Rizzi - Modeling and Simulating Aircraft Stability and Control

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Modeling and simulating aircraft stability and controlThe SimSAC project

Arthur Rizzi

Dept. of Aeronautical and Vehicle Engineering, Royal Institute of Technology (KTH), Stockholm 10044, Sweden

a r t i c l e i n f o

Available online 26 October 2011

Keywords:

Aircraft design

Aerodynamics

Flight dynamics

Flight control

CFD

Simulation

a b s t r a c t

This paper overviews the SimSAC Project, Simulating Aircraft Stability And Control Characteristics for

Use in Conceptual Design. It reports on the three major tasks: development of design software,

validating the software on benchmark tests and applying the software to design exercises. CEASIOM,

the Computerized Environment for Aircraft Synthesis and Integrated Optimization Methods, is a

framework tool that integrates discipline-specific tools for conceptual design. At this early stage of the

design it is very useful to be able to predict the flying and handling qualities of this design. In order to

do this, the aerodynamic database needs to be computed for the configuration being studied, which

then has to be coupled to the stability and control tools to carry out the analysis. The benchmarks for

validation are the F12 windtunnel model of a generic long-range airliner and the TCR windtunnel

model of a sonic-cruise passenger transport concept. The design, simulate and evaluate (DSE) exercise

demonstrates how the software works as a design tool. The exercise begins with a design specification

and uses conventional design methods to prescribe a baseline configuration. Then CEASIOM improves

upon this baseline by analyzing its flying and handling qualities. Six such exercises are presented.

& 2011 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574

1.1. The aircraft design process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5741.2. Conceptual design for stability and control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574

2. CEASIOM software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576

2.1. ACBuilder-sumo module to define configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577

2.2. NeoCASS module for aero-structural sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578

2.3. AMB-CFD module for aerodynamic table construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

2.4. S&C analyzer/assessor modules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580

3. Benchmarks to validate CEASIOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

3.1. DLR-F12 windtunnel model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

3.2. SimSAC-TCR wind-tunnel model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

4. Design, simulate and evaluate exercisesgallery of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582

4.1. Flying aircraft. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583

4.1.1. Ranger 2000 trainer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583

4.1.2. B-747 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .583

4.2. Existing configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584

4.2.1. Alenia ERC-SMJ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584

4.2.2. Dassault SEJ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .584

4.3. New designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

4.3.1. GAV asymmetric Z-wing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

4.3.2. SAAB TCR TransCruiser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586

5. Concluding remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

Further reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

Contents lists available at SciVerse ScienceDirect

journal homepage: ww w.elsevier.com/locate/paerosci

Progress in Aerospace Sciences

0376-0421/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.paerosci.2011.08.004

E-mail address: [email protected]

Progress in Aerospace Sciences 47 (2011) 573588
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1. Introduction

1.1. The aircraft design process

The design of aircraft is an extremely interdisciplinary activity

produced by simultaneous consideration of complex, tightly

coupled systems, functions and requirements. The design task is

to achieve an optimal integration of all components into an

efficient, robust and reliable aircraft with high performance that

can be manufactured with low technical and financial risks, and

has an affordable cost taking in consideration the whole lifetime

of the aircraft. The aircraft design process (see Fig. 1(a)) is in

general divided into three phases, which tend to overlap in astaggered fashion. In the conceptual design phase the aircraft is

defined at a system level. Many variants are studied, and the

design selected is the one that best fulfils the specifications of the

market or a customer. This design then becomes a project and is

studied further. In the preliminary design phase, the tentatively

selected concept is refined until feasibility is established, i.e.

extensive array of design sensitivities are generated, design

margins, etc. About two-thirds of the way through this phase,

the concept is frozen and no major changes are expected there-

after unless serious problems arise. The final phase is the detailed

design phase in which details of the product are elaborated,

optimizations are made and data sets are generated. A large

variety of tools are used in each phase of the design process,

including empirical/handbook methods, wind tunnel testing,

flight-testing and numerical simulation and optimization tools

including NavierStokes solution methods. In general, low-fide-

lity tools are supposed to be used in the conceptual design phase

where many alternatives need to be analyzed in a short period,

while high-fidelity tools are used in the other design phases since

the concept evolves to an acceptable level of maturity. The term

fidelity refers here to the representation of the aircraft geometry

(and/or structure, where applicable) and of the physical modeling

that determines the aircraft behavior and performance (aerody-

namic stability and control and loads data bases). Today this is the

existing practice for developing a new aircraft. SimSAC focuses on

the modeling and simulation aspects in the design stages in the

circle in Fig. 1(a), namely in conceptual design and the down-selecting of configurations for project studies in preliminary

design. The reason that SimSAC focuses mainly on the conceptual

design process is that 80% of the life-cycle cost of an aircraft is

incurred by decisions taken during the conceptual design phase,

seeFig. 1(b). Mistakes here must be avoided because they are very

costly to remedy later and delay acceptance. Matters involving

the interaction of aerodynamics with structures and controls are

particularly prone to errors due to the low fidelity of the analysis

methods traditionally used.

1.2. Conceptual design for stability and control

Present trends in aircraft design toward augmented-stability

and expanded flight envelopes call for an accurate description of

Nomenclature

Symbols

CL lift coefficient

Cm pitching moment coefficient

F forces acting on aircraft

I moments of inertia

Kn static margin

L Euler angle rates

M1 Mach number

m mass

M aerodynamic moments

q pitch rate (rad/s)

S surface Area

ue elevator control signal

xcg X-location of center of gravity

U horizontal velocity

V velocity of aircraft

Greek letters

a angle of attackb side slip angle

d control surface deflection

te elevator actuator lag timeH aircraft orientation angle

x rotation rate of aircraft

Subscripts

c chord length

c canard

c cruise

e elevator

w wing

Abbreviations

AC aerodynamic center

ACBulder aircraft builder

AMB aerodynamic model builderB-747 Boeing wide-body airliner

CAD computer aided design

CG center of gravity

CEASIOM computerized environment for aircraft synthesis and

integrated optimization methods

CFD computational fluid dynamics

DSE design simulate evaluate

FCS flight control system

FHQ flying handling qualities

GAV general aviation vehicle

MAC mean aerodynamic chord

MTOW mean take-off weight

NeoCASS next generation conceptual aero-structural sizing suite

Ranger 2000 EADS military trainer aircraftSAS stability augmented system

SDSA simulation and dynamic stability analysis

SMJ Alenia 70-seat regional commuter jet concept

SEJ supersonic executive jet

SimSAC simulating aircraft stability and control characteristics

S&C stability and control

TCR Transonic Cruiser

VLM vortex lattice method

WB weights and balances

WT wind tunnel

Z-wing asymmetric wing planform

A. Rizzi / Progress in Aerospace Sciences 47 (2011) 573588574


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the flight-dynamic behavior of the aircraft in order to properly

design the flight control system (FCS). Hence there is a need to

increase knowledge about stability and control (S&C) as early as

possible in the aircraft development process in order to be First-

time-right with the FCS design architecture. The review paper by

Vos et al.[1] describes these ideas in terms of the Virtual Aircraft

and explains much of the background motivation for our

work here.

Fig. 2 spells out the details in the early design steps in the

circle shown in Fig. 1(a) for the definition of the virtual aero-

servo-elastic aircraft. It illustrates two design loops in the

conceptual design phase that follow the first-guess sizing (usually

done by a spread-sheet) to obtain the initial layout of the

configuration. The first one, the pre-design loop, is aimed at

establishing a very quick (time-scale can be from one to a few

weeks) yet technically consistent sized configuration with a

predicted performance. The second one, the concept-design loop,

is a protracted and labor intensive effort involving more advanced

first-order trade studies to produce a refinement in defining the

minimum goals of a candidate project. At the end of the

conceptual design phase all the design layouts will have been

analyzed, and the best one, or possibly two designs will be

Fig. 1. SimSAC design: (a) aircraft design process from conceptual design to manufacturing and testing. SimSAC focuses on the Conceptual-to-Project phases in the circle;

(b) contemporary product development contrasted against Virtual Aircraft approach.

Fig. 2. Two design loops in the conceptual design phase process and the down-select to project study in preliminary design. CEASIOM focuses in particular on the S&C,

structural-aeroelastic and performance characteristics of the aircraft (after an illustration by Daniel Raymer).

A. Rizzi / Progress in Aerospace Sciences 47 (2011) 573 588 575


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down-selected to the preliminary design phase. During the pre-

liminary definition, project design is still undergoing a somewhat

fluid process and indeed warrants some element of generalist-

type thinking, but the minimum goals of the project have already

been established during the conceptual definition phase and the

aim is to meet these targets using methods with higher order than

those used during the conceptual definition phase.

The first stages of the design process of a new aircraft are

related to the sizing of the main components. The designer refersto some stability and control characteristics as a guidance of the

design process. Up to now, the aerodynamic data considered in

these early design steps were mostly based on tabulated data,

issued from previous experience and/or semi-empirical

approaches. Although satisfactory when determining some high

level parameters (e.g. areas and planforms of lifting surfaces),

such simplified approaches can lead to errors in the sizing

process, especially when used in final conceptual design steps

(e.g. sizing or allocation of control surfaces), and do not offer

sufficient fidelity. For example errors can be due to Reynolds

number effects, configuration sensitivities, dynamic motion

effects and related issues, and such errors generally can be

detected only with a significant increase in the fidelity of the

aerodynamic data base, for instance with wind-tunnel data or

even flight test data. The later in the design process the error

identified, the higher the cost of its correction.

Traditionally, wind-tunnel measurements are used to fill look-

up tables of forces and moments over the flight envelope but

wind-tunnel models become available only later in the design

cycle (see Fig. 3). To date, most engineering tools for aircraft

design rely on handbook methods or linear fluid mechanics

assumptions. The latter methods provide low-cost reliable aero-

data that as long as it is a conventional configuration the aircraft

remains well within the limits of its flight envelope. However,

current trends in aircraft design toward unconventional designs

with augmented-stability and expanded flight envelopes require

an accurate description of the non-linear flight-dynamic behavior

of the aircraft. The obvious option is to use Computational Fluid

Dynamics (CFD) early in the design cycle to predict the aerodata,

as indicated inFig. 3. Thus, an increase in the fidelity level of the

aerodynamic database is needed at all the steps of the design

process: this is one of the main objectives of the SimSAC project

(Simulating Aircraft Stability And Control Characteristics for Use

in Conceptual Design). This FP6 European project gathers a total

of 17 partners and is coordinated by KTH (A. Rizzi) (www.

simsacdesign.eu). This paper surveys the three main areas of

project activities:

construction of a new tool, called CEASIOM, dedicated to theconceptual and preliminary design and analysis of fixed-wing

aircrafts, assessment and improvement of existing CFD tools for pre-dicting the stability and control dynamic derivatives,

application of the CEASIOM software to two clean-sheet designstudies; a near-sonic large transport aircraft (TCR) and an

unconventional Z-wing general aviation configuration (GAV);

in addition existing designs are studied further, such as the

Alenia regional commuter jet SMJ and the Dassault supersonic

executive jet SEJ, and lastly real aircraft, Ranger 2000 military

trainer and the B-747 are evaluated.

CEASIOM is meant to support engineers in the conceptual

design process of the aircraft, with emphasis on the improved

prediction of stability and control properties achieved by higher-

fidelity methods than found in contemporary aircraft design tools.

Moreover CEASIOM integrates into one application the main

design disciplines, aerodynamics, structures and flight dynamics,

impacting on the aircrafts performance. It is thus a tri-disciplin-

ary analysis brought to bear on the design of the aero-servo-

elastic aircraft. CEASIOM does not however carry out the entire

conceptual design process indicated inFigs. 2and3. It requires as

input an initial layout as the baseline configuration sized to the

mission profile (output of pre-design loop O(10) parameters).

Then it refines this design (in concept-design loop O(100) para-

meters) and outputs it as the revised layout for consideration in

the down-select process (say O(1000) parameters). In doing all

this, CEASIOM, through its simulation modules, generates signifi-

cant knowledge about the design and thereby increases its

fidelity. The information generated is sufficient input to a six

Degrees of Freedom engineering flight simulator. It is also

sufficient to construct a suitable wind-tunnel model, comparable

in quality to the one used in the traditional approach to S&C

design. In fact the design exercise TCR spans all these steps,

starting with a baseline input and refining it all the way to flight

simulation, WT model construction, testing and comparison-

verification of the entire SimSAC concept.

2. CEASIOM software

CEASIOM is a framework tool that integrates discipline-spe-

cific tools like CAD and mesh generation, CFD, stability and

control analysis and structural analysis, all for the purpose of

aircraft conceptual design [2]. The flight-dynamic equations foraircraft motion begin with Newtons Second Law and lead to the

non-linear inertial expressions for translation, rotation and kine-

matical relationships, written in symbolic form:

Translation: m _VxmV FaeroFthrust FgravityRotation : I _xx Ix Maero

Kinematics: _H L 1

where F denotes aerodynamic (aero), propulsion (thrust) and

gravity forces;M denotes aerodynamic (aero) moments;Vrepre-

sents the velocity of the aircraft and x its rotation rate;mdenotes

its mass andI moments of inertia;H is its orientation angle andL

the Euler angle rates. The coupled expressions in Eq. (1) yield a

system of ordinary differential equations that determine the

instantaneous motion of a rigid aircraft. The aircraft is free to

Fig. 3. With initial sizing as input CEASIOM advances the design to fidelity of

wind-tunnel model by high-fidelity simulation (top) to enrich design parameters

by two orders magnitude (bottom).

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move under the influence of the aerodynamic forces and

moments while the instantaneous state of the flow field sur-

rounding the aircraft is influenced by its prior states. CEASIOM

addresses the task of solving Eq. (1).

The classical approach to analyzing system (1) is to linearize it

through a perturbation analysis that effectively decouples the

system. This approach yields the so-called stability and control

parameters to characterize aircraft flight dynamics upon which a

large knowledge base has been built to help designers do theirwork. The system is de-coupled by a local linearization procedure

where the forces and moments are expanded in a Taylor series

yielding the static and dynamic stability derivatives, exemplified

by the time dependent pitching moment:

The task then is to compute these derivatives by CFD and use

them for solving Eqs. (1), the S&C task. Dynamic stability para-

meters (derivatives), in particular, provide information about the

stiffness and damping attributes of the dynamic system. For

example, the so-calleddamping derivativecharacterizes the varia-

tion of forces and moments with respect to angular rates. The S&Cmodule in CEASIOM analyzes and evaluates the dynamical system

(1) for suitable flight handling qualities using such parameters.

Showing aspects of its functionality, process and dataflow,

Fig. 4 presents an overview of how the CEASIOM software goes

about solving Eq. (1).

Significant features are developed and integrated in CEASIOM

as modules:

1. Geometry module Geo-sumo

A customized geometry construction system to define the

aircraft configuration coupled to surface and volume grid

generators; Port to CAD via IGES.

2. Aerodynamic module AMB-CFD

A replacement of and complement to current handbook aero-dynamic methods with new adaptable-fidelity modules

referred to as (a) Tier I, (b) Tier I and (c) Tier II:

a. Steady and unsteady TORNADO vortex-lattice code (VLM)

for low-speed aerodynamics and aero-elasticity.

b. Inviscid Edge CFD code for high-speed aerodynamics and

aero-elasticity.

c. RANS (Reynolds Averaged NavierStokes) flow simulator

for high-fidelity analysis of extreme flight conditions.

3. Stability and Control module S&C

A simulation and dynamic stability and control analyzer and

flying-quality assessor. Six Degrees of Freedom test flight

simulation, performance prediction, including human pilot

model, Stability Augmentation System (SAS) and a LQR basedflight control system (FCS), or J2 Universal Tool-Kit, the

commercially available industrial grade engineering analysis

tool for assessment and visualization of aircraft in flight. (see

www.j2aircraft.com).

4. Aeroelastic module NeoCASS

Quasi-analytical structural analysis methods that support

aero-elastic problem formulation and solution.

5. Flight Control System design module FCSDT

A designer toolkit for flight control-law formulation, simula-

tion and technical decision support, permitting flight control

system design philosophy and architecture to be coupled early

in the conceptual design phase.

6. Decision Support System module DSS

An explicit DSS functionality, including issues such as fault

tolerance and failure tree analysis.

2.1. ACBuilder-sumo module to define configuration

The task is to build a tabular model for the aerodynamic forces

and moments on the airframe by simulation. The geometry

should be represented in a way to be parameterized by a small

number, say O(100), of parameters with intuitive interpretation.

Fig. 5(b) presents an overview of the main components in

ACBuilder-sumo and their functionality [7]. ACBuilder provides

basic parametrization, which sumo then enhances to produce

surface and volume grids for Euler simulation as well as a bone

fide IGES file that is meshable (watertight). The meshable modelcan subsequently be used directly as input by the Tier I or II

solvers of the Aerodynamic module AMB-CFD.

The tools for managing the geometry modeling are described

below with comments on the workflow, in particular on the

Fig. 4. CEASIOM Software for analyzing Eq. (1): core modules ACBuilder-sumo, AMB-CFD, NeoCASS and S&C (SDSA, J2 and FCSDT) in the CEASIOM software.

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degree of automation achievable while preserving the engineers

accountability for the quality of the data compiled. The challenge

is to approach automatic volume mesh generation for Tier I ,

with geometries including control surface deflections.

The geo.xml file defines the geometry with sufficient details

for the Tier I computations. The lifting surfaces are assembled

from quadrilateral planforms, twist, dihedral, etc., and airfoil

definitions. Body, booms, cockpits, etc. are described by only a

few key parameters, for the VLM the slender body approximation

provides a rough estimate of the body influence on the downwash

on lifting surfaces. Control surface deflections are simple because

the lifting surfaces are modeled as lamina, and can be effected by

actually changing the geometry or by just manipulating surface

normals in the numerical flow tangency conditions.

The geo.xml file is edited by the ACBuilder GUI, which gives

visual feed-back of not only external geometry as needed for

aerodynamics but also data necessary for weights and balance

estimates. In addition to geo.xml, VLM requires a few solver

parameters, such as lattice densities, wake relaxation scheme, etc.

These parameters can easily be set by the engineer and have

default values based on past experience.

Panel methods and Euler simulations require much higher

fidelity geometry, in particular a closed surface, smooth enough to

support a surface grid with proper refinements at critical places

like leading and trailing wing edges, wing tips, etc. But also the

surfaces on body (booms, fairings, etc.) must be well-rounded not

to create spurious pressure peaks or troughs.

The sumo package builds an aircraft model from a set of closed

spline surfaces and provides a proper GUI for designing the

shapes from cross sections and control points. Sumo calculates

the intersections and can perform local smoothings and closure of

features such as un-closed wing tips, as necessary, to make asingle closed surface. It can proceed to generate a triangular

surface mesh with density controlled by radii of curvature, etc.,

from a small set of user parameters.

The geo.xmlsumo interface provides most of the data neces-

sary, but user interaction is required when the xml geometry is

inadequate. Typically, components such as vertical and horizontal

tails and the rear fuselage may not intersect properly; sumo will

then attempt repair with default parameter settings and issue

error messages; the response called for is to change the geometry

using ACBuilder. Control surface deflections can be done by actual

geometry deformation before mesh generation, or by manipula-

tion of surface normals. The surface deformation currently fills

the gaps that are created; details of multi-element high-lift

systems are not supported.

The step from surface mesh to volume mesh is taken by the

TetGen package, which needs only a few user parameters to fill

the volume between exterior of aircraft and the far-field sphere

by a tetrahedral mesh. The quality of the surface mesh is crucial.

Inadequate surface meshes are often caused by surface irregula-

rities, and call for geometry repair by the engineer.

The Tier II geometry models require high-quality surfaces with

all relevant details. Such high-quality geometry models can be

created by sumo and sent as IGES file to fully-fledged mesh

generator systems such as ICEM/CFD. A CAD model often exists

for existing aircraft, and data may be available for validation

experiments and modification exercises. The approximation of a

given CAD geometry by the geo.xml format is not a well-defined

task and currently must be done manually by the engineer, by

extracting cross sections, etc., as native sumo input, or with even

more radical shape approximation, by adapting the O(100) para-

meters of geo.xml to the best fit.

2.2. NeoCASS module for aero-structural sizing

The NeoCASS (Next generation Aero Structural Sizing) module

combines state of the art computational, analytical and semi-

empirical methods to tackle all the aspects of the aerostructural

analysis of a design layout at the conceptual design stage [8]. It

gives a global understanding of the problem at hand without

neglecting any aspect of it: aerodynamic, structural and aeroelastic

analysis from low to high speed regimes, buffet onset, divergence,

flutter analysis and determination of trimmed condition and

stability derivatives both for rigid and deformable aircraft.

Similar to the aerodynamic module, structural models of

increasing accuracy and computational cost provide consistent

structural representation of the aircraft from the early conceptualdefinition until the late detailed definition (see Fig. 6). Prelimin-

ary analysis is focused on determining and representing a reason-

able structural/nonstructural mass and stiffness distribution,

which satisfies strength, stiffness and stability requirements. A

few structural elements capable of giving equivalent structural

behavior are available, such as a linear equivalent plate and a

linear/nonlinear equivalent beam to introduce geometry non-

linear effects. These models lead to low-order algebraic problems,

keeping the computational cost very low and allowing several

configurations to be examined quickly.

Two classic lifting surface methods are implemented. The

Vortex Lattice Method (VLM) is used for subsonic steady aero-

dynamic and aeroelastic calculations, and the Doublet Lattice

Method (DLM) for subsonic flutter analysis and prediction of

Fig. 5. Shape Definition Module ACBuilder-sumo. (a) ACBuilder visual feedback. (b) ACBuilder-sumo software chain: from sketch to CFD grids.



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harmonic stability derivatives. For higher fidelity and higher

Mach number CEASIOM uses the inviscid version of the CFD code

Edge. Aero-elastic analyses and control surface deflections are

carried out by the transpiration boundary-condition method,

which accounts for structural motion and deformation by speci-

fying the velocity direction at the wall. This method avoids

complex and time-consuming remeshing as well as sliding mesh

techniques and the meshing of narrow gaps.

Flutter analyses are carried out by Reduced-Order Models

(ROM) constructed by the DLM and Edge solvers. Indeed, the

aerodynamic ROM is determined through a numerical perturba-

tion to the system starting from an equilibrium condition. The

determination of the trimmed steady state of the aircraft flying a

frozen manoeuvre is an important sub-problem in most analyses,

to determine pressureload distribution and structural deflec-

tions/twists and to assess flutter instability. With non-linear

models an iterative process is required to determine this condi-

tion. NeoCASS uses a Jacobian-Free NewtonKrylov (JFNK)

method, which does not need the Jacobian of the system.

Coupling of structural and aerodynamic models is accomplished

by a meshless radial basis function scheme, which allows any

combination of them. With the structural model so specified, the

aeroelastic stability coefficients, the so-called eta values can be

determined.

2.3. AMB-CFD module for aerodynamic table construction

A prerequisite for realistic prediction of the S&C behavior and

sizing of the FCS is the availability of complete and accurate

aerodata (i.e. the S&C database). The aerodata is represented by

an multidimensional array of dimensionless coefficients of aero-

dynamic forces and moments, stored as a function of the state

vector and control-surface deflections. The aerodynamic tables in

AMB-CFD have the following format for the stability coefficients,for the control coefficients and for the unsteady coefficients,

wherea is the angle of attack, Mis the Mach number and b theside slip angle,q,p and rare the three rotations in pitch, roll and

yaw. The three control surfaces that can be deflected are the

elevator (de), the rudder (dr) and the aileron (da). TheTable 1is

linearized and build up from 7 three-dimensional tables with a,Mand a third parameter (b, q, p and r,de,dr orda). The coefficients

must be computed for each of these three parameters throughout

the flight envelope, hence the computational cost is problematic,

particularly if done by brute force: a calculation for every entry in

table. The total entries can number in tens of thousands, or even

more in late design stages. Fortunately methods are available that

can reduce the computational cost.

There are essentially three issues, see Fig. 7(a).

Fig. 6. Architecture, function and process of NeoCASS.

T

able

1

FormattablesinSDSA.

(a)Stabilitycoefficientstable

Alpha

Mach

Bet

a

Q

P

R

CL

CD

Cm

CY

C

Cn

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(b)Controlcoefficientstable

Alpha

Mach

de

dr

da

CL

CD

Cm

CY

C

Cn

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

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x

x

x

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x

x

x

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x

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(c)Unsteadycoefficientstable

Mach

Cm_a

CZ

_a

CX

_a

CY

_b

C

_b

Cn_b

x

x

x

x

x

x

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Firstly, a spectrum of computational tools available, from

RANS to potential flow models and semi-empirical methods. Each

of the tools has a range of validity which can be exploited to keep

the computational cost down. For the preliminary design of the

aircraft and its FCS and as long as the flight attitude remains well

within the limits of the flight envelope in the range of low-speed

aerodynamics, Tier I computational methods can provide the

aerodata. For a refined design of the FCS or for flight attitudes

close to the border of the flight envelope, the linear or inviscid

methods used in the Tier I tools fail to predict the proper

aerodynamic behavior and also Tier II RANS methods will be used

selectively. Then results from all these different sources, with low

fidelity/low-cost data indicating trends and a small number of

high-fidelity/high-cost simulations correcting the values, can be

fused into a single database [16].

Secondly, mesh-free interpolation methods can significantly

reduce the number of data points which actually need to be

computed to fill the table. Some studies[1517] of using kriging

for the generation of aerodynamic data have been published using

the software package DACE (Design and Analysis of Computer

Experiments), a Matlab toolbox for working with kriging approx-

imations to computer models. Here the states of the aircraft are

set to be the inputand the aerodynamic coefficients are set to be

the response of the computer model. The aim is to use this

approximation model as a surrogate for the computer model.

Thirdly, the identification of parameter regions where the

aerodynamics is nonlinear, and hence where Tier II fidelity is

needed, is a samplingproblem. Therefore the AMB-CFD module

develops with these three elements [6].

The Tier II CFD tools are currently loosely coupled to CEASIOM

because users are mainly interested in coupling their own RANS

CFD tools. However, standard interfaces and file formats are

defined in CEASIOM to which different RANS solvers have been

coupled with MATLAB and Python scripts to perform sequences ofruns and collect results.

2.4. S&C analyzer/assessor modules

CEASIOM offers its user three distinct modules: SDSA, J2 and

FCSDT for analyzing and assessing the flight characteristics of the

design configuration, i.e. they solve 1rewritten here symbolically,

as part of their flight simulator

ds

dt A1Fg FaFt 2

where

s fu,

v,

w,

p,

q,

rg 3

andFahas been determined by AMB-CFD and Fgby NeoCASS-WB.

The stability analysis requires deriving the linear set of equations

by calculation of the Jacobian B for the defined state of the flight;

Ads

dt Bs 4

where

B @F i,j@sj

( ) 5

Now the eigenvalue problem can be formulated as

A1BIls 0 6

The solution of the eigenvalue problem gives directly the

frequency and damping coefficients. The eigenvector problem is

also solved to identify the motion modes. Solving the nonlinear

equation system for the equilibrium state

Fs,t 0 7

determines the trim conditions. The SDSA module (Simulationand Dynamic Stability Analyzer) provides the following function-

alities[9]:

1. Stability analysis:

a. eigenvalue analysis of linearized model,

b. time history identification (nonlinear model).

2. Six Degrees of Freedom flight simulation:

a. test flights, including trim response,

b. turbulence.

3. Flight Control System:

a. human pilot model,

b. stability augmentation system,

c. FCS based on Linear Quadratic Regulator (LQR) theory.

4. Performance prediction5. Miscellaneous (data review, results review, cross plots, etc.)

Fig. 7(b) illustrates the structure and functionality of this

module. SDSA solves the nonlinear model of the aircraft motion

Eq. (1) for all its functions. For the eigenvalue analysis, the model

is linearized numerically around the equilibrium (trim) point.

Eigenvalue and eigenvector analyses allow automatic recognition

of the typical modes of motion and their parameters. The flight

simulation module can be used to perform test flights and record

flight parameters in real-time. The recorded data can be used for

identification of the typical modes of motions and their para-

meters (period, damping coefficient, phase shift). The stability

analysis results are presented as figures of merits based on JAR/

FAR, ICAO and MIL regulations. The SDSA embedded flight control

Fig. 7. AMB-CFD and S&C Modules: (a) architecture of the Aerodynamic Dataset Generator AMB-CFD; (b) SDSA structure and functionality.



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system allows a pilot in the loop, and SAS and FCS based on a

LQR approach. The LQR-FCS module allows computing and saving

control matrices for simulations over the whole envelope. In this

way, SDSA includes the FCS for stability characteristics and in-

flight simulation for the closed loop case. The performance

option is designed to compute basic performance parameters:

flight envelope (Vmin and Vmax versus altitude of flight), selected

manoeuvres (e.g. regular turn), range and endurance character-

istics. For all mentioned functionalities the starting point is thecomputation of the trimmed state with sufficient initial condi-

tions. The test flight settings include initial state, disturbances,

and single/double step controls. SDSA is a stand-alone application

integrated into CEASIOM. As a module of CEASIOM, it receives all

the necessary data (aerodynamics, mass, inertia, available thrust),

when available, without special prompting.

The necessary data can be delivered to SDSA as an XML file or

as a set of plain text files. The second option is useful e.g. for

experimental data. The data set contains aerodynamic coefficients

or/and stability derivatives tables, mass and inertia data, propul-

sion data, control derivatives and reference dimensions. The

control and propulsion data can be completed and edited using

special options of SDSA. SDSA accepts aerodynamic data as tables

of stability derivatives as a function of angle of attack and Mach

number. SDSA also accepts as a multidimensional array of force

and moment coefficients versus six state parameters (angle of

attack, Mach number, sideslip angle and rotational velocity

components). A similar array is defined for control derivatives

and stability derivatives versus selected accelerations (i.e. alpha

dot derivatives). All aerodynamic data (derivatives) can be

reviewed and are checked by comparison with typical values

(Fig. 8).

The functionalies of the J2 and FCSDT modules are similar to

SDSA. Commercially available, J2 is a stand-alone system that has

been coupled to CEASIOM, see www.j2aircraft.com for further

details about J2. The Flight Control System design module FCSDT

is a designer toolkit for flight control-law formulation, simulation

and technical decision support. The companion paper [23]in this

issue describes this module in more detail.

3. Benchmarks to validate CEASIOM

Two benchmarks [5] have been used in SimSAC to validate

CEASIOM. The first is DLRs wind-tunnel model F12, a generic

long-range airliner. The model has no defined control surfaces.

The second one, the TCR TransCruiser, originates from one of the

SimSACs DSE exercises which designed, built and tested the final

configuration. It has one control surface for longitudinal control,

an all-moving canard.

3.1. DLR-F12 windtunnel model

The DLR-F12 model used is a typical geometry of a generic

transport aircraft and was constructed specifically for dynamic

tests. Such a model must meet different design criteria than

conventional wind tunnel models. The mass of a dynamic wind-tunnel model as well as its moments of inertia must be as low as

possible to achieve a favorable ratio between the aerodynamic

forces of interest and the additional acting forces from mass. On

the other hand, the elastic deformation has to be as small as

possible. Furthermore, the first Eigenfrequency of the model

should be one order of magnitude above the excitation frequency,

at least 15 Hz, to avoid the excitation of the models higher

harmonics. The best material to meet all these requirements

proves to be carbon fibre reinforced plastic (CFRP). Using CFRP-

Sandwich structure as is used in building full-size gliders, the

DLR-F12 model has a weight of 12 kg. The model was manufac-

tured by the DLR plastics workshop in Braunschweig. In order to

evaluate the influence of individual components of the tested

airplane configurations, such as winglets, vertical or horizontalstabilizers, nacelles, on the dynamic derivatives the models are

designed in a modular way so that every component of interest

can be added to the model. The DLR-F12 model not only allow the

measurement of unsteady forces and moments but also unsteady

pressure distributions using pressure taps at specific chordwise

stations on the wing and horizontal and vertical stabilizers.

A variety of computed aerodynamic coefficients versus angle

of attack are compared with the experimental data in Fig. 9. The

lift coefficient is well predicted by CFD tools with a lift over-

estimation by Euler methods for the highest angles of attack.

A shift in the pitching moment of about 0.03 exists between

experimental and computational data and is likely to come from

the model support effect (ventral sting), not taken into account in

the computations. As far as the VLM tools are concerned, thediscrepancy of the results is large, probably coming from differ-

ences in the geometries and/or meshes. This benchmark case is in

the linear range of the flight envelope 51rar81.

3.2. SimSAC-TCR wind-tunnel model

A wind-tunnel model, without engines, of the TCR-C15 canard

configuration, the final design of the DSE-TCR exercise, has been

built by Politecnico di Milano and the model has been tested in

the TsAGI T103 wind tunnel at a speed of 40 m/s. This is the wind

Fig. 8. Windtunnel measurements of F12. (a) DLR-F12 model on the MPM, (b) axis-system for force coefficients.

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tunnel of continuous type of action with open working section

(elliptical cross section of 2.33 4.0 m). The static test in the wind

tunnel campaign includes a variation of pitch and slide-slip anglesfrom 101 to 401 with step of 21 and of the canard deflection

angle of incidence from 151 to 151 with a step of 51. The

campaign also includes dynamic tests of low and high amplitude

oscillations for pitch, roll and yaw at selected frequencies. The

length of the model is 1.5 m, which corresponds to a scaling factor

of 1:40 to the real aircraft. The wind tunnel campaign will be

reported in a separate publication [5]. A variety of computed

aerodynamic coefficients versus angle of attack is compared with

the experimental data inFig. 10. Compared to the F-12 case, the

flight envelope here is larger, 51rar81, and includes thenonlinear range. The pitch moment versus a curve can be calledpiece-wise linear, with several break-points between linear sec-

tions. The flight control system must take these break-points into

account, and so they must be represented in the computed aero-

database of coefficients and derivatives. This topic has been

investigated by Eliasson et al. [12]and they give a flow-physics

explanation for these breakpoints along with the computationalrequirements to resolve them.

4. Design, simulate and evaluate exercisesgallery of results

A major undertaking in SimSAC is the design, simulate and

evaluate (DSE) exercise. The endeavor begins with a design speci-

fication and uses conventional design methods to prescribe a

baseline configuration. Then CEASIOM improves upon this base-

line by analyzing its flying and handling qualities. This section

presents a gallery of results for the DSE exercises.

Three different types of exercises were undertaken. The first

one studied real aircraft in order to bring in very practical aspects,

e.g. loss of the aircraft during flight. The second one applied

Fig. 9. Evolution of lift and pitching moment coefficients with angle of attack.

Fig. 10. Comparison of computed normal force and pitch moment with data measured in the TsAGI windtunnel. (a) Breakpoints in normal and moment curves. (b) Model

in TsAGI tunnel.



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CEASIOM to existing configurations that are still on the devel-

opers drawing boards. The third one presents clean-sheet designs

resulting from CEASIOM where the specifications were drawn up

in SimSAC.

4.1. Flying aircraft

4.1.1. Ranger 2000 trainer

The Ranger 2000 aircraft, Fig. 11, is a mid-wing, tandem seat

military training aircraft with a turbofan engine. The wing and

fuselage are manufactured of composite material and the empen-

nage is a metal T-tail design. The control surfaces are manually

operated elevator and rudder, hydraulically assisted ailerons, a belly

mounted speed-brake and electrically operated split flaps[9].

One issue that was discovered with the Ranger 2000 was the

rudder free effects at low altitude and low speed with the Speed

Brake out when the aircraft was hit by a lateral gust. This was

discovered through the aircraft crashing on approach. As such the

question was asked as to whether the crash could be modeled in

the J2 module flight simulatorFig. 11(c).

Taking the original model and adding a slight modification

to help to drive the rudder through the aircraft sideslip, a new

manoeuvre was created where the aircraft was hit by a lateral

gust that caused an initial yaw rate disturbance, and the rudder

was left to be deflected by the ensuing sideslip. From the results

shown above, it can be seen inFig. 11(d) that the Yaw Rate never

manages to damp out despite the oscillations of the Rudder and

the Sideslip (increasing in magnitude each oscillation). The result

is that the aircraft rolls inverted and continually loses altitude.

The end result is a crash. The same manoeuvre was also

attempted at a higher speed to see if speed had any effect. Whatwas discovered was that increasing the speed on the aircraft

resulted in a stable reaction.

4.1.2. B-747

The goal here is to analyze a real aircraft, with realistic control

surfaces and channels (Fig.12). The aircraft analyzed is the B-747,

a widebody commercial airliner, with all the control surfaces

modeled in CEASIOM[13].

The control system consists of Krueger flaps, a movable

stabilizer with four elevator segments for longitudinal control,

five spoiler panels, an inboard aileron and an outboard aileron for

lateral control and a two-segment rudder for directional control.

Several Stability and Control qualities are analyzed, from simple

Fig. 11. Overview of DSE results for Ranger 2000. (a) Ranger 2000 Military Training Aircraft. (b) Pressure computed on the surface. (c) Ranger crash studied in J2 flight

simulator. (d) Rudder deflection & yaw rate time histories from flight simulator.



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trim calculations over control law design to complete nonlinear

simulations. For the next step in the fidelity staircase CEASIOM

uses CFD calculations in Euler mode, on grids adapted to geo-

metric features only. The sumo surface modeler constructs a

water-tight solid model from the individual surfaces which

describe the aircraft components. A triangular surface mesh is

generated on the outer surface of the solid, controlled by

geometric properties such as curvature of the surface. A tetra-

hedral grid suitable for Euler flow models is subsequently

generated by the TetGen software [20]. For RANS modeling it

may be desirable to resolve the trailing edges of lifting surfaces

properly. But for inviscid flow models and CFD flow solvers in

Euler mode, sharp trailing edges are appropriate, so in the interest

of grid economy, sumo knows about sharp trailing edges of lifting

surfaces. A detailed RANS model requires resolution of the open-

ing gaps and exposed edges of a deflected control device. For

potential-flow modeling, CEASIOM/sumo provides data for a

transpiration-law model where the mesh is left undisturbed and

only the surface normals are rotated. The sumo-generated surface

mesh on the tail are shown inFig. 12(a). The surface mesh does

notconform to hinge lines, but it knows which surface elements

are affected by the deflection, and those are colored. The pressure

field computed in the Edge Euler-simulation for straight and level

M1 0:8 flight with angle of attack a 11 after a 101 rudderdeflection is shown in Fig. 12(b). Fig. 12(c) presents the short-

period analysis by SDSA illustrated against the ICAO recommen-

dations for undamped natural frequency.

4.2. Existing configurations

The objective of the task was to analyze the characteristics

of several aircraft configurations, existing on the companys

drawing boards, making use of the CEASIOM tools. The baseline

configuration was then modified/optimized in order to make an

improvement to their S&C characteristics, as determined by

CEASIOM[14]. The configurations under study were

1. Alenia Executive/Regional Commuter (SMJ), analyzed by

Alenia Aeronautica

2. Dassault supersonic executive jet (SEJ), analyzed by Dassault

Aviation

4.2.1. Alenia ERC-SMJ

Alenia analyzed the 70-seat Regional Commuter SMJ config-

uration, especially as weight and S&C characteristics are

concerned. Some deficiencies were found in S&C properties that

have been corrected by appropriate configurational changes.

SDSA analysis indicated non-optimal performance of the baseline

configuration with respect to Dutch Roll and elevator deflection.

At higher speed and altitude the aircraft is not compliant with JAR

23 rules for Dutch Roll characteristics. Another problem found

was the elevator deflections required for trim were too high and

also the originally designed horizontal tail presented problems.

This analysis suggested changing some details in the configura-

tion in order to improve the S&C characteristics, namely:

1. vary wing dihedral angle;

2. vary wing position;

3. vary the horizontal tail dihedral angle;

4. vary incidence of the horizontal tail.

Several different configurations, with variations of the above

parameters, have been defined and analyzed, and the optimized

layout found featuring the best S&C characteristics. The defined

changes are as follows:

1. reduced wing dihedral from 7.251to 3.01;

2. wing position moved ahead, 2% of fuselage length;

3. reduced horizontal tail dihedral from 6.01to 01;

4. increased incidence of horizontal tail from 01to 31.

The new configuration is presented inFig. 13.

4.2.2. Dassault SEJ

The Supersonic Executive Jet SEJ is a prototype proposed by

Dassault Aviation for a civil supersonic jet (Fig. 14). It is part of the

HISAC project (www.hisacproject.com), which aims at establish-

ing the technical feasibility of an environmentally compliant

small size supersonic transport aircraft. Objectives mainly dealwith reduction of the external noise and NOx emissions and range

at least transatlantic. SEJ is the low noise configuration, which is

based on the following design drivers:

delta wing and nose canard; three high by-pass ratio CVC engines; main landing gears attached on the wing structure; a vertical fin attached on the rear fuselage; design cruise speed M1.6 and the cruise altitude 14,600 m; nominal payload: eight passengers; approximate take-off weight: 50,200 kg.

The aerodynamic coefficients in low speed have been calculated

using the Tier I method Tornado v.135 (VLM). Using the aerodata

Fig. 12. The B-747-100 airliner modeled in CEASIOM with Edge Euler solution, M1 0:8, a 11, rudder deflection dr 101. (a) Control surfaces: stabilizer, inboard andoutboard ailerons, and two-segment rudder. (b) Pressure coefficient. (c) Short period characteristics predicted in SDSA.

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obtained, the stability and control module SDSA calculates the trim

characteristics (Fig. 14(b) and (c)). The baseline configuration has

been developed by Dassault using in-house methods (Fig. 14(a)).

Ailerons and rudder are used together for lateral control. Flaps and

slats are used as high-lift devices. Fig. 14(b) and (c) presents the

longitudinal trim analysis results from SDSA. Notice that the static

margin is negative ( 5.5% fora 01), which means that the aircraft

is unstable. Today, fly-by-wire systems allow such a configuration(although the authorities do not yet) and increase aircraft perfor-

mance. This value fits quite well with the Dassault predicted one.

Some conclusions can be drawn from the results. For maximum

approach speed (TAS 80 m/s), angle of attack at landing is a little

bit too high and exceeds the tolerance (151), which may disturb pilot

visibility. However elevator deflection angle is within the tolerance

interval.Fig. 14(d) shows that dynamic stability for Phugod motion

is satisfied.

4.3. New designs

Two clean-sheet designs originating in the SimSAC project are

presented. The GAV is a very light jet with a novel asymmetric

Z-wing design comparable in size and mission to the Eclipse 500.

The TCR TransCruiser is a sonicairliner of 200 passengers.

4.3.1. GAV asymmetric Z-wing

The objectives of this DSE exercise were to design an unconven-

tional (Z-configuration) general aviation aircraft and to explore what

type of manual flight-control system would be required to make itfly[10]. The Z-configuration has one side of the main wing moved

back to the empennage position giving it a Z looking layout from top

view. It is done to be able to generate direct lift. But this configura-

tion poses some interesting lateral/directional flying characteristics.

Thus it is a good exercise to quantify the added-value of the

enhanced S&C analyzer/assessor for predicting FHQs.

The starting point is the Eclipse 500 Very Light Jet, a conven-

tional T-tail configuration. It carries 6 PAX with a 1300-mile range

at 370 kt max speed (Fig. 15(a)). The idea is to use CEASIOM to

determine whether drag savings can be achieved through uncon-

ventional design, and to propose a controller to handle its coupled

modes of motion.

The Tier 1 work carried out for the Z-wing has started

investigating some of the peculiarities of asymmetric aircraft,

Fig. 13. Comparison of optimized and baseline configurations obtained with CEASIOM for SMJ. (a) SMJ modeled in CEASIOM. (b) Plan view. (c) Front view.

Fig. 14. CEASIOM analysis of the existing SEJ configurationlow speed. (a) SEJ layout (left) and modeled in ACBuilder (right). (b) SDSA predicted angle of attack for trim.

(c) SDSA predicted elevator deflection for trim. (d) SDSA predicted phugoid characteristics against ICAO recommendations.



14/16

including its aerodynamic characteristics, the multiple trim set-

tings and the strong coupling between longitudinal and lateral

motions. The configuration analyzed was designed to be statically

unstable longitudinally, which needed to be accounted for by the

control system (Fig. 15(c)).

Two control techniques were used to design controllers for the

Z-wing aircraft. The first uses eigenstructure assignment to design

a state-feedback controller to stabilize and decouple the aircrafts

motions. A simulation of a stabilized non-linear model of the

aircraft showed that applying a pulse doublet to the flaperons

resulted primarily in a rolling motion, with the pitching motion

being smaller in magnitude (Fig.15(d) and (e)). Using Eigenstruc-

ture Assignment to design a state feedback controller it was

possible to significantly decouple the modes with comparatively

low gains of 1.08 or less. Potential benefits of the Z-configuration

include a reduction in drag due to absence of horizontal tail.

4.3.2. SAAB TCR TransCruiser

The objective of this DSE was to stress the CEASIOM software

in the nonlinear transonic flight regime [11]. Thus the specifica-

tion called for a 200 passenger airliner cruising at M1 0:97 and

high altitude. The baseline configuration that SAAB proposed

using its in-house design methods consisted of a conventional

mid-to-low-winged T-tail configuration with two wing mounted

engines. Ailerons and rudder are used together with an

all-moving horizontal tail for control. Flaps and slats are used as

high-lift devices. The landing gear is a conventional tri-cycle type

where the main gears are mounted in the wing. This baseline has

been analyzed and improved using the CEASIOM software. Poor

trim characteristics as well as a T-tail prone to flutter were

identified as problems on the original configuration. Thus, a

redesign to a canard configuration was undertaken. This resulted

in an all moving canard configuration. As discussed inSection 3.2,

a wind tunnel TCR model without engines was designed and built

by Politecnico di Milano. The model specifications were defined in

accordance with the dynamic testing in the T103 wind tunnel in

TsAGI. A 1:40 scale, ability to receive an internal balance, weight

constraint, interface with the wind tunnel were the main con-

straints put on the model design. The main geometrical para-

meters of the TCR model are as follows:

1. reference area: S 0:3056 m2;

2. wing span: b 1.12 m;

3. mean aerodynamic chord: c0.2943 m;

4. position of the center of gravity from the fuselage apex:

xCG 0:87475 m.

The most interesting quantity for the stability and control is

the pitching moment. The experimental results show that thereare two breaks in the pitch moment curve (Fig. 16). The first break

occurs at about a 81 and results in an increased slope of thecurve. The second break occurs at about a 201where the pitchmoment suddenly drops and then continues to grow again with

about the same slope. The VLM TORNADO does not pick up the

first break and change of slope in the pitch moment. The Edge

Euler results predict a change of slope but at a too high incidence.

The NSMB Euler results predict the first break very well, which

probably indicates that the Edge grid is not sufficiently resolved.

All RANS Tier II results predict this phenomenon well. The RANS

results differ in the vicinity of the second break though. Edge does

not predict the break at all, NSMB seems to predict it a bit early.

The best experimental agreement is obtained from the PMB

calculations. Figure also displays the x-component of the

Fig. 15. Stabilizing the asymmetric Z-wing configuration GAV. (a) Eclipse 500. (b) Eclipse 500 modeled in ACBuilder. (c) Plan view of Eclipse morphing to GAV. (d) Euler-

computed surface pressure on GAV. (e) Pitch, roll and yaw response to flaperon doublet input.



15/16

skin-friction distribution from EDGE in which a blue color denotes

negative values and flow separation. The flow separation on the

canard starts at its tip and leading edge and the separated area

grows with the increasing incidence. The onset of separation

occurs at an angle of attack where the normal force stops to grow,

at about a 221. There is a massive separation at a 261. Themain wing has mostly attached flow except for a small spot at

inboard span that reduces in size with increasing angle of attack.

There is a small leading edge separation at the outer part of the

wing that seems fairly constant with the angle of attack.

All of these Tier-II CFD results were put into the aerodynamic

database and analyzed in SDSA for its S&C characteristics. The

flight simulator in SDSA was used to check the stability of the TCR

flying in trimmed transonic cruise and then subjected to a wind

gust of large amplitude that alters its angle of attack by 3 1.Fig. 17

shows the flight simulation of the TCR. With stick fixed and no

augmentation the TCR responds by pitching up somewhat, but the

time histories of the oscillations in y and a do not damp out,instead they grow and the aircraft departs from controlled

flight

a nonlinear instability that must be handled. Adding

Fig. 16. Integrated normal force CN(top-left) and pitch momentCm (top-right) from RANS solutions by NSMB/CFS and EDGE/FOI for TCR TransCruiser; bottom: surface

skin-friction (x-component) distribution from NSMB, blue denotes reversed flow, M1 0:115, b 01, d 01. (For interpretation of the references to color in this figure

legend, the reader is referred to the web version of this article.)

Fig. 17. Nonlinear stability analysis in SDSA flight simulator, response to wind gust.



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stability augmentation to the flight control produces the time

histories shown in right half ofFig. 17that shows with augmen-

ted stability the oscillations in y and a are now damped and theTCR is stable to this nonlinear disturbance.

5. Concluding remarks

The paper has surveyed developments in the SimSAC Project and

the achievements reached at its termination. The CEASIOM software

enables the S&C analysis of the aerodynamic dataset generated

using the full range of its adaptive-fidelity modules for geometry,

aero-structural sizing and CFD tools appropriate for both low-speed

and high-speed flights. The stability-analysis results obtained from

its S&C modules offer an assessment of the computational methods

ability to compute the stability coefficients and derivatives to

sufficient accuracy for conceptual design. Six such DSE exercises

have demonstrated the functionality and utility of CEASIOM as a

tool for aircraft conceptual design. While the four individual modules

within CEASIOM may not represent any major advancement in their

respective discipline, it is the chaining of these modules into an

integrateddesign system of adaptable fidelity that is the new and

significant contribution of CEASIOM.

The SimSAC Project is now terminated, but the CEASIOMsoftware lives on. The software developers and stake-holders

are determined to continue developing and testing CEASIOM

through a coupled community-of-users approach that welcomes

outsiders to pitch in. More information is given on the website

www.ceasiom.com where even data like the TCR benchmark is

planned to be uploaded. So come join us for an exciting future.

Acknowledgments

The financial support by the European Commission through

co-funding of the FP6 project SimSAC is gratefully acknowledged.

Dr. Stefan Hitzel of EADS-MAS graciously provided the Ranger

2000 data in accessible form.

References

[1] Vos JB, Rizzi A, Darracq D, Hirschel EH. NavierStokes solvers in Europeanaircraft design. Progress in Aerospace Sciences 2002;38.

[2] von Kaenel R, Rizzi A, Oppelstrup J, Goetzendorf-Grabowski T, Ghoreyshi M,Cavagna L, et al. CEASIOM: simulating stability & control with CFD/CSM inaircraft conceptual design, Paper 061. In: 26th Intl Congress of the Aero-nautical Sciences, Anchorage, Alaska, September 2008.

[5] Mialon B, Khrabov A, Da Ronch A, Badcock K, Cavagna L, Eliasson P, et al.Validation of numerical prediction of dynamic derivatives: the DLR-F12 andthe transcruiser test cases. Progress in Aerospace Science, doi:10.1016/

j.paerosci.2011.08.010. This issue.

[6] Da Ronch A, Ghoreyshi M, Badcock KJ. Generation of aerodynamic tables forflight dynamics using computational fluid dynamics. Progress in AerospaceScience, this issue [see also AIAA Paper No. 2010-8239].

[7] Oppelstrup J, Eller D, Tomac MM, Rizzi A. From geometry to CFD gridsanautomated approach for conceptual design. In: Special session AIAA AFMconference, Toronto, 2010.

[8] Ricci S, Cavagna L, Travaglini L. NeoCASS: an integrated tool for structuralsizing, aeroelastic analysis and MDO at conceptual design level. In: Special

session AIAA AFM conference, Toronto, 2010.[9] Goetzendorf-Grabowski T, Mieszalski D, Marcinkiewicz, E. Stability analysis

in conceptual design using SDSA tool. In: Special session AIAA AFM con-ference, Toronto, 2010.

[10] Richardson TS, McFarlane C, Beaverstock C, Isikveren A. Comparison ofconventional and Z-wing VLJ designs using CEASIOM. In: Special sessionAIAA AFM conference, Toronto, 2010.

[11] Rizzi A, Eliasson P, Goetzendorf-Grabowski T, Vos JB, Zhang M, Richardson T.Design of a canard configured transcruiser using CEASIOM. Progress in

Aerospace Science, doi:10.1016/j.paerosci.2011.08.011.This issue.[12] Eliasson P, Vos J, Da Ronch A, Zhang M, Rizzi A. Virtual aircraft design of

transcruisercomputing break points in pitch moment curve. In: AIAA-2010-4366, 2010.

[13] Da Ronch A, McFarlane C, Beaverstock C, Oppelstrup J, Zhang M, Rizzi A.Benchmarking CEASIOM software to predict flight control and flying qualitiesof the B-747. In: Proceedings of 27th congress of the international council ofthe aeronautical sciences. ICAS 2010-5.10.1, 2010.

[14] Larsson R. Final reporting of WP6. SimSAC deliverable report D6.4-8. Stock-holm: Royal Institute of Technology; 2010.

[15] Tang CY, Gee K, Lawrence S. Generation of aerodynamic data using a designof experiment and data fusion approach. In: 43rd AIAA aerospace sciences

meeting, Reno, NV, AIAA-2005-1137, 2005.[16] Ghoreyshi M, Badcock KJ, Woodgate M. Integration of multi-fidelity methods

for generating an aerodynamic model for flight simulation. In: 46th aero-space sciences meeting, Reno, NV, AIAA-2008-197, 2008.

[17] Laurenceau J, Sagaut P. Building efficient response surfaces of aerodynamicfunctions with kriging and cokriging. AIAA Journal 2008;46(2):498507.

[20] Si H, Gaertner K. Meshing piecewise linear complexes by constrained

Delaunay tetrahedralizations. In: Proceedings of 14th international meshingroundtable, September 2005. p. 14763 /http://tetgen.berlios.de/S.

[23] Richardson T, Beaverstock C, Lowenberg M. Flight control system caseanalysis of the 747 using CEASIOM. Progress in Aerospace Science, this issue.

Further reading

[1] Isikveren A. Quasi-analytical modeling and optimisation techniques for trans-port aircraft design. Doctoral thesis report 2002-13. Stockholm: Department ofAeronautics, Royal Institute of Technology; 2002.

[2] Raymer DP. Aircraft design: a conceptual approach.4th ed Reston, VA: AIAAEducation Series; 2006.

[3] Goetzendorf-Grabowski T. Influence of stability derivatives on a quality ofsimulation (supersonic flow). Journal of Theoretical and Applied Mechanics1994;32(4):77391. Warsaw.

[4] Eller D. Mesh generation using sumo and tetgen. SimSAC Delivery report 2.3-5.Stockholm: Royal Institute of Technology; 2010.

[5] DASA-TN-R-R-002-M-0011RANGER 2000 FR06/RP01 aerodynamic datasetrelease 1.1, 1994.

[6] Goetzendorf-Grabowski T, Vos JB, Sanchi S, Molitor P, Tomac M, Rizzi A.

Coupling adaptive-fidelity CFD with S&C analysis to predict flying qualities.In: AIAA Paper 2009-3630, 2009.

http://www.ceasiom.com/http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.010http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.010http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.011http://tetgen.berlios.de/http://tetgen.berlios.de/http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.011http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.010http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.paerosci.2011.08.010http://www.ceasiom.com/http://www.ceasiom.com/http://www.ceasiom.com/http://www.ceasiom.com/

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