurement 8 parameters and provide near-real time reporting. During operation, the device calculated and 9 displayed the coefficient of friction, or mu (). The device was calibrated using first principles 10 of physics and was performed by the operator on-site in about ten minutes. 11 12

13 Figure 3. Continuous Friction Measurement Equipment (CFME) Used 14 15 DESCRIPTION OF TEST SITES 16 A primary consideration in evaluating runway surface friction characteristics is the type of 17 runway surface and its macro- and micro-texture (European Aviation Safety Agency, 2008 and 18 2010). For unpaved runways, it was recognized that the runway surface friction is very 19 dependent upon the surface soil type. Soil types are delineated based upon particle size and 20 plasticity characteristics of the fine fraction. In addition, it was recognized by Tingle (1998) that 21 the performance of unpaved runways is also affected by the climate, where ambient moisture 22 conditions impact the near surface soil behavior. Five distinct test sites were selected to provide 23 a representative distribution of the predominant soil types for a variety of climatic conditions. 24 Table 2 presents a summary of the test sites used in this study. The combination of test sites was 25 considered to be representative of approximately 65% of the earth’s soil-climate distributions 26 according to the worldwide soil-climate distribution described in Robinson and Rabalais (1993). 27


1

2 3 DATA COLLECTION 4 In order to collect surface friction data for a variety of soil moisture conditions, aircraft testing 5 was conducted under dry soil conditions first. Then, the runway was re-groomed to remove any 6 ruts or loose material that may adversely affect the data collection. For wet testing, the runway 7 surface was progressively wet using five water trucks to apply the water. The amount of water 8 discharged by each truck was calibrated by measuring the spray width and distance travelled for 9 each gear of the truck at high rpms. The objective of the wetting was to provide a reduced 10 equivalent RCR (friction between the tire and ground) without extreme water penetration such 11 that soil bearing capacity was not lost and the tires would not sink into the ground. Each runway 12 was delineated to include a minimum 300-ft underrun, a 90-ft wide and 500-ft long touchdown 13 box, a wet braking zone (during wet testing), a dry braking zone, and a 1,000-ft long safety zone. 14

For the purposes of predicting the RCR, the relevant aircraft braking maneuvers consisted of 15 maximum effort landings and acceleration-deceleration braking events. A maximum effort 16 landing includes the following: a steep approach with full flaps, slats extended, maximum 17 braking, and idle thrust reverse (the situation most damaging to the runway surface). An 18 acceleration-deceleration maneuver consists of the aircraft accelerating to a target speed on the 19 ground, and then performing maximum effort braking to gather braking performance at lower 20 aircraft speeds. An acceleration-deceleration maneuver was considered as a braking event for 21 this paper. 22

A typical sequence of events during the flight testing of the C-17 would consist of a 23 maximum effort landing after which ground personnel would measure the effects of the landing 24 operation. The aircraft would then perform various taxi movements, or acceleration-deceleration 25 maneuvers according to the test plan. Following the ground maneuvers, the aircraft would 26 takeoff from the airfield after which ground personnel would perform friction measurements on 27 the airfield prior to the next landing event. This sequence of events was developed to provide 28 ground personnel with an opportunity to collect data without significantly affecting aircraft 29 operations thereby reducing overall time requirements and costs. The planned operation 30 sequence included a gradual increase in gross weight at each test condition and a gradual 31 decrease in RCR by cumulative wetting of the runway surface to safely approach the test limits 32 of the aircraft-airfield combination. 33

Total Total Percent

Length1 Width2 Soil WorldwideTest Site Runway ft ft Type Climate %

Schoonover LZ, Fort Hunter Liggett, CA 12-30 6,200 110 SM Semi-Arid 25.7

Goatman LZ, Edwards AFB, CA 07-25 9,900 110 CH Arid3 8.2Young LZ, Fort McCoy, WI 11-29 6,250 110 SM Temperate 18.4Rattlesnake LZ, Fort Chaffee, AR 07-25 6,200 110 CL Temperate 11.2Lakebed Runway 7-25, Edwards AFB, CA 07-25 9,900 110 CH Arid 2.0 1The total length of the runway includes the overruns.

2The total width of the runway includes the 90-ft primary operating surface and 10-ft shoulders on each side.

3Goatman LZ was modified to incorporate more moisture into the soil to simulate a CH in a temperate environment.

Table 2. Airfield Dimensional Summary


The RCR is an index value ranging from 1 (frictionless) to 25 (perfect friction) that relates 1 the frictional behavior of the runway to aircraft performance. An RCR of 4 is typical of ice, 2 while an RCR of 23 is typical of dry unpaved runways. For mission planning purposes, it was 3 necessary to conservatively estimate the Runway Condition Reading (RCR) in order to ensure 4 that the next test point would be conducted in a safe manner. Thus, a rolling average for RCR 5 was determined from the previous three test points. Planning for the next test point used the 6 lowest RCR recorded during the last three test points minus one-half of the range (Equation 1). 7

RCRPlanning = RCRMin(Last 3 Test Points) – [(RCRMax – RCRMin)/2] Eq. 1 8

For example, if the previous three RCR measurements were 17, 18, and 14, then the planning 9 RCR for fourth test point RCRPlanning = 14 – [(18-14)/2] = 12 rounded to the lowest whole 10 number. Prior to establishing three test points, aircraft stopping performance was planned using 11 a conservative RCR of 4. The maximum allowable rut depth was limited to 4 inches. 12

RESULTS 13 A total of 144 data points were collected from the four test sites. Each data point included an 14 aircraft-recorded RCR value and a corresponding friction value measured with the CFME 15 device. Figure 4 presents a plot of the data demonstrating the distribution of data points across 16 the full spectrum of RCR values. Figure 4 also includes linear regression models with and 17 without reliability factors. A simple linear regression provides a coefficient of correlation (R2) 18 of 0.8328 indicating a strong relationship between the average friction value (avg) and the RCR 19 as measured from the aircraft performance data. Since the RCR is used to estimate aircraft 20 stopping distance, it is imperative that reliability be incorporated into any prediction model. 21 Thus, linear regressions related to the RCR minus one standard error of the estimate (STE) and 22 two standard error of the estimate (2STE) are also shown in Figure 4 relating to 68.2% and 95% 23 reliability that the actual RCR will be higher than the predicted value. 24 25

26 Figure 4. Linear regression models of RCR versus CFME device avg 27


A review of the linear regression models including reliability in Figure 4 shows that the models 1 become significantly conservative at the upper range of RCR values. Thus, alternative model 2 forms were evaluated. Figure 5 presents nonlinear regression power models developed from the 3 data with and without reliability factors. The basic nonlinear regression power model provides 4 the same coefficient of correlation (R2) of 0.833 as the linear regression model. However, the 5 nonlinear regression power models including one standard error of the estimate (STE), 1.5 STE, 6 and 2STE show an improved fit with the data due to the nonlinear shape of the curve at the 7 higher RCR values. 8 9

10 Figure 5. Combined RCR data versus CFME device avg 11 12 ANALYSIS 13 The basic linear and nonlinear regression models shown in Figures 4 and 5 demonstrate a 14 reasonable ability to predict the C-17 RCR for a wide range of runway friction conditions (RCR 15 values) using only the measurement of the average surface friction with the CFME device, 16 explaining over 83% of the data. Since the models presented are aircraft specific, they indirectly 17 account for the drag friction characteristics specific to the aircraft design. Thus, the models 18 should not be used for other aircraft with decidedly different aircraft design characteristics. 19 While these models may be improved by adding additional independent variables that affect 20 stopping friction such as rolling resistance due to dynamic deflection, the remaining variables 21 influencing stopping friction have a relatively low impact on the model accuracy while adding 22 unnecessary complexity in the collection of input data. 23 24


To assess the utility of the models, the predicted RCR values were plotted versus the measured 1 RCR values for the basic nonlinear regression power model in Figure 6 to evaluate model bias. 2 As shown in Figure 6, the data straddles the line of equality nicely indicating that the model is 3 not particularly biased. 4 5

6 Figure 6. Predicted RCR versus Measured RCR for the basic nonlinear regression power model 7 8 As implementation of the regression models was being considered, it was recognized that the 9 critical dependency of the prediction on the specific CFME device was a concern. Therefore, a 10 smaller data set was collected using two different units of the same CFME type device to 11 evaluate the repeatability of the measurement between different units. Figure 7 shows the data 12 from two different CFME machines for the same events. The data show a strong correlation 13 between the two units with small differences between the avg values measured by each unit. 14 These data provide an indication that the CFME devices used provide relatively consistent 15 friction values between different units and that the avg values from different units can be used 16 with the regression models presented previously to effectively estimate the RCR for C-17 aircraft 17 operations on unpaved runways. Obviously a more comprehensive study of the precision and 18 bias between numerous units would be welcomed, but the existing data are promising. 19

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Predicted Versus Measured Landing RCR

Flight Test Data Points Line of Equality


1 Figure 7. Comparison of avg values from different CFME units 2 3 Based upon the strong correlations developed and the added confidence in the ability to 4 consistently measure the avg values using different measurement units, further consideration was 5 given to the models for implementation into operational guidance. Initially, the nonlinear 6 regression power model to predict RCR minus 2STE was selected as a conservative estimate of 7 RCR to ensure that 95% of the actual RCR values would be higher than the model predicted 8 value (i.e. more surface friction than predicted to ensure adequate runway length for stopping). 9 However, it was realized that the RCR is only measured and reported as a whole number for the 10 mission computer. An analysis of the predicted values versus measured values for the RCR 11 minus 2STE model indicated that the added conservatism associated with rounding down to the 12 nearest lower whole number would render the prediction unnecessarily conservative. Thus, the 13 RCR minus 1.5STE model was selected for implementation. Equation 2 describes the 14 recommended prediction model. 15 16 RCRLanding = 41.665(avg)1.374 R2 = 0.809 Eq. 2 17 18 CONCLUSIONS AND RECOMMENDATIONS 19 Flight tests were conducted with the C-17 aircraft on different unpaved runways to evaluate the 20 ability of different surface friction devices to predict the RCR. A small deployable trailer-type 21 CFME device was identified as providing a consistent measurement of average surface friction 22 (avg). Regression models were developed to effectively predict the RCR on unpaved runways 23 for C-17 operations using the CFME device. This research resulted in the following conclusions: 24


1. Meaningful linear and nonlinear regression models were developed for predicting the RCR 1 for C-17 operations using average surface friction (avg) measurements from the CFME 2 device. 3

2. The nonlinear regression power model provided a better estimate of the RCR, particularly at 4 higher RCR values, than the linear regression model with similar correlation coefficients. 5

3. The measurement of average surface friction (avg) was reasonably consistent between 6 different CFME units of the same type. 7

8 ACKNOWLEDGEMENTS 9 The tests described and the resulting data presented herein, unless otherwise noted, were 10 obtained from research sponsored by the U.S. Air Force, and performed by the U.S. Army 11 Engineer Research and Development Center, Waterways Experiment Station. Permission was 12 granted by the Director, Geotechnical and Structures Laboratory to publish this information. 13 14 REFERENCES 15 1. Horne, W., Yager, T., Sleeper, R., and Merritt, L. 1977. Preliminary Test Results of the Joint FAA-16

NASA Runway Research Program, Part I – Traction Measurements of Several Runways Under Wet 17 and Dry Conditions With a Boeing 727, a Diagonal Braked Vehicle, and a Mu-Meter. NASA 18 Technical Memorandum TM X-73909. Langley Research Center, Hampton, Virginia. 19

2. Yager, Thomas J. 1999. Aircraft and Ground Vehicle Winter Runway Friction Assessment. 20 NASA/TM-1999-209142. Langley Research Center, Hampton, Virginia. 21

3. European Aviation Safety Agency. 2008. RuFAB – Runway Friction Characteristics Measurement 22 and Aircraft Braking: Volume 4 Operational Friction Measurement and Runway Condition Rating. 23 6575Vol 4.FR. Cologne, Germany. 24

4. European Aviation Safety Agency. 2010. RuFAB – Runway Friction Characteristics Measurement 25 and Aircraft Braking: Volume 3 Functional Friction. 6575Vol 3.FR. Cologne, Germany. 26

5. Robinson, James H. and Rabalais, Conrad P. 1993. Performance of the Combat Engineer Vehicle 27 with Mineplow Operating Worldwide and in Theaters of Operation. Technical Report GL-93-23. 28 U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS. 29

6. Rado, Zoltan and Wambold, James C. 2002. Correlation of Ground Friction Measurements to 30 Aircraft Braking Friction Calculated from Flight Data Recorders. 2002 Federal Aviation 31 Administration Technology Transfer Conference, Atlantic City, NJ. 32

7. Tingle, Jeb S. 1998. Testing and Analysis of C-17 Live-Flight Operations on Semi-Prepared 33 Airfields. Technical Report GL-98-11. U.S. Army Engineer Waterways Experiment Station, 34 Vicksburg, MS. 35

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