NASA/TM-2005-213663

Loads Model Development and Analysis for 

the F/A-18 Active Aeroelastic Wing Airplane

Michael J. Allen, Andrew M. Lizotte, Ryan P. Dibley, and Robert Clarke

NASA Dryden Flight Research Center

Edwards, California

November 2005

National Aeronautics and

Space Administration

Dryden Flight Research Center

Edwards, California 93523-0273
NASA/TM-2005-213663

Loads Model Development and Analysis for 

the F/A-18 Active Aeroelastic Wing Airplane

Michael J. Allen, Andrew M. Lizotte, Ryan P. Dibley, and Robert Clarke

NASA Dryden Flight Research Center

Edwards, California

November 2005
ABSTRACT

The Active Aeroelastic Wing airplane was successfully flight-tested in March 2005. During phase 1

of the two-phase program, an onboard excitation system provided independent control surface

movements that were used to develop a loads model for the wing structure and wing control surfaces. The

resulting loads model, which was used to develop the control laws for phase 2, is described. The loads

model was developed from flight data through the use of a multiple linear regression technique. The

loads model input consisted of aircraft states and control surface positions, in addition to nonlinear inputs

that were calculated from flight-measured parameters. The loads model output for each wing consisted of

wing-root bending moment and torque, wing-fold bending moment and torque, inboard and outboard

leading-edge flap hinge moment, trailing-edge flap hinge moment, and aileron hinge moment. The

development of the Active Aeroelastic Wing loads model is described, and the ability of the model to

predict loads during phase 2 research maneuvers is demonstrated. Results show a good match to phase 2

flight data for all loads except inboard and outboard leading-edge flap hinge moments at certain flight

conditions. The average load prediction errors for all loads at all flight conditions are 9.1 percent for

maximum stick-deflection rolls, 4.4 percent for 5-g windup turns, and 7.7 percent for 4-g rolling pullouts.

NOMENCLATURE

A loads model coefficient

AAW Active Aeroelastic Wing

Ail aileron

Ail_sq square of aileron position

CONDUIT control designer’s unified toolbox

deg degree

Errsumsq sum of the squared error values

Errmodel load prediction error

HM hinge moment

I loads model intercept term

ILEF inboard leading-edge flap

Lmeas measured load

Lpred predicted load

N number of data samples

Nz normal acceleration

NzW product of normal acceleration and total aircraft weight

OBES onboard excitation system
2

OLEF outboard leading-edge flap

p roll rate

dynamic pressure

R number of inputs

RPO rolling pullout

Rud rudder position

sign signum function

Stab stabilator position

TEF trailing-edge flap

TEF_pos positive trailing-edge flap

W total aircraft weight

WFBM wing-fold bending moment

WFTQ wing-fold torque

WRBM wing-root bending moment

WRTQ wing-root torque

WUT windup turn

X input set

! angle of attack

" angle of sideslip

# deflection

Subscripts

C collective

D differential

i sample index

j input index 

L left

R right

q
3

INTRODUCTION

The Active Aeroelastic Wing (AAW) project was initiated in 1996 by the Air Force Research

Laboratory and NASA to investigate the use of wing twist for roll control (ref. 1). This concept was

tested on a modified F/A-18 supersonic fighter aircraft (Boeing Company, St. Louis, Missouri) during the

spring of 2005 (fig. 1). Modifications to the AAW aircraft included new wing skin panels to reduce wing

torsional stiffness, independent outboard leading-edge flap (OLEF) drive systems to increase control

authority, and new control laws to drive the four control surfaces on each wing. The control laws

provided surface position commands to the inboard leading-edge flap (ILEF), OLEF, trailing-edge flap

(TEF), and aileron (Ail) of each wing. Differential stabilator deflection was not used in the research

control laws so that roll due only to wing control surfaces could be studied.

EC04-0361-02

Figure 1. Active Aeroelastic Wing airplane.
4

A control design optimization tool called the control designer’s unified toolbox (CONDUIT) (ref. 2)

was used to determine research control law gains to maximize roll rate and produce desirable handling

qualities while maintaining loads within limits. The CONDUIT-based multiple-objective design

technique required accurate models of aircraft aerodynamics and loads to produce acceptable control law

gains. Analytical models initially were used for this purpose but were found to be insufficient for control

design (ref. 3), so a series of 51 flights (phase 1 of the AAW program) were flown to gather data for the

creation of improved aerodynamic and loads models. These models then were used to develop research

control law gains. The second project phase, phase 2, consisted of 35 flights and was used to test the new

control law gains. Data from these flights also were used to validate the aerodynamic and loads models.

Previous projects, which have successfully used loads models for a variety of purposes, guided the

process presented in this report. For example, accurate loads models were required for the Advanced

Fighter Technology Integration (AFTI) F-111 aircraft Maneuver Loads Control (MLC) experiment

(ref. 4). More recently, a loads model correction process that used F/A-18 flight data was performed for

the AAW project (ref. 3). Haas, Flitter, Milano, and Imber used a more complete approach to model

component loads of a helicopter rotor system (refs. 5–7).

The process presented in this report is similar to the research discussed in references 5, 6, and 7, but it

differs primarily in that the loads model development presented in this report is based on a higher number

of flight maneuvers and is for a fixed-wing fighter aircraft at multiple subsonic and supersonic flight

conditions. Neural networks were investigated early in the development of the AAW loads model with

much success (ref. 8) but were abandoned, because the high extrapolation required of the leading-edge

flap hinge moment (HM) predictions could not be easily analyzed for uncertainty. This report describes

the processes that were used to generate an AAW loads model from flight data. Results that show loads

model prediction errors for the AAW aircraft with new control laws also are presented.

FLIGHT TEST DATA

Each wing of the AAW aircraft was instrumented with approximately 100 strain gage bridges. These

strain gages were used to determine the hinge moments of the four control surfaces on each wing and the

bending moment and torque loads at the wing-root and wing-fold locations (refs. 9, 10). Figure 2 shows

the wing load measurement locations. Extensive ground tests were conducted to calibrate the output of

the strain gages for load measurement. During these tests, each wing was covered with 52 load pads

divided into 16 load zones that covered 60 percent of the lower wing surface. Each wing was subjected to

a wide range of distributed and point loads, and the strain gage outputs were measured. The measured

applied load values and resulting strain gage output data were used to develop equations to calculate wing

loads from strain gage flight data.
5

Figure 2. Locations of Active Aeroelastic Wing measured loads.

In the fall of 2002 through the spring of 2003, phase 1 flight tests of the AAW airplane were

conducted to quantify wing aeroelastic effects, perform air data calibration, investigate failure scenarios,

and gather data needed for the development of aerodynamic and loads models. Preprogrammed onboard

excitation system (OBES) maneuvers were used to excite the aircraft response. These maneuvers

consisted of a sequence of small, medium, or large collective or differential doublets individually applied

to each control surface. Collective and differential doublets were used to excite the aircraft longitudinal

and lateral-directional responses, respectively. Tables 1 and 2 present the input sizes for each surface

doublet during each OBES maneuver. Figure 3 shows data from a large collective OBES maneuver.

Table 1. Collective onboard excitation system maneuver doublet magnitudes.

OBES size

, 

deg

, 

deg

,

deg

 and ,

deg

,

deg

, 

deg

Small 1 1 3 1 4 0.8

Medium 2 2 4 2 5 0.8

Large 3 3 5 3 6 0.8

030127

#OLEF #ILEF #TEF #OLEF #ILEF #Ail #Stab
6

Figure 3. Large collective onboard excitation system maneuver. Stabilator position is not shown.

Because the OBES maneuvers were relatively small in magnitude, additional piloted maneuvers were

flown to identify load characteristics at higher load levels. These maneuvers consisted of 5-g windup

turns (WUTs); 4-g rolling pullouts (RPOs); and half-stick, three-fourths–stick, and full-stick rolls.

Push-over–pull-up maneuvers designed for nose-boom air data calibration also were flown. Figure 4

presents the flight conditions at which these maneuvers were flown.

Table 2. Differential onboard excitation system maneuver doublet magnitudes.

OBES size

,

deg

, 

deg

,

deg

,

deg

,

deg

,

deg

 and ,

deg

Small 4 2 2 6 8 6 2

Medium 4 4 4 8 10 6 4

Large 4 6 6 10 12 6 6

#Rud #OLEF #ILEF #TEF #Ail #Stab #OLEF #ILEF

Time, s

Surface position, deg

030128

–6

–4

–2

4

6

2

0

0 5 10 15 20 25 30

Outboard leading-edge flap

Inboard leading-edge flap

Trailing-edge flap

Aileron
7

Figure 4. Active Aeroelastic Wing flight conditions.

DATA CONDITIONING

Several data conditioning steps were performed before the data could be used to create a loads model.

Considerable emphasis was placed on data conditioning because of concern that data spikes, noisy data,

or invalid data could cause the loads model to improperly characterize cause and effect relationships. The

initial steps involved filling in data dropouts and missing data points by means of linear interpolation.

This step was performed to prevent distortions caused by filtering. The data then were synchronized in

time through the use of time signals correlated with each data source on the airplane. Data spikes then

were removed with an interactive spike removal tool, and the data were filtered with fifth-order low-pass

Butterworth filters. Cutoff frequencies for these filters were approximately one-half of the lowest aircraft

structural mode frequency. The filters were run forward and backward to eliminate filter-induced phase

lag. After filtering, the data points were resampled at the sample rate of the surface positions through the

use of linear interpolation. The next step involved the removal of any data points that were added by

interpolation during the previous steps. Interpolated data were removed, because they were added for

filtering only. After the inserted data points were removed, each input parameter was scaled using preset

scale factors. Scale factors were chosen from simulated roll maneuvers to normalize the inputs to an

approximate range of ±1.0. When scaled parameters are used, the importance of an input in the model can

be judged by the value of its corresponding model coefficient.

Separate data sets were used to define the aircraft states and control surface positions associated with

the left and right wing loads. When symmetry is assumed, both left and right wing loads can be used to

derive a single load equation. This derivation was accomplished by reversing the sign on the

lateral-directional states and differential surface positions for left-wing data sets. To de-emphasize loads

at trim, specific sections of data were resampled at a lower sample rate to adjust data density during each

maneuver when the load was low. Because the number of data points was reduced at low loads, high load

data were given more priority by the regression process.

10 11 12

13 14 15

1 16

25,000

4

7

2

5

8

3

6

9

17

19

20,000

15,000

Altitude, ft

10,000

5,000

0.85 0.90 0.95

Mach number 050285

1.10 1.20 1.30
8

LOADS MODEL DEVELOPMENT

The AAW loads model is a collection of load equations in which each equation defines a single

component load at a single flight condition. The component loads that were modeled for the AAW are

wing-root bending moment (WRBM), wing-root torque (WRTQ), wing-fold bending moment (WFBM),

wing-fold torque (WFTQ), inboard leading-edge flap hinge moment (ILEF HM), outboard leading-edge

flap hinge moment (OLEF HM), trailing-edge flap hinge moment (TEF HM), and aileron hinge moment

(Ail HM). The form of each load equation is shown in equation (1).

(1)

The term Lpred is the predicted load from the loads model, I is the intercept term, R is the number of

inputs, and X is the set of input parameters used in each load equation. The equation coefficients, A, were

derived using multiple linear regression. The intercept term, I, was allowed to differ for left and right load

equations to account for measurement biases and aircraft asymmetry. Multiple linear regression produces

equation coefficients that minimize the sum of the squared errors as shown in equation (2),

(2)

where Lmeas is the measured load and N is the number of data samples. The equation coefficients can be

obtained directly using matrix inversion, as shown in equation (3) (ref. 11).

(3)

The input set, X, was chosen for each load equation from a set of aircraft states and control surface

positions. The AAW loads model primarily was intended as a control law design tool used with a

simulation of the AAW aircraft. Therefore, only parameters that were available in the simulation were

used as inputs to the loads model. For this reason, surface actuator positions were used instead of

measured surface positions. The measured surface positions differ from the surface actuator positions

because of surface flexibility. Table 3 presents the set of all candidate aircraft states and surface actuator

positions used for model development. Table 4 lists the new input variables that were calculated from the

parameters provided in table 3. These new input variables were created to account for nonlinear load

responses. Generally, the calculated parameters only slightly improved the accuracy of the model. Inputs

were chosen for each load equation from the set provided in tables 3 and 4. The determination of an input

set for each load equation involved several steps. First, all possible input combinations were examined to

find a subset of inputs that produced the lowest error shown in equation (2). The input set that was chosen

was further refined by disallowing highly correlated inputs, such as normal acceleration and angle of

attack, in the same load equation.

Lpred I Aj X j

j = 1

R

= + $

Errsumsq

LmeasiLpredi – % & ' (

2

i = 1

N

= $

A XTX –1

XT Lmeas =
9

Table 3. Measured inputs.

Parameter Description

Mach Mach number

Dynamic pressure

! Angle of attack

" Angle of sideslip

p Roll rate

Nz Normal acceleration

ILEFL Left inboard leading-edge flap position

ILEFR Right inboard leading-edge flap position

OLEFL Left outboard leading-edge flap position

OLEFR Right outboard leading-edge flap position

TEFL Left trailing-edge flap position

TEFR Right trailing-edge flap position

AilL Left aileron position

AilR Right aileron position

Table 4. Calculated inputs.

Parameter Equation

NzW (total aircraft 

weight)

Ail_sq sign(Ail) (Ail)2

TEF_pos

ILEFC 0.5 (ILEFL + ILEFR)

ILEFD ILEFR – ILEFL

OLEFC 0.5 (OLEFL + OLEFR)

OLEFD OLEFR – OLEFL

AilC 0.5 (AilL + AilR)

AilD AilL – AilR

TEFC 0.5 (TEFL + TEFR)

TEFD TEFL – TEFR

StabC 0.5 (StabL + StabR)

q

Nz •

•

0 0

0

 for TEF

TEF for TEF

<

*

 ̈

©

a

¬

­

®
10

Phase 1 flight data were used to derive the equation coefficients and assess model accuracy. Phase 1

maneuvers consisted of OBES maneuvers and piloted maneuvers. The OBES maneuvers primarily were

used to derive equation coefficients, whereas piloted maneuvers were used for both validation and

derivation. Piloted maneuvers consisted of rolls, 5-g WUTs, 4-g RPOs, and push-over–pull-up

maneuvers. A typical set of derivation maneuvers consisted of small, medium, and large collective and

differential OBES maneuvers, a WUT, a half-stick roll, a full-stick roll, and a full-stick RPO. Validation

with independent maneuvers was an important step in the development of the loads model. This step

allowed the user to check the model for hidden problems, such as highly correlated inputs and

misidentified input-to-load relationships, which would not have been found by only comparing model

accuracy to derivation maneuvers. A typical set of validation maneuvers consisted of a medium collective

OBES maneuver, medium differential OBES maneuver, three-fourths–stick roll, WUT, and

three-fourths–stick RPO. The quality of the loads model was determined with the load prediction error

shown in equation (4). This error calculation is hereafter used in this report to show the performance of

the loads model. The load prediction errors for phase 1 maneuvers ranged from 2 to 20 percent and

typically were slightly better for derivation maneuvers than for validation maneuvers.

(4)

The loads model was implemented in the nonlinear simulation for control law design and testing

purposes. The AAW phase 2 control law design process interfaced with the simulator and attempted to

maximize roll performance while maintaining loads within boundaries and providing adequate handling

qualities (ref. 2). Because the optimization process tended to push at least one wing load or surface hinge

moment to its limit, additional margins were placed on the load limits to account for loads model

prediction uncertainty. Loads model uncertainty at limit loads could not be obtained directly, because the

flight data did not always include high-load data for each load. In these cases, uncertainty was estimated

from cross plots of measured load as a function of predicted load and scatter plots of model prediction

error as a function of measured load. Figures 5 and 6 show a typical set of these plots. Figure 5 shows

trends in the loads model prediction as the load increases. These trends were useful in estimating the

amount of loads model over-prediction or under-prediction that could occur at high load values. Figure 6

shows the load prediction error as a function of measured load. Positive errors on this plot indicate loads

model over-prediction. From these two plots, uncertainty bounds of 10 percent for negative limit loads

and five percent for positive limit loads were used for this load in control design. These relatively low

values were used, because the trends seen in figures 5 and 6 show that high loads will be over-predicted

by the model and thus are conservative. The loads model over-prediction of high loads and

under-prediction of low loads was a typical occurrence for the control surface hinge moments.

Err

model

Lmeasi

Lpredi( – )

2

i = 1

N

$

N -------------------------------------------------------

load limit = -----------------------------------------------------------
11

Figure 5. Load prediction cross plot of the inboard leading-edge flap hinge moment at Mach 0.90,

5,000-ft altitude (flight condition 8).

Figure 6. Load prediction error as a function of measured inboard leading-edge flap hinge moment at

Mach 0.90, 5,000-ft altitude (flight condition 8).

0

0

Lmeas 050286

Lpred

Derivation maneuvers

Validation maneuvers

15

0

Lmeas 050287

(Lpred – Lmeas)/(load limit), percent

Derivation maneuvers

Validation maneuvers

10

5

0

–5

–10

–15
12

PHASE 2 RESULTS

Phase 2 of the AAW project was used to evaluate the use of wing twist for roll control. New control

laws (ref. 2) were designed at the NASA Dryden Flight Research Center (Edwards, California) through

the use of the loads model described in this report. During the spring of 2005, these control laws were

flown with the AAW aircraft and subsequently analyzed. Load time histories and load prediction error

tables for these maneuvers are presented in this section to demonstrate the final accuracy of the loads

model during its intended use. Many of the phase 2 maneuvers cause the leading-edge flap hinge moment

equations to significantly extrapolate from the phase 1 data used to create the model. The lack of

sufficient phase 1 data used for these loads was a result of small magnitude leading-edge flap OBES

maneuvers and the limited use of leading-edge flaps by the standard F/A-18 control system at the AAW

flight conditions. The OBES maneuvers were a smaller fraction of the limit load than desired, because the

original analytically derived loads model significantly over-predicted the leading-edge flap hinge

moments (ref. 3).

Figure 7 shows a typical time history plot for the leading-edge flap hinge moments during a full-stick

roll. This plot shows the over-prediction of the leading-edge flap hinge moments by the loads model.

Figure 8 shows the shape of the loads model prediction as load increases. A perfect load prediction would

follow the dashed line in figure 8, indicating that the predicted load was equal to the measured load.

Loads model errors caused by time skew in the load prediction were removed from this plot. The overall

nonlinear trend in the measured leading-edge flap hinge moments seen in this plot was only

approximated by the prediction. Sources of nonlinearity include control surface free play, control surface

flexibility, Mach effects, and partial flow separation. Table 5 lists the model prediction errors for the left

and right leading-edge flap hinge moments during maximum-stick rolls at all of the AAW flight

conditions. Errors that are greater than 20 percent are shaded in the table to indicate poor load

predictions. Figure 9 shows the TEF and aileron hinge moments during the same maneuver as that

presented in figure 7. This figure shows good load predictions by the loads model. The difference in

accuracy for leading-edge load predictions and trailing-edge load predictions is partially caused by the

size of the phase 1 input maneuvers used to derive the loads model. Small inputs were used for the

leading-edge flap excitation maneuvers, whereas large inputs were used for the TEF and aileron

excitation maneuvers. The limited input size of the leading-edge flap maneuvers caused degradation in

leading-edge flap hinge moment prediction accuracy. Fortunately, this degradation usually did not affect

the control law design, because the leading-edge flap positions often were limited by other constraints.
13

Figure 7. Leading-edge flap hinge moments during a full-stick roll at Mach 1.1, 20,000-ft altitude (flight

condition 13).

Figure 8. Scatter plot of predicted outboard leading-edge flap hinge moment (OLEF HM) as a function of

measured OLEF HM during roll maneuvers at Mach 1.1, 20,000-ft altitude (flight condition 13).

Measured

Predicted

050288

OLEFL HM OLEF

R HM ILEFL HM ILEF

R HM

Measured OLEF HM

Predicted OLEF HM

0

0

050289

Left full-stick roll

Left 3/4-stick roll

Left 1/2-stick roll

Right 3/4-stick roll

Right full-stick roll

Trim
14

Table 5. Model prediction errors for the leading-edge-flap hinge moments during

maximum-stick rolls at all flight conditions.

Mach 

number Altitude, ft ILEFR HM ILEFL HM OLEFR HM OLEFL HM

0.85 15,000 5.7 2.8 9.0 11.7

0.90 15,000 4.1 5.7 15.3 16.5

0.95 15,000 6.4 2.5 11.7 27.6

0.85 10,000 4.0 6.0 10.1 1.9

0.90 10,000 10.1 5.5 19.4 10.1

0.95 10,000 1.7 1.3 4.4 29.0

0.85 5,000 6.7 6.3 3.2 4.0

0.90 5,000 12.0 10.6 16.6 14.5

0.95 5,000 7.4 3.5 26.7 17.1

1.10 25,000 4.5 8.6 2.6 6.2

1.20 25,000 7.3 7.7 18.5 5.4

1.30 25,000 3.9 8.3 8.2 11.4

1.10 20,000 6.4 7.5 18.8 20.2

1.20 20,000 8.0 12.7 12.8 10.1

1.30 20,000 11.7 27.7 7.0 14.0

1.10 15,000 10.4 14.4 10.6 2.4

1.20 15,000 12.7 22.5 21.5 13.4

1.10 10,000 8.8 29.6 11.0 16.7
15

Figure 9. Trailing-edge flap and aileron hinge moments during a full-stick roll at Mach 1.1, 20,000-ft

altitude (flight condition 13).

Windup-turn maneuvers flown to 5-g normal acceleration also were used in the phase 2 flight test for

envelope expansion, aerodynamic model validation, and loads model validation. These maneuvers

caused much higher wing-root and wing-fold bending moment loads than the roll maneuvers. Figure 10

shows typical time history plots of the WRBM, WFBM, WRTQ, and WFTQ loads of the left wing.

Bending moment predictions provided by the loads model have lower model prediction errors than the

torque predictions. This decrease likely is a result of the smaller magnitude of the torque response.

The small jump in the measured WFTQ that occurs 4 seconds into the maneuver is caused by free play

in the OLEF.

050290

AilL HM Ail

R HM TEFL HM TEF

R HM

Measured

Predicted
16

Figure 10. Typical bending and torque loads during a 5-g windup turn.

Tables 6 and 7 present the overall performance of the loads model. These tables show the model

prediction errors, calculated from equation (5), for various maneuvers. The maximum roll maneuver

shown in these tables is the largest maneuver from the set of roll buildup maneuvers flown in phase 2.

The maximum roll maneuver often was a full-stick roll, but the roll buildup occasionally was halted at a

lower stick deflection for safety reasons. The roll errors indicate the performance of the loads model

during its intended use as a control law design tool for a roll control experiment. These errors are

generally low, indicating a good match between flight-measured load and model-predicted load. The

highest error occurs in the prediction of the OLEF HM. These loads were consistently over-predicted by

the loads model, as described previously. The average load prediction error across all flight conditions for

the maximum roll maneuver is 9.1 percent. WFTQ WRTQ WFBM WRBM

Measured

Predicted

0

050291

2 4 6

Time, s

8 10 12
17

The loads model prediction of WUT loads generally was good. The lowest error for these maneuvers

is 1.4 percent for the WRBM. The highest error is 11.8 percent for the OLEF HM. The loads during

5-g WUTs generally were the easiest to model. The average load prediction error across all flight

conditions for the WUTs is 4.4 percent.

The RPO maneuvers were used to test the combination of longitudinal and lateral maneuvering on the

aircraft. A single RPO maneuver consists of a 4-g WUT combined with roll input. Loads resulting from

Table 6. Average subsonic model prediction

root-mean-square errors.

Load Maximum rolls, 

percent

5-gWUT, 

percent

4-g RPO, 

percent

WRBM 10.1 2.8 4.6

WRTQ 5.3 4.4 7.8

WFBM 6.1 2.6 5.9

WFTQ 9.9 6.7 15.1

ILEF HM 5.5 4.6 7.9

OLEF HM 15.7 11.8 14.4

TEF HM 6.1 3.8 7.4

Ail HM 10.6 4.9 12.0

Mean 8.7 5.2 9.4

Table 7. Average supersonic model prediction

root-mean-square errors.

Load Maximum rolls, 

percent

5-gWUT, 

percent

4-g RPO, 

percent

WRBM 13.9 1.4 4.9

WRTQ 5.9 3.0 3.7

WFBM 7.5 1.8 4.4

WFTQ 5.2 3.6 6.3

ILEF HM 12.3 2.5 4.7

OLEF HM 13.0 8.7 11.5

TEF HM 7.2 3.8 5.1

Ail HM 10.1 4.2 7.4

Mean 9.4 3.6 6.0
18

RPO maneuvers typically were the most difficult to predict. The RPOs were flown at every flight

condition except Mach 1.1 and an altitude of 10,000 ft. The average load prediction error for all RPO

maneuvers is 7.7 percent. Load prediction errors during these maneuvers may result from the lack of

superposition in the aircraft load response. The loads model assumes that superposition is valid for this

application. In reality, combined longitudinal and lateral maneuvering may cause an aircraft loading that

is different from a simple sum of longitudinal and lateral inputs. Average supersonic load predictions are

consistently better for all maneuvers. This trend is consistent with model development and analysis with

phase 1 flight data. This trend may be caused by the reduction of transonic shock-effects that occur at

subsonic flight conditions.

SUMMARY OF RESULTS

A loads model for the design and analysis of new control laws for the Active Aeroelastic Wing

airplane was successfully completed using multiple linear regression of flight data. Analysis of the loads

model with flight data through the use of the new control laws provided the following results:

1. The use of surface doublet maneuvers, rolls, 5-g windup turns, and 4-g rolling pullouts worked

effectively to create a loads model for the Active Aeroelastic Wing control law design.

2. The use of separate maneuvers to validate the loads model was useful during loads model

development to ensure that potential problems were identified.

3. The load prediction errors for phase 1 maneuvers ranged from 2 to 20 percent and typically were

slightly better for derivation maneuvers than for validation maneuvers.

4. Insufficient excitation of the leading-edge flap hinge moments and nonlinear load responses in the

model development data caused degradation of loads model prediction accuracy for those loads.

5. The loads model generally predicted loads more accurately during 5-g windup turns than during

rolls or rolling pullouts.

6. The loads model generally predicted loads from rolling pullout maneuvers less accurately than

loads from other maneuvers.

7. The loads model accurately predicted loads caused by roll maneuvers, with the exception of the

leading-edge flap hinge moments at some flight conditions.

8. The average load prediction errors for all loads at all flight conditions were 9.1 percent for

maximum stick-deflection rolls, 4.4 percent for 5-g windup turns, and 7.7 percent for 4-g rolling

pullouts.

9. Overall, good load prediction was obtained for subsonic and supersonic wing-root and wing-fold

bending moments and torque loads, in addition to trailing-edge control surface hinge moments.

Dryden Flight Research Center

National Aeronautics and Space Administration

Edwards, California, June 22, 2005
19

REFERENCES

1. Pendleton, Ed, Denis Bessette, Pete Field, Gerry Miller, and Kenneth Griffin, “The Active

Aeroelastic Wing Flight Research Program Technical Program & Model Analytical Development,”

AIAA-98-1972, 1998.

2. Dibley, Ryan P., Michael J. Allen, Robert Clarke, Joseph Gera, and John Hodgkinson,

“Development and Testing of Control Laws for the Active Aeroelastic Wing Program,”

AIAA-2005-6314, 2005.

3. Olney, Candida D., Heather Hillebrandt, and Eric Y. Reichenbach, An Evaluation Technique for an

F/A-18 Aircraft Loads Model Using F/A-18 Systems Research Aircraft Flight Data,

NASA /TM-2000-209028, 2000.

4. Lewis, George E., “AFTI/F-111 Mission Adaptive Wing Maneuver Load Control,” available from

the NASA Center for AeroSpace Information (CASI), CASI document identification 19910074478.

5. Haas, David J., Lance Flitter, and Joel Milano, “Helicopter Flight Data Feature Extraction or

Component Load Monitoring,” Journal of Aircraft, Vol. 33, No. 1, Jan.–Feb. 1996.

6. Haas, David J., and Robin Imber, “Identification of Helicopter Component Loads Using Multiple

Regression,” Journal of Aircraft, Vol. 31, No. 4, July–Aug. 1994.

7. Haas, David J., Joel Milano, and Lance Flitter, “Prediction of Helicopter Component Loads Using

Neural Networks,” Journal of the American Helicopter Society, Vol. 40, No. 1, Jan. 1995.

8. Allen, Michael J., and Ryan P. Dibley, Modeling Aircraft Wing Loads from Flight Data Using

Neural Networks, NASA/TM-2003-212032, 2003.

9. Lokos, William A., Candida D. Olney, Tony Chen, Natalie D. Crawford, Rick Stauf, and Eric Y.

Reichenbach, “Strain-Gage Loads Calibration Testing of the Active Aeroelastic Wing F/A-18

Airplane,” AIAA-2002-2926, 2002.

10. Lokos, William A., and Rick Stauf, Strain-Gage Loads Calibration Parametric Study,

NASA/TM-2004-212853, 2004.

11. Chapra, Stephen C., and Raymond P. Canale, Numerical Methods for Engineers, 2nd ed.,

McGraw-Hill, Inc., New York, St. Louis, San Francisco, 1988.
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

1. REPORT DATE (DD-MM-YYYY)

15-11-2005

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Loads Model Development and Analysis for the F/A-18 Active Aeroelastic 

Wing Airplane

5a. CONTRACT NUMBER

 6. AUTHOR(S)

Michael J. Allen, Andrew M. Lizotte, Ryan P. Dibley, and Robert Clarke

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Dryden Flight Research Center

P.O. Box 273

Edwards, California 93523-0273

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

H-2610

10. SPONSORING/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

Also presented at the AIAA Atmospheric Flight Mechanics Conference and Exhibit, San Francisco, CA, August 15–18, 2005. 

An electronic version can be found at the NASA Dryden Flight Research Center Web site, under Technical Reports.

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified -- Unlimited

Subject Category 01 Availability: NASA CASI (301) 621-0390 Distribution: Standard

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

The Active Aeroelastic Wing airplane was successfully flight-tested in March 2005. During phase 1 of the two-phase program, an onboard 

excitation system provided independent control surface movements that were used to develop a loads model for the wing structure and wing control 

surfaces. The resulting loads model, which was used to develop the control laws for phase 2, is described. The loads model was developed from 

flight data through the use of a multiple linear regression technique. The loads model input consisted of aircraft states and control surface positions, 

in addition to nonlinear inputs that were calculated from flight-measured parameters. The loads model output for each wing consisted of wing-root 

bending moment and torque, wing-fold bending moment and torque, inboard and outboard leading-edge flap hinge moment, trailing-edge flap hinge 

moment, and aileron hinge moment. The development of the Active Aeroelastic Wing loads model is described, and the ability of the model to 

predict loads during phase 2 research maneuvers is demonstrated. Results show a good match to phase 2 flight data for all loads except inboard and 

outboard leading-edge flap hinge moments at certain flight conditions. The average load prediction errors for all loads at all flight conditions are 9.1 

percent for maximum stick-deflection rolls, 4.4 percent for 5-g windup turns, and 7.7 percent for 4-g rolling pullouts.

15. SUBJECT TERMS

Aeroelastic, Aircraft loads, Linear regression, Modeling, Simulation

18. NUMBER

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24

19b. TELEPHONE NUMBER (Include area code)

(301) 621-0390

a. REPORT

U

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b. ABSTRACT

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 ABSTRACT

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Prescribed by ANSI Std. Z39-18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

723 56 00 SE PR 00 000

11. SPONSORING/MONITORING

 REPORT NUMBER

NASA/TM-2005-213663

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