Usv FTB-1 Reusable Vehicle Aerodatabase Development

MATHEMATICAL MODELING, COMPUTATION AND EXPERIMENTATION FOR MULTIPHYSICS

AEROSPACE AND ENVIRONMENTAL ENGINEERING PROBLEMS

J. Periaux, B. Chetverushkin, G. Bugeda and T. Kozubskaya Editors (Eds.)

© CIMNE, Barcelona, Spain 2007

USV FTB-1 REUSABLE VEHICLE AERODATABASE

DEVELOPMENT

Giuseppe C. Rufolo Pietro Roncioni and Marco Marini*

CIRA CIRA

Italian Aerospace Research Centre Italian Aerospace Research Centre

Via Maiorise Via Maiorise

81043 Capua (CE), Italy 81043 Capua (CE), Italy

Email: [email protected] Email: [email protected], [email protected]

web page: http://www.cira.it web page: http://www.cira.it

Abstract. The paper describes the methodology of integration of the different

sources of data adopted for the development of the aerodynamic database of the Italian

Unmanned Space Vehicle FTB-1, which is a multi-mission, reusable vehicle designed

and built by CIRA in the framework of the Italian National Aerospace Research

Program. The first mission is aimed at experimenting the transonic flight of a re-entry

vehicle carried in altitude by a stratospheric balloon, and properly released in such a

way to fly in transonic conditions. Other missions are scheduled with the aim at

extending the flight envelope up to the supersonic regime. Main data sources have been

wind tunnel tests executed at CIRA-PT1 and DNW-TWG facilities, and CFD

simulations performed at CIRA. A comparison of numerical, experimental and

extrapolated-to-flight values obtained through the Aerodynamic Prediction Model has

been performed, and a structured model of uncertainties for the aerodynamic

coefficients and derivatives has been developed.

Key words: Unmanned Space Vehicle, Aerodatabase, Aerodynamic Prediction Model

1 INTRODUCTION

A methodology of integration of different sources of aerodynamic data adopted for

the development of an Aero-Data-Base (ADB) is described hereinafter in this work. The

present application is the Italian Unmanned Space Vehicle (USV) FTB-1, see Fig. 1,

which is a multi-mission, reusable vehicle developed and built by CIRA1 in the

framework of the PRO.R.A., i.e. the National Aerospace Research Program.

The first USV FTB-1 mission, the Dropped Transonic Flight Test (DTFT-1), is

aimed at experimenting the transonic flight of a re-entry vehicle carried in altitude by a

stratospheric balloon. In addition, other three missions are scheduled in the next years

with the goal to extend the flight envelope up to a fully supersonic regime.

Giuseppe C. Rufolo, Pietro Roncioni and Marco Marini

Figure 1. The USV FTB-1 vehicle.

It has been necessary to develop a suitable Aerodynamic Prediction Model (APM) in

order to properly describe the vehicle’s aerodynamic behaviour, following the build-up

approach2 for the description of the global aerodynamic coefficients. Main data sources

have been wind tunnel tests on a 1:30 scaled model, performed at CIRA-PT13 and

DNW-TWG4,5

facilities, CFD simulations performed by CIRA6-8

to fill gaps of the

experimental tests (effects of Reynolds number, base flow, sting interference), and

engineering approximate methods to quickly analyze the effects of some configuration

changes on the global vehicle’s aerodynamics9 and to properly evaluate the dynamic

derivatives10, 11

.

As a final result, an Aero-Data-Base covering the whole range of Mach number (M),

Reynolds number (Re), angle of attack (α), angle of sideslip (β) and control surfaces

deflections (δE, δR, respectively for elevon and rudder) has been developed and

released12,13

, together with a structured model of uncertainties14

for the aerodynamic

coefficients, this latter in order to

perform Monte Carlo mission

simulation studies.

Before to go to the following

sections, where all the aspects

introduced above are described in

detail, it is important to highlight

some other major outcomes

deriving from the experience of

the USV Program development.

The USV FTB-1 project has

exploited all the multi-physics

know-how available at CIRA in

the different disciplines of

aerodynamics, flight mechanics,

GNC, structures, etc., with their

own proper interfaces, thus

implementing an iterative design

loop (see Fig. 2). The USV

FTB-1 project has needed a long

design effort and has been a good

example of collaborative team

work. The Aero-Data-Base

development methodology here

described has been a typical

result of shared knowledge:

Mach

Qu

ota

MACH

0.9÷1.4

18÷24 Km

CFD EFD

ADBADB

ENG

Flight Mechanics, GNCFlight Mechanics, GNC

StructuresStructures

Flyability ?

Controllability ?

Flyability ?

Controllability ?

Structural verification ?

The Vehicle

The Mission

The Flight

Y

Y

N

N

Figure 2. The USV FTB-1 iterative design process.


computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) have

worked together, also helped by engineering tools based on approximate methods, in

order to prepare inputs for Flight Mechanics, Guidance, Navigation and Control and

Structures.

2 THE USV FTB-1 VEHICLE

The USV FTB-1 vehicle can be classified as a winged body, see Fig. 3. The main

body of FTB-1 has an overall length of 8000 mm, from the nose apex (without

considering the air data boom) up to the base plate. The front fuselage ends with a

pointed nose constituted by a quasi-conical shape closed by a 1-cm radius hemisphere.

Downstream of the pointed nose, the windside part of the forebody geometry rapidly

changes from a quasi-circular to a rounded-square shape. The mid-fuselage is

characterized by a quasi-constant section while the afterbody ends with a boat-tailed

truncated base. The wing of the FTB-1 vehicle has a double delta shape with a main 45

deg sweepback leading edge and a strake with a 76 deg sweepback leading edge. The

trailing edge has a sweepforward angle of 6 deg. To improve lateral stability, the FTB-1

wing has a dihedral angle of 5 deg with referring to the wing reference plane. Overall

wing span is 3562 mm, while the strake root chord is 2820 mm. An elevon with both

functions of elevator and aileron is mounted on the FTB-1 wing, whose hinge line has

no sweep angle while its span is 1094 mm.

For directional stability and control a V-Tail solution has been adopted: the two

vertical tails have a dihedral angle of 40 deg, a sweepback angle of 45 deg and a span of

800 mm. The chord at root station is 1000mm while at the tip it reduces to 500mm,

while the airfoil section is symmetric with a 12% mean thickness. A pair of full-span

movable rudders is also implemented for directional control, which could be also used

as ruddervators in order to improve longitudinal control capabilities and as an energy

management device (speedbrake).

Moreover, in order to augment directional stability characteristics of the vehicle and

to reduce possibilities of Dutch-roll occurrence, appeared in a later phase of the design

process, a pair of full symmetric ventral fins has been also added. Ventral fins, without

movable surfaces, are characterized by a 55 deg sweepback angle, a root chord of

800mm with a taper ratio of 0.455, and a span of 418 mm. Their design has been

conceived in order to have the larger effectiveness with the lower impact on the already

designed structure.

3 THE DTFT-1 MISSION DESCRIPTION

The first USV FTB-1 mission (DTFT-1, Dropped Transonic Flight Test-1) is aimed

at experimenting the transonic flight of a re-entry vehicle. Moreover, the USV FTB-1

will perform additional flights, each of them simulating the final portion of a typical re-

entry trajectory. The USV FTB-1 vehicle is basically composed by a Flying Test Bed

(FTB-1) and a Carrier based on a stratospheric balloon.1 During the missions the

balloon carries the FTB-1 up to the desired altitude (around 20 km for the first mission)

and then, after having established a cruise horizontal trajectory, releases it from the

gondola. At this moment the FTB-1 vehicle starts its own flight following the designed

trajectory.


Figure 3. The USV FTB-1 three views.

In the frame of a step-by-step approach, the FTB-1 will reach during each subsequent

mission an increasing Mach number, starting from Mach 1 during the first mission up to

Mach 2 during the last scheduled flight. During the flight it performs the experiments

(Aerodynamics, Structure and Materials, Autonomous Guidance Navigation and

Control), and by means of a pull-up manoeuvre it decelerates in order to enter in the

safe parachute opening regime. The final recovery of the vehicle is performed from the

sea by a ship of the Italian Navy.

An example of Mach-altitude trajectory for a typical dropped controlled mission is

shown in Fig. 4, where it is possible to identify the following four characteristic phases.


Mach

Qu

ota

A

B

C

D

MACH

AL

TIT

UD

E

Figure 4. The USV DTFT-like mission.

A. DROP: low Mach Number (< 0.5), low dynamic pressure and low Reynolds

(<400000). The presence of large regions of laminar flow makes difficult the

aerodynamic prediction. No aerodynamic control due to the low dynamic pressure. This

portion is of no interest in the frame of experimentation aimed at the orbital re-entry.

B. ACCELERATION: increasing Mach Number (0.7<M<Mmax) and increasing

dynamic pressure, thus increasing effectiveness of aerodynamic control. Reynolds

number range comparable with the one of the CIRA-PT1 wind tunnel. No need for

extrapolation of measurements to flight Reynolds number.

C. MANOEUVRING REGION at M≈Mmax: corresponding to the maximum Mach

number along the trajectory when the acceleration reaches its minimum and with a

suitable pull-up or push-over manoeuvre it is possible to acquire data at a constant

Mach number during a α−sweep. The operative Reynolds number is close to the

maximum along the trajectory, and it depends mainly upon the drop altitude.

D. DECELERATION: decreasing Mach number from Mmax to 0.7. High Reynolds

number. CFD/Flight comparison and validation of extrapolation-to-flight procedure.

4 THE AERODYNAMIC PREDICTION MODEL

In order to properly describe the aerodynamic characteristics of the USV FTB-1

vehicle it has been necessary to develop a suitable Aerodynamic Prediction Model

(APM), i.e. a mathematical representation of the physics of the problem. The first step

for the definition of the APM is the identification of the quantities needed in output, i.e.

the quantities required by the disciplines that use aerodynamic inputs: Flight Mechanics,

Guidance, Navigation and Control, and Structures.

In the case of a winged body as the USV FTB-1 vehicle, the properties to be

characterized are the six global aerodynamic coefficients (CL, CD, CY : lift, drag and side

forces; Cl, Cm, Cn : rolling, pitching and yawing moments), the hinge moments (CiH) of

the control devices (elevons, rudders) and the surface pressure distribution (pw)12,13

:

{CL, CD, CY, Cl, Cm, Cn, CiH, pw} (1)


An analysis of the phenomenology to be characterized has allowed for a meaningful

selection of the set of parameters which APM outputs depend upon12,13

:

{ }αδδδβα &,,,,,,,,Re,, rqpMrlr

EE (2)

These independent variables have been recognized as influent on the aerodynamic

state of the USV FTB-1 vehicle. Starting from the knowledge of the real

phenomenology and basing on previous experience it has been possible to define the

functional structure of the APM: for each of the six aerodynamic coefficients (1) a

dependence from a suitable subset of independent variables (2) has been assumed.

A classical build-up approach is used for the description of the global aerodynamic

coefficients, as done in the past for NASA X-34 and X-33 experimental vehicles2,

where each coefficient is expressed as a linear summation over a certain number of

contributions, each of them dependent by a small number of parameters.

Generally speaking, each aerodynamic coefficient Ci has the following structure:

( )rqpa

R

l

E

r

E

re

rqpM

iiiiiii

BL

i

i

CCCCCCCC

,,,,,,,,Re,,C

∆+∆+∆+∆+∆+∆+∆+=

=&

&

δδβ

αδδδβα (3)

The term ( )αRe,,MC BL

irepresents the baseline contribution to the global coefficient

iC at zero-sideslip in clean configuration (with no-deflection of control surfaces) and

with no dynamic effects. The term βiC∆ is the incremental coefficient due to the sideslip

angle (β) in clean configuration, and it has to be intended as:

( ) ( )αβαβ Re,,,Re,,Ci MCMC ii −=∆ (4)

It is assumed that if a parameter is not considered as an independent variable its

value is zero.

Terms EδiC∆ and Rδ

iC∆ represent, respectively, the incremental effect of an elevon

deflection at zero sideslip, and the incremental effect of rudders deflection:

( ) ( )αδδαδRe,,,,Re,,Ci MCMC i

r

E

l

EiE −=∆ (5)

( ) ( )βαδβαδ,Re,,,,Re,,Ci MCMC iRi

R −=∆ (6)

By concluding, the term α&iC∆ represents the effect of the time derivative of the angle

of attack, and p

iC∆ , q

iC∆ , r

iC∆ the effects of the vehicle angular velocities.

Basing on a deep analysis of available (mainly wind tunnel) data some simplifying

hypotheses have been made for the functional dependence of each aerodynamic

coefficient (1)12,13

, the goal being to identify the independent variable (2) which mostly

affects the single aerodynamic coefficient Ci.

Once the APM is defined, it is necessary to gather a sufficient amount of data in

order to explicit the functional dependencies of each piece of the model. Data sources

considered are: wind tunnel, CFD and simplified engineering (approximate) methods. In

particular, for the development of the USV FTB-1 Aero-Data-Base the primary source

of data has been represented by the test campaigns carried out within the CIRA PT-1

wind tunnel3 and the Transonic Wind-tunnel Göttingen (TWG) of DNW consortium

4,5.

CFD data, obtained by using the CIRA code ZEN15

, have been primarily used to cross-

check wind tunnel data and to fill gaps of the measurements, e.g. the effects of base


drag, Reynolds number and model support system interference. In addition, simplified

numerical methods like Eulerian CFD, Vortex Lattice Method, Panel Method and

DATCOM have been used to fill gaps in wind tunnel data (M<0.5), to provide a rapid

estimation of configuration changes occurring during the design process, and mainly to

provide dynamic stability derivatives6, 9-11

. It has been shown that Eulerian CFD is quite

useful to provide a suitable preliminary estimation of aerodynamic coefficients in

transonics, where approximate methods

(VLM, PM, DATCOM) begin to fail6.

By following the logical process

reported in Fig. 5, test campaigns have

been designed taking into account the

hypotheses made in the APM in order to

optimize test matrices. A limited amount

of tests is anyhow devoted to the

verification of such hypotheses.

The analysis of the gathered data has

allowed for the determination of the

functional dependencies of the APM by

means of polynomial expressions whose unknown coefficients have been determined by

means of best-fitting algorithms. The approach followed in the present case is based on

the identification of a primary variable that drives the phenomenon, e.g. α for BL

iC . The

polynomial expression is written with respect to this variable, while secondary

dependencies are introduced directly into the polynomial coefficients. Still with

referring to the baseline contribution it can be written:

( )∑=

−⋅=n

i

i

i

BL

i MaMC1

1Re,)Re,,( αα (7)

where ia are the coefficients of the polynomial expression in α , and are function of

Mach and Reynolds numbers. As a general rule, a complete set of coefficients ia will be

given for a suitable and minimal subset of secondary variables combination. Proper

interpolation has to be adopted in order to obtain coefficients for a combination of

secondary parameters not included in the minimal set.

5 DATA SOURCES ANALYSIS

5.1 Wind Tunnel Data

Main data sources for USV FTB-1 Aero-Data-Base are wind tunnel data gathered

within the CIRA PT-1 facility3 and the Transonic Wind-tunnel Göttingen (TWG)

4,5

operated by the German-Dutch wind

tunnel foundation (DNW). Figure 6

shows the 1:30 scaled model (26cm of

overall length) with modular

characteristics (fuselage, wings, tails,

ventral fins) used for both test

campaigns. The control surfaces (elevons

and rudders) are removable, and a set of

different parts is available to simulate

different control surface deflections. The

Figure 5. The APM graphical representation.

Figure 6. The USV FTB-1 scaled model.


aerodynamic forces and moments have been measured by internal strain gauges

balances, while thirty-three pressure taps are distributed on the model surface. The

model has been tested in transition-fixed mode by applying a 0.15mm Carborundum

grit, whose effectiveness has been verified by means of dedicated tests. The details of

both test campaigns are reported in the related test reports3-5

, however it must be

stressed that in the CIRA PT-1 facility (see Fig. 7) was reproduced the Reynolds

number of the upper part of the flight trajectory, and a suitable combination of the

parameters reported in Table 1 has been chosen with the aim at minimizing the number

of experimental tests.

A large amount of experimental data was

acquired in the DNW-TWG facility

between the end of 2005 and the

beginning of 20064,5

. Some transonic

tests have been duplicated in such a way to have a bridge (and a cross-check) with the

tests executed at CIRA, while also tests in supersonic regime (M=1.5, 1.8, 2.0) have

been performed. The DNW-TWG test

matrix is reported in Table 2, while some

oil flow visualizations and Schlieren

images are reported in Fig. 8 and Fig. 9.

Figure 7. CIRA PT-1 facility test chamber.

Mach 0.3; 0.5; 0.7; 0.8; 0.85; 0.9; 0.95; 0.97; 0.99; 1.02; 1.05; 1.13; 1.4

α (°) -5; 0; 5; 7.5; 10; 12.5; 15; 17.5

β (°) -8; -4; 0

-30/-30; '-25/-25; -20/-20; -10/-10; 0/0; 10/10; 20/20; 25/25; 30/30

-25/0; -10/0; 10/0; 25/0

δRr/δR

l-25/-25; -10/-10; 10/10; 25/25

δEr/δE

l

Table 1. CIRA PT-1 Test Matrix.

Mach 0.7; 0.85; 0.94; 0.99; 1.02; 1.05; 1.13; 1.2; 1.52; 1.79; 2.0

α (°) sweep [-5;20]; fixed (-5, 0, 5, 12.5, 17.5)

β (°) sweep [-8;8]; fixed (-8; -4; 0)

-30/-30; '-25/-25; -20/-20; -10/-10; 0/0; 10/10; 20/20;

-25/0; -10/0; 10/0; 20/0

10/10; 20/20; 25/25

25/10, 25/0,25/-10, 25/-25, 10/25, 10/0,10/-10, 10/-25; 0/10, 0/25

δEr/δE

l

δRr/δR

l

Table 2. DNW TWG Test Matrix.

Figure 8. Oil flow visualization

(M=1.05, Re=0.63·106, α=10.55 deg,

β=0 deg).

Figure 9. Schlieren images at M=1.2 (top:

α=10 deg, β=0 deg; bottom: α=10 deg, β=8 deg).


5.2 CFD Data

A large number of CFD simulations has been performed in order to support the

extrapolation to flight of wind tunnel measurements, and to fill gaps in experimental

data which have been corrected for the effects of Reynolds number, base flow (not

accounted for in wind tunnel tests) and model support system interference.

The baseline test matrix of viscous simulations is reported in Table 3.

In the table VF stands for Ventral Fins (see Fig. 3), whose effect in the sub-transonic

range was preliminarily evaluated by means of approximate methods and Eulerian CFD

computations6, whereas WT (=Wind Tunnel), INT (=Intermediate) and FL (=Flight)

represent, respectively, the three Reynolds number values selected to derive proper

scaling laws for studying viscous effects (logarithmic average), i.e.

( ) ( ) ( )[ ]FLWTINT LogLogLog ReRe2

1Re += →

FLWTINT ReReRe ⋅= ≈2.100.000 (8)

being ReWT=4·105 the Reynolds number reached in DNW-TWG at M=1.5, and

ReFL=11·106

the maximum value high enough to cover a supersonic drop test envelope.

The numerical code used to carry out the present aerodynamic analysis of USV FTB-

1 vehicle is the CIRA code ZEN15

that solves the Reynolds Averaged Navier-Stokes

equations in a density-based approach with a Jameson-like numerical scheme for the

convective terms, and central differencing for viscous terms. The code solves the

discrete governing equations in a finite volume approach with a centred formulation, on

a multi-zone block-structured grids. The two-equation k-ε Myong-Kasagi turbulence

model is used with particular damping functions to simulate the flow behaviour in the

viscous region of a turbulent boundary layer. The two-equation k-ω TNT turbulence

modelling due to Kok is also used, not presenting any dependence of eddy viscosity

calculation on the distance from the wall (this fact is particularly advantageous in the

presence of corner flows). The code, implemented with a vectorial technology, has been

run on the CIRA Super Computer NEC SX-6 (8 CPUs, 64 GB DDR SDRAM, 64

GFLOPS of combined peak performance) and the CIRA Super Computer NEC TX-7

(scalar-parallel supercomputer with 20 1500MHz processors Itanium2, 40 GB of central

memory and a total peak power of 120 GFLOPS).

Several computational grids for the different flow regimes have been generated by

using the commercial code ICEMCFD-HEXA (see Fig. 10), building them by using

four main O-grids for half vehicle configuration (around fuselage and wing, V-tail, V-

fin and base) and local block decomposition. The external boundaries have been

δΕ δR Configuration Mach Re αααα ββββ 0 0/0 No-Sting 0.70, 1.05 FL -5, 0, 10, 20 0

0 0/0 No-Sting 0.70, 1.05 WT 0, 10 0

0 0/0 No-Sting 0.95 WT, FL -5, 0, 10, 20 0

0 0/0 No-Sting 1.05 INT 10 0

0 0/0 Sting 0.70, 1.05 WT 0, 10 0

10 0/0 Sting 0.70, 1.05 FL, WT 0, 10 0

20 0/0 Sting 0.70, 1.05 FL, WT 0, 10 0

0 0/0 No-Sting 0.70, 1.05 FL, WT 0, 10 8

0 0/0 No-Sting, VF 1.05 FL 0, 10 0

0 0/0 No-Sting, VF 1.05 WT,INT,FL 0 8

0 0/0 No-Sting, VF 1.40 WT,INT,FL 0, 10 0


0 0/0 No-Sting, VF 2.00 WT,INT,FL 0, 10 0


Table 3. Baseline CFD Test Matrix.


adapted to the flow regime, and the full vehicle grid (to study sideslip effects) has been

obtained by simply mirroring with respect the centreplane. Figure 11 shows a typical

flow field computed around the FTB-1 vehicle in subsonic regime, in terms of

streamtubes and iso-contours of total pressure.

Regarding the effects of grid resolution, a grid sensitivity analysis has been

performed for one sample case (M=1.4, α=10 deg, β=0 deg) by employing three levels

of structured multi-block grid7. Results have indicated either that the CFD solution lays

in the asymptotic range of spatial convergence, either that only small differences arise

between fine and medium grid level results, thus being close to grid-convergence of

computed results. This has been also demonstrated by comparing the finest grid result

with the one extrapolated at zero-spacing grid by means of the Richardson extrapolation

method7.

As a time-convergence criterion7, the achievement of steady state values of global

aerodynamic coefficients has been assumed, and average values (over a proper number

of cycles) have been calculated in presence of oscillations caused mainly by naturally

base flow unsteadiness.

Some CFD results7,8

in terms of lift (CL) and pitching moment (Cm) coefficients and

drag (CD) coefficient are reported, respectively in Fig. 12 and Fig. 13, in function of

Reynolds number. It must be concluded that for M=1.05 and M=1.4 an asymptotic

behaviour with respect to Reynolds number has been predicted for CL and Cm at both

Figure 11. M=0.7, Re=6.5·106, α=10 deg, β=0 deg

(streamtubes and total pressure contours) .

AoA=0

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600

100000 1000000 10000000 100000000

Reynolds

CL

-0.0500

-0.0400

-0.0300

-0.0200

-0.0100

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

Cm

y

CL , Beta=0, M=1.40

CL, Beta=-8, M=1.40

CL, Beta=-8, M=1.05

CMy Beta=0, M=1.40

CMy Beta=-8, M=1.40

CMy Beta=-8, M=1.05

Figure 12. Lift and pitching moment coefficients

(α=0 deg).

0.1000

0.1200

0.1400

0.1600

0.1800

0.2000

0.2200

0.2400

0.2600

0.2800

0.3000

100000 1000000 10000000 100000000

Reynolds

CD

AoA=0, Beta=0, M=1.40

AoA=10, Beta=0, M=1.40

AoA=0, Beta=-8, M=1.40

AoA=0, Beta=-8, M=1.05

Figure 13. Drag coefficient, Reynolds number

effects (α=0 deg).

Figure 10. Half vehicle grid (3.1·106 cells).


angles of attack (0 and 10 deg), with a major effect at α=10 deg. Regarding CD a not

monotonic trend in the Reynolds number dependence has been predicted: the analysis of

the drag provided by the different parts of the vehicle7,8

(lumped coefficients) at M=1.4

has shown a clear increasing trend of the base drag with Reynolds number. Being

friction drag due to the base negligible, base drag is only caused by the pressure

distribution establishing over it, and for which the Reynolds number variation has an

opposite effect with respect to friction drag, this being caused by the increase of the

expansion due to the reduction of boundary layer thickness. This is also confirmed by

the total pressure drag whose trend is similar to the one of the base pressure drag7,8

,

while regarding friction drag, a Blasius-like dependence upon Reynolds number has

been also predicted7,8

. The lateral-directional aerodynamic analysis7,8

, performed at all

Mach numbers for β=0, 8 deg, has shown that increasing Reynolds number enhances

both the lateral stability and the directional stability.

6 EXTRAPOLATION TO FLIGHT

The data collected during the experimental test campaigns do not allow for a

complete characterization of the FTB-1 aerodynamics with respect to all the interesting

parameters: a typical example is the effect of Reynolds number. Therefore,

experimental data (the base of the ADB) must be properly corrected by means of scaling

laws describing Reynolds number effects, and these laws have been obtained with the

help of CFD simulations. Moreover, the necessity to correct wind tunnel measurements

derives also by other factors such as the presence of a model support (i.e. the sting) and

the impossibility to include the base contribution in the total drag measurement.

The general expression used for the calculation of the extrapolated-to-flight

aerodynamic coefficients (left hand side terms) is the following:

{ }

press

D

fric

D

Sting

D

Base

D

WT

D

Flight

D

ipress

i

Sting

i

WT

i

Flight

i

CCCCCC

CCCC

ReRe

n m, l, Y, L, Re ,

∆+∆+∆+∆+=

∆+∆+= =

(9)

where the in-flight values correspond to the Reynolds number encountered along the

mission nominal trajectory, the first terms of the right hand side are the wind tunnel

measured coefficients and the remaining terms are the CFD-based corrections.

It must be noted that only for the drag coefficient the correction is composed, in

general, by a sum of four contributions taking into account the effect of Reynolds

number over the forebody pressure drag, the variation of friction drag with Reynolds

number and the contribution of base drag. The sting effect on the vehicle’s forebody is

obviously neglected in supersonic regime.

6.1 Reynolds number effects

To account for actual viscous effects, the general approach is to correct each global

aerodynamic coefficient with a function of Reynolds number, i.e.

( ) ( ) ),( FLWTWTiFLi ReRefReCReC += (10)

where the correction is always conceived as a difference, in such a way to be somewhat

independent from the goodness of the comparison between CFD and wind tunnel data:

( ) ( ) ( )WTFLFLWT RegRegReRef −=, (11)


A second order polynomial interpolation has been found versus the logarithm of

Reynolds number13

, i.e.

( ) ( ) ( ) cbag ++= RelogRelogRe 10

2

10 (12)

It must be remarked that these analytical laws have been introduced to evaluate the

variation between flight and wind tunnel Reynolds number conditions, to be added to

the experimental measurements, with the exception of the base drag whose contribution

to total drag has been calculated basing only on CFD simulations.

6.2 Base flow effects

Typical model installation with a rear sting does not allow to take into account the

contribution of the base to the total aerodynamic drag, since wind tunnel balances only

measure the resultant of the aerodynamic actions over the model forebody. In fact,

residual contribution deriving from differences between the asymptotic and cavity

pressure are depurated from the measured axial force. Since the existing empirical

correlations for base drag are strongly problem-dependent, CFD seems the most reliable

way of correcting wind tunnel data for the effect of base drag, although it is well known

that a lot of difficulties exist in the right prediction of large re-circulating base flow

regions. Some CFD results are shown in Fig. 14 in terms of pressure coefficient

contours and skin-friction lines on FTB-1 base surface, while Fig. 15 reports a typical

CFD-based function describing the base drag correction (∆CDBase

).

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20.01

0.02

0.03

0.04

0.05

0.06

Mach

CD

Base

AoA=0° - AoS=0° - Re=1.E6

Figure 15. The ∆CDBase

correction vs. Mach number.

Figure 14. Pressure coefficient contours and skin-friction lines on FTB-1 base surface (M=1.05;

α=0 deg ; Re=ReWT, left ; Re=ReFL, right).


6.3 Sting effects

The presence of the sting alters the flow pattern and, at subsonic speeds, this has a not

negligible effect on the global aerodynamic coefficients. Correction functions have been

found out (∆CDSting

) for longitudinal aerodynamic coefficients in subsonic regime. On

the other hand, for lateral-directional coefficients the sting effect was not clearly

achievable from CFD results, so it has been preferred to not correct them.

7 AERO-DATA-BASE SET UP

By correcting and integrating experimental data it has been possible to generate a

reliable set of data which covers to a certain extent the variations of all the identified

parameters, to be used to build the vehicle Aero-Data-Base. The analysis of the gathered

data has allowed for the determination of the functional dependencies of the APM by

means of polynomial expressions, whose unknown coefficients are determined by

means of best-fitting algorithms. For each piece of the build-up, a primary variable

which drives the phenomenon has been identified. Then, a polynomial expression has

been written in function of this variable, while secondary dependencies have been

introduced directly into the polynomial coefficients.

The general procedure to derive each contribution can be summarized as follows:

1. Suitable data subsets are extracted from the set of experimental data

(extrapolated to flight). In particular, data are chosen by selecting from the pool

of data those characterized by a fixed combination of all the independent

parameters except the driving one (see Fig. 16 as a practical example).

2. If necessary, small oscillations of Mach number are filtered out in order to have

each subset of data at the same Mach number by locally interpolating data.

3. A Chi-Square type algorithm has been adopted for the best-fitting procedure.

This kind of approach allows to take into account the standard deviation of

each single sample of the set. The generic function y(x) is a linear summation

over M basis functions Xk(x), which in our case are polynomial expressions,

and a merit function (χ2) is defined as follows:

( )∑=

=M

k

kk xXaxy1

)( ( )

2

1

12 ∑∑

=

=

−=

N

i i

M

k ikki xXay

σχ (13)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6-1

-0.5

0

0.5

1

1.5

2

M

CL

PT-1 β=0 δE=0/0 δ

R=0

αx=-5

αx=0

αx=5

αx=10

αx=15

αx=17

Figure 16. CIRA PT-1 CL vs. M at different α’s.


where σi is the uncertainty associated with the single sample. In order to find

out the ak coefficients it is necessary to minimize the 2χ function. It has been

chosen to use the Singular Value Decomposition (SVD) to find least squares

optimal solution. By using the SVD is possible to derive the covariance matrix,

and then the uncertainty associated with each coefficient of the fitting.

4. A fitting function is generated for each combination of the parameters for

which data exist. It means that the dependence of the contribution by the

secondary parameters is included within the fitting coefficients. For the

parameters combination for which fitting functions do not exist, a suitable

interpolation of the fitting coefficients is performed.

In the following, details about the polynomial expression derivation from the

gathered data are given12,13

for some examples of baseline contributions to the global

coefficient Ci at M=0.94, in the hypothesis of zero-sideslip, clean configuration (no-

deflection of control surfaces) and absence of dynamic effects. Results are reported in

Figs. 17-19, respectively in terms of lift, drag and pitching moment coefficient, together

with the polynomial expressions in function of the angle of attack α. Note that the ai

coefficients depend upon Mach and Reynolds number, and that this baseline

contributions appear only on longitudinal actions being the vehicle symmetric with

respect to the centre plane.

( )∑ =

−⋅=N

i

i

i

BL

L MaC1

1Re, α

1M if 4,N

1M if 6,N

>=

≤=

(14)

( )∑ =

−⋅=6

1

1Re,i

i

i

BL

D MaC α

(15)

-10 -5 0 5 10 15 20 25-1

-0.5

0

0.5

1

1.5

2

M=0.94 β=0 δE=0/0 δR

=0

α (°)

CL

TWG - Re=611840

TWG - Re=618154

PT1 - Re=1000000

CFD - Re=850500

ADB - Re=1000000

Figure 17. CL vs. α (M=0.94).

-10 -5 0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

M=0.94 β=0 δE=0/0 δR

=0

α (°)

CD

fore

-CD

frictio

n

TWG - Re=611840

TWG - Re=618154

PT1 - Re=868509

CFD - Re=850500

ADB - Re=1000000

Figure 18. CD vs. α (M=0.94).


In the figures a comparison between the different data sources (CIRA-PT1, DNW-

TWG, CFD) and the ADB output is made. Figure 17 shows a quasi-linear behaviour of

CL (for all Mach numbers) up to α=12 deg, and a quite good agreement between

different data sources, both experimental and numerical. Drag coefficient reported in

Fig. 18 is depurated from base and friction drag, and shows again a quite good

agreement apart from some discrepancies between CIRA-PT1 and DNW-TWG data at

M=0.94 and higher values of α. The pitching moment coefficient reported in Fig. 19 is

characterized by a more complex behaviour. The noticeable difference between CIRA-

PT1 data and DNW-TWG data basically relies on the presence of ventral fins only for

the latter data. Although the effect over the integral of pressure distribution of ventral

fin is quite small (lift and pressure drag), the effect on the shape of pressure distribution,

and consequently on the pitching moment, is quite stronger.

Similar results are available (and similar considerations can be made) for sideslip

angle effects on longitudinal and lateral-directional actions (side-force, rolling moment

and yawing moment vary roughly linearly with β), and for the effects of control surfaces

(elevon, rudder) on longitudinal and lateral-directional actions. All these effects12,13

are

described by means of polynomial expressions written in function of the primary

variable, while secondary dependencies are introduced directly into the polynomial

coefficients.

8 UNCERTAINITIES MODEL

Even though the Aerodynamic Prediction Model is aimed at being the best possible

by exploiting the available tools and know-how, it remains however a representation of

the actual phenomenology, and therefore it is characterized by errors. To assess the

APM output data it is then necessary to estimate the entity of such errors, by associating

to the nominal values provided by the APM the related uncertainty margins. The

uncertainty model associated to the present APM is characterized by a proper functional

structure, and by a certain number of basic parameters12,13

.

Without going into the details, it can be said that each of the terms of uncertainty

model is obtained as a sum of different contributions due to the different parts of the

APM. Usually, the most common sources of errors are:

− random experimental errors (repeatability)

( )∑ =

−⋅=5

1

1Re,i

i

i

BL

m MaC α

(16)

-10 -5 0 5 10 15 20 25-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

M=0.94 β=0 δE=0/0 δR

=0

α (°)

Cm

TWG - Re=611840

TWG - Re=618154

PT1 - Re=868509 - NO VFIN

CFD - Re=850500 - NO VFIN

ADB - Re=1000000

Figure 19. Cm vs. α (M=0.94).


− systematic experimental errors (known and not removable errors)

− CFD errors: computational grid, convergence, turbulence modelling, boundary

conditions, etc.

To such error sources it must be added the uncertainty due to the ignorance, i.e. the

incapacity to predict any unexpected phenomenology not foreseen by the APM during

the flight.

In practice, only by comparing pre-flight predictions and flight data it is possible to

estimate the reliability of the APM, and as a consequence improve or, at least, increase

the level of confidence of the estimated uncertainties. Before flight the only thing that

can be done is to compare as much data sources as possible, i.e. different wind tunnel

(CIRA PT-1, DNW-TWG) and CFD data. Moreover, also existing literature data for

similar vehicles are considered in order to assess the overall uncertainty level14

. As an

example, Fig. 20 shows the uncertainties envelope included in the USV FTB-1 Aero-

Data-Base for Cm0 and Cmα coefficients.

-0.070

-0.050

-0.030

-0.010

0.010

0.030

0.050

0.070

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25

M

UN

C[C

m00 00]] ]]

-0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25

M

UN

C[C

mαα αα]] ]]

Figure 20. Uncertainty versus Mach number for Cm0 (left) and Cmα(right).

Figure 21. The USV FTB-1 vehicle attached to the carrier (left) and just after launch (right).


9 CONCLUSIONS

A methodology for the integration of experimental and numerical aerodynamic data

sources aimed at the development of the USV FTB-1 Aero-Data-Base has been

described. The main goal of the activity has been the development of a general

framework to be used for a generic vehicle re-entry mission.

Moreover, the entire process of set-up and verification of the methodology will take

a great advantage by the in-flight experiments that will be carried out during the DTFT

missions. Global aerodynamic coefficients by means of inertial measurements and

surface pressure distributions by means of 306 static pressure ports disposed on the

vehicle surface will be acquired during the flight.

The experiments will give us the possibility of performing a comparison between the

prediction of the aerodynamic performance obtained by means of the APM and the in-

flight measurements. Main benefits deriving from this kind of comparison can be

recognized in: verification and validation of predictive capabilities of CFD codes for a

complex configuration in flight condition; verification of the suitability of the wind

tunnel test methodology; verification and tuning of the methodology for the

extrapolation to flight of the experimental measurements; reduction of the uncertainty

margins associated with the pre-flight prediction of the aerodynamic coefficients.

The first mission of USV FTB-1 was flown on February 24th

, 2007 (see Fig. 21), and

the flight data post-processing is being started at the time of writing.

ACKNOWLEDGEMENTS

The authors are grateful to some CIRA laboratories, in particular to the people of

Transonic Wind Tunnel laboratory, for having supervised both the test campaigns, to

the people of Applied Aerodynamic Laboratory for having provided approximate

methods results, and finally to the staff of Space Program Office, responsible of the

USV Program.

REFERENCES

[1] Pastena M. et al., “PRORA USV1: The First Italian Experimental Vehicle to the

Aerospaceplane”, AIAA paper 2005-3406, 13th

AIAA/CIRA International Space

Planes and Hypersonic Systems and Technologies Conference, Capua, Italy, May

16-20, 2005.

[2] Pamadi B.N. and Brauchmann G.J., “Aerodynamic Characteristics and

Development of the Aerodynamic Database of the X-34 Reusable Launch

Vehicle”, Paper No. 17.1, International Symposium on Atmospheric Reentry

Vehicles and Systems, Arcachon, France, March 16-18, 1999.

[3] Palazzo S. and Manco M., “CIRA USV Programme. Transonic Wind Tunnel Test

Results on the USV FTB-1 Scaled Model in Longitudinal and Lateral-Directional

Flight”, CIRA-CF-04-0435, September 2005.

[4] Gardner A.D. and Jacobs M., “Force and Moment Measurements on PRORA-

USV 1:30 FTB1 Model in DNW-TWG June-July 2005”, DNW IB 224-2005-C-

11, August 2005.

[5] Gardner A.D. and Jacobs M., “Force and Moment Measurements on PRORA-

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January 2006.


[6] Votta R., Roncioni P., Rufolo G.C., Marini M., “A Preliminary CFD Analysis of

the PRORA-USV Vehicle in Transonic and Low Supersonic Regime”, EWHSFF-

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[7] Roncioni P., Rufolo G.C., Votta R., Marini M., “An Extrapolation-To-Flight

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Vehicle”, International Astronautical Congress IAC2006, Valencia, Spain,

October 2-6, 2006.

[8] Roncioni P., “PRORA-USV Programme: Supersonic CFD Viscous Calculations

of USV/FTB1 Vehicle”, CIRA-CF-06-0237 Rev.1, July 2006.

[9] Andreutti G., “Proposte per l’incremento della stabilità latero-direzionale per

USV/FTB1”, CIRA-CF-04-0691, March 2005.

[10] Andreutti G., “Studio delle derivate dinamiche latero-direzionali per il velivolo

FTB1”, CIRA-CF-04-0599, March 2005.

[11] Mingione G. and Andreutti G., “Caratterizzazione aerodinamica del velivolo

FTB1 in regime basso supersonico utilizzando metodologie semplificate”, CIRA-

CF-05-0633, October 2005.

[12] Rufolo G.C., Roncioni P., Marini M., Votta R., Palazzo S., “Experimental and

Numerical Aerodynamic Data Integration and Aerodatabase Development for the

PRORA-USV-FTB_1 Reusable Vehicle”, AIAA paper 2006-8031, 14th

AIAA/AHI

International Space Planes and Hypersonic Systems and Technologies

Conference, Canberra, Australia, November 6-9, 2006.

[13] Rufolo G.C., Roncioni P., “PRORA USV FTB-1 Aerodynamic Characterization

and Aerodataset Development”, CIRA-CF-06-1410, December 2006.

[14] Cobleigh B.R., “Development of the X-33 Aerodynamic Uncertainty Model”,

NASA TP-1998-206544, April 1998.

[15] Catalano P., Amato M., “An Evaluation of RANS Turbulence Modelling for

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Documents

Usv FTB-1 Reusable Vehicle Aerodatabase Development