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8/13/2019 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: rizzi@kth.se
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
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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).
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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
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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
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.
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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.
A. Rizzi / Progress in Aerospace Sciences 47 (2011) 573588586
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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.
A. Rizzi / Progress in Aerospace Sciences 47 (2011) 573588588
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