Associative Parametric CAE Methods

8/9/2019 Associative Parametric CAE Methods

1/11

Aerospace Science and Technology 9 (2005) 641651

www.elsevier.com/locate/aescte

Associative parametric CAE methodsin the aircraft pre-design

Christof Ledermann a,, Claus Hanske b, Jrg Wenzel c, Paolo Ermanni a, Roland Kelm b

a Centre for Structure Technologies, ETH Zurich, Leonhardstrasse 27, 8092 Zurich, Switzerlandb Airbus Deutschland GmbH, Mass Properties EGW, Kreetslag 10, 21129 Hamburg, Germany

c German Aerospace Center (DLR), Institute of Structural Mechanics, Lilienthalplatz 7, 38108 Braunschweig, Germany

Received 4 January 2005; received in revised form 26 April 2005; accepted 10 May 2005

Available online 15 June 2005

Abstract

Aircraft manufacturers are facing several challenges in the pre-design of aircraft structures. This early stage of the aircraft design has a

very multi-disciplinary character. Different competence centres need input data, which is at this point in time to a large extent undefined.

Therefore, a large variety of specialised tools is used in order to estimate and predict the required data. If these tools are not compatible,

interface problems are the consequence. A permanent improvement of the applied processes with regard to the informal value as well as the

applicability remains a continuous challenge.

The objective of a collaboration project between Airbus Germany GmbH, the DLR Braunschweig, and the ETH Zurich is to find new

methods and approaches to improve accuracy, efficiency, and flexibility of data prediction for primary aircraft structures. The use of modern

CAE systems together with the integration of finite element methods into the early pre-design process is a very promising approach [F. Bian-

coni, P. Conti, N. Senin, D.R. Wallace, CAE systems and distributed design environments, in: XII ADM International Conference, Italy,

57 September, 2001 [2]; M. Pellicciari, G. Barbanti, A.O. Andrisano, Functional requirements for a modern CAD system, in: XII ADM

International Conference, Italy, 57 September, 2001 [9]; T. Richter, H. Mechler, D. Schmitt, Integrated parametric aircraft design, in: ICAS2002 Congress, Institute of Aeronautical Engineering, TU Munich].

The modular and knowledge-based architecture of modern CAE systems allows to represent complex assemblies like aircraft struc-

tures by parametric associative and very dynamic models. Design knowledge can be integrated into the modelling [M. Mntyl, S. Finger,

T. Tomiyama, Knowledge Intensive CAD, vol. 2, Chapman & Hall, 1997 [8]] and different characteristics or individuals of the same structure

can be mapped through parameters.

This document presents concepts, which allow to design comprehensive digital models of novel aircraft structures whereas the level of the

modelling detail shall be variegated flexibly [D.E. Whitney, R. Mantripragada, J.D. Adams, S.J. Rhee, Designing assemblies, Res. Engrg.

Design 11 (1999) 229253 [11]; P. Aspettati, S. Barone, A. Curcio, M. Picone, Parametric and feature-based assembly in motorcycle design:

from preliminary development to detail definition, in: XII ADM International Conference, Italy, 57 September, 2001].

The strongly parameterised structures allow calculating and assessing different individuals of a given structure in a very efficient and

automated way. This makes parametric associative structures very suitable for optimisation.

After structural optimisation tasks have successfully been performed with parametric models [U.M. Fasel, O. Knig, M. Wintermantel,

N. Zehnder, P. Ermanni, DynOPS an approach to parameter optimization with arbitrary simulation software, Centre of Structure Technolo-gies, ETH Zurich; O. Knig, R. Puisa, M. Wintermantel, P. Ermanni, CAD-entity based evolutionary design optimization, Centre of Structure

Technologies, ETH Zurich, and VGTU, Faculty of Mechanics, Vilnius, Lithuania; U.M. Fasel, O. Knig, M. Wintermantel, P. Ermanni, Us-

ing evolutionary methods with a heterogeneous genotype representation for design optimization of a tubular steel trellis motorbike-frame,

Centre of Structure Technologies, ETH Zurich], multi-disciplinary optimisations are gaining importance, since they have the potential to find

global optima instead of the discipline-dependent optimal configurations and solutions.

2005 Elsevier SAS. All rights reserved.

This article was presented at the German Aerospace Congress 2004.* Corresponding author. Tel.: +41 44 632 31 47, fax: +41 44 633 11 25.

E-mail address:[email protected] (C. Ledermann).

1270-9638/$ see front matter 2005 Elsevier SAS. All rights reserved.

doi:10.1016/j.ast.2005.05.001


2/11

642 C. Ledermann et al. / Aerospace Science and Technology 9 (2005) 641651

Keywords:Aircraft pre-design; Parametric associative CAD models; Dynamic objects; Structural optimization; Multi-disciplinary optimisation

Fig. 1. Schematic representation of the dependencies between different dis-

ciplines.

1. Introduction

The strong coupling of different disciplines and physicalparameters leads to a relevant mutual dependency between

different competence centres. This dependency will be illus-

trated by means of the simplified scheme in Fig. 1.

In order to perform a structural design or calculation, theaerodynamic loads, which have to be supported by the struc-

ture, as well as some key measures like the wing span or

the length of an aircraft need to be known. For the purpose

of providing such information the aerodynamics on its part

needs to know the total weight of the aircraft and its alloca-

tion in order to calculate the required lift, wing positions, and

profiles. Obviously the mass can only be estimated when firstdesign ideas, structures, and key dimensions are available.

This multi-disciplinary dependencies are kept within

bounds for late development stages or for the design of

derivatives within a given aircraft family where a lot of mea-

sures are already known. However, the impact is crucial for

the pre-design stage, especially if novel aircraft are devel-

oped. Hence, the development of such novel aircraft is a

challenging task where improvements in the highly multi-

disciplinary pre-design phase seem possible.

This problem can only be solved using iteration processesbeyond disciplines or departments. Thus, it is very important

to have clearly defined interfaces between different disci-

plines that allow the exchange of reciprocatively required

data. In practice this is not always easy to establish because

different disciplines and competence centres have different

requirements and needs regarding simulation tools and data

formats.

A common parametric-associative geometry as a basisfor various simulation domains and different disciplines ispromising to overcome these difficulties in the aircraft pre-

design.

The geometry is supposed to be as generic as possi-

ble in order to allow all imaginable configurations. Design

knowledge can be integrated into the geometry. The para-

meters representing the different configurations as well as

some measures, simulation results and configuration assess-

ments are stored in a database. Structural analysis can then

be performed by loading or importing the components of

interest, meshing them, defining the loads and boundary con-

ditions, and solving the FE problem. The analysis can be

Fig. 2. Knowledge-based geometry as a basis for different domains.

conducted with the help of any specialised tool (Nastran,

Ansys, CATIA, . . .) supporting a geometry format that can

be exported by the common geometry system. Similarly,

CFD simulations can be performed using any specialized

CFD tool (Fluent, StarCD, CFX, in-house code. . .) that al-

lows meshing one of the supported geometry formats. Any

other software package, scripts or in-house code can use the

geometry for calculation.

All results of the different domains are fed back to the

database which can be used to modify and optimise the

geometry. Since the different domains use the same geom-

etry, information can even be exchanged between different

simulations. For example mesh-coupling tools like MpCCI

[5,12] allow to exchange data between very different meshes

based on the same geometry using interpolation algorithms.

Like this, multi-disciplinary optimisations can be performed.The following sections will describe and illustrate the

concepts and architecture of such a knowledge based para-

metric associative geometry model.

2. Hierarchical parametric associative aircraft model

Fig. 3 shows a hierarchical parametric associative aircraft

model. This model is a generic one and was built to illus-

trate certain concepts. It does not raise a claim to accurately

represent any specific aircraft type or model.The presented model was done with CATIA V5, which

is a modern CAE system with consistent object oriented

architecture. It allows parameterising geometry, associating

different components and building hierarchical assemblies.

Due to the open architecture there are a lot of possibilities to

automate processes and to integrate in-house code. Further,

CATIA V5 provides a large variety of features which allow

to integrate design knowledge into a digital mock-up. Some

of the used terminology may be system specific. However,

basically all presented concepts are of general character and

could be transferred to similar systems with equivalent func-

tionality.


3/11

C. Ledermann et al. / Aerospace Science and Technology 9 (2005) 641651 643

Fig. 3. Hierarchical parametric-associative aircraft model.

Fig. 4. Different fuselage cross sections. 1) ellipse, 2) blended double bub-

ble, and 3) spline.

2.1. Parametric CAD-models

Complex designs cannot be made in one step. It is nec-

essary to only represent the most important data in order to

evaluate different configurations. As soon as some key datalike fuselage length or wing span are fixed, the design can be

further detailed based on the best configuration (see 2.3.2)

[1].

To have the possibility to assess different configurations,

the digital model needs to be parameterised. If one parame-

ter is changed, the geometry needs to adapt to the changes

accordingly.

A crucial point for the efficiency of a parametric geom-

etry is the parameterisation. Often a trade off needs to be

made between efficiency and flexibility. This will be illus-

trated with the help of Fig. 4.

Designing fuselage cross sections in a geometric aircraftmodel, one needs to know what kind of cross sections should

be represented by the model. An easy way to design a cross

section might be to use an ellipse. Like this, three parameters

are sufficient to define the geometry one parameter for the

major and the minor axis each and a third one for the vertical

position. Due to the symmetry of the cross section it can-

not be moved horizontally. If the representation of an ellipse

is sufficient, this would be the most efficient way to para-

meterise for calculation or optimisation. However, it might

not be sufficient if the next aircraft would have a blended

double-bubble as cross section. Parameterising a blended

double bubble will need some more parameters but it allows

Fig. 5. Variation of the cross section of a constant fuselage section.

more flexibility. Most flexibility is given by a spline. How-

ever, defining a cross section by splines may be inefficient,

as basically two parameters are required to define the coor-

dinates of each point.

The above described problem can be solved in CATIA

V5 as follows. All possible cross section types are defined

in parallel and one parameter is used to define the currently

used type. A variable of type curve is used to take over

the geometrical solution of the used cross section type. This

solution works fine, allows a large degree of flexibility, but italso means some additional work to design and parameterise

all different cross sections in the beginning.

In general it can be stated that the geometry is supposed

to be as generic as required in order to allow all imagin-

able configurations of interest. But the labour of defining

the generic geometry increases by the number of parame-

ters. Furthermore, optimisations get very costly for a large

number of parameters.

2.2. Associative CAD models

Complex CAD designs like a whole aircraft cannot be de-

signed as one part. A huge amount of data, components and

parts have to be organised in a smart way. Basically these de-

signs can be split into parts and assemblies where assemblies

are built of several parts.

If a value changes in one part, this can have an impact to

other parts. For example, if the position of the wings should

be modified, it is not sufficient to send this information to the

electronic assembly of the wings since the fuselage section

with the wing box also needs to move, and depending on the

level of detail that is reached, it might be necessary to move

a lot of reinforcing elements.

An associative model allows interrelating different geo-

metric objects. If the position of the wings changes, alsothe wing box, the fuselage, and the fairing are automatically

adapted to the new situation.

2.3. Hierarchy and data flow

The associativity requires a clearly defined data flow. The

more associativities exist in a geometrical model, the more

vulnerable it gets to circular references. For example if a py-

lon should adapt its shape to the wings profile and at the

same time the locations of the wing ribs should be influ-

enced by the position of the pylon, this will lead to a circular

reference, which cannot be solved in general. This problem


4/11


Fig. 6. Schematic representation of a parametric associative CAD assembly

consisting of different components.

can be solved by hierarchical structures though. In the above

described problem a superior component could contain the

information of the wing profile and the position of the py-

lon. Both the wing and the pylon as two sub-components

would then retrieve the same information from one and

the same location, and both sub-components could adapt to

value changes.

Therefore, the digital mock-up is organised hierarchi-

cally, where each component can have sub-components.

A whole aircraft will exist of many components like wings,

fuselage, horizontal and vertical stabilisers, and so on. For a

first approach this might be detailed enough, but of course,

each component can be detailed further by creating sub-

components. Like this, a wing could contain ribs and a fuse-

lage will contain frames, stringers, and windows. The same

procedure can theoretically be continued down to the rivets.

Each component is organised in a similar way according

to Fig. 7. The design specification consists ofderived pub-lished parameters and internal parameters. The derived pub-

lished parameters represent a link to the inherited specifica-

tion parameters defined externally in the super-component.

This makes sure that all sub-components meet the basic idea

of the super-component. For example, if the whole fuselage

has a parameter to adjust the length, all fuselage sections as

sub-components need to refer to this total length. As soon

as the total length of the fuselage is changed by the para-

meter setting, the fuselage sections need to react. They may

react in different ways. All of the sections could keep a cer-

tain percentage of the full fuselage length, only one section

could adapt to the changes, or the number of sections couldeven be altered for big changes in length. All these possi-

bilities represent the design knowledge or intelligence of the

sub-components.

Internal parameters are used for the component-internal

use only. Of course, each component contains geometry.

This is what the CAD designers see on the screen and what

can be exported to other applications.

Although it is important to have a clear data flow top-

down in a hierarchical structure in order to make sure that all

components help to fulfil the global goals, it is not sufficient.

There is also some bottom-up information flow required

in digital mock-ups. Especially measured data like weight

Fig. 7. Composition of a single component.

could be aggregated on each level in order to have the in-

formation of the total weight available at top level. This is

done by Smart Data objects explained further down. Also

checks designed to verify if all requirements within a com-

ponent can be met, if limits are exceeded or if problems

occur, should send their exception information to the super

component.Components can also contain analysis datawhich is the

result of finite element analysis and helps to assess differ-

ent configurations. Besides optimisation cases, configura-

tion data, and other objects, the components also contain

published parameters or published geometry, which is vis-

ible for other sub-components. Published parameters and

published geometry are objects, which are referenced only

by their respective names. Given that, they allow exchang-

ing components with the same name definitions.

Considering a hierarchical geometric model that works as

described above and that aggregates the mass from the base

to the top, this would mean that the total mass of the aircrafton top is only known when the last rivets are designed in

the digital mock-up. Thus, a second organisational element

is introduced the organisation inlevels of detail.

As a consequence, there are now two organisational ele-

ments:

Organizational structure with components;

Levels of detail.

Both of these two elements are important and they have

a different task. They will be described in the following two

sections.


5/11


Fig. 8. Organisational structure of hierarchical aircraft model with compo-

nents.

Fig. 9. Organisational matrix of a hierarchical geometry model.

2.3.1. Organisational structure with components

Organising a large and complex structure or assembly

into different components and sub-components leads to a

tree structure. This is very helpful if branches of a tree are

considered. The organisational structure allows to use con-

current engineering, to split responsibilities and to organise

access rights. Most of modern CAE systems support natively

the organisational structure with components. For large and

complex systems, concurrent engineering, a split of respon-

sibilities or the organisation of access rights is normally ad-

ministered by PDM systems.

2.3.2. Levels of detail

Levels of detail organise a parametric associative geom-

etry model across the organisational structure with compo-

nents. Together with the tree structure of components and

sub-components it leads to an organisational matrix.Each part of an assembly is assigned to a certain level

of detail. These levels can then be used for visualisation,

optimisation, estimation or calculation of any data. Fig. 10

shows the geometry of the same aircraft model at different

levels of detail.

For illustration, the first depicted level is a wireframe rep-

resentation of the aircraft. The second one shows the master

geometry and the third one contains some inner geometry.

Of course, a comprehensive digital mock-up will have more

than these three levels of detail.

The important point is that all three levels represent the

complete aircraft. They contain for example a mass estima-

Fig. 10. Geometric aircraft model with different levels of detail: (top) wire-

frame, (middle) master geometry, (bottom) including some inner geometry.

tion based on the available geometry. The third one might be

more accurate and more detailed. However, on the first level

of detail there is also a complete estimation made based onthe data available at that point in time. This has the big ad-

vantage, that measured and aggregated data like mass is not

only available at the very end of the pre-design.

The levels of detail are more in line with a real design

process of a large and complex project. At the beginning

there is only some key information about the final product

available. Nevertheless, an overview of the whole product

must be there. The further the project develops, the more de-

tailed the design gets, and the more accurate calculated or

estimated data will be. But at the same time the required

computational power will increase dramatically if some of

the main dimensions are changed.Levels of detail allow to efficiently modify geometry only

taking into account the geometry down to a certain depth.

Furthermore, it allows to integrate the concept of design

freeze in digital models. After certain key measures are fixed,

a design freeze can be made on a certain level. Like this,

sub-components one level further down can still be modi-

fied and optimised while their inherited input data will stay

fixed. This is crucial for optimisations since the required

computational power for optimisations mainly depends on

the number of free variables.

Levels of detail are of big importance to managerial ac-

tivities since they allow keeping an overview of the whole


6/11


aircraft at any given time. In contrast to the organisational

structure with components, this functionality is not natively

integrated in all modern CAD systems. PDM systems may

take over this functionality, but they complicate automation

or flexible optimisation processes.

A combination of the two organisational concepts allows

to perform tasks like the following ones:

Visualisation of the left wing with all its details;

Aggregation the mass of the fuselage at the level of de-

tail 5;

Minimisation the weight of a fuselage section by finding

the best values for stringer pitch, frame pitch, and shell

thickness, meeting some stiffness boundary conditions,

and taking into account the data at the level of detail 2.

This list of tasks should just give an idea and could be

continued. The third task mentioned will be described more

in detail in section [4].

2.4. Dynamic objects

Dynamic objects are a concept to improve parametric-

associative geometry. The concept of dynamic objects was

implemented with the help of Visual Basic scripts.

Fig. 11 will help to outline the idea of the dynamic ob-

jects.

Currently available parameterisation features can be di-

vided into different parameterisation levels.

Fixed models represent the lowest level of parameteri-

sation. This means that certain geometry has an assignedproperty like for example a length. This length is saved in the

geometrical object itself, and in order to modify the value of

the property, the object needs to be opened and modified. In

fact, it may be argued whether this is parameterisation at all.

Parametersare used to define a geometrical property like

a length outside the geometric object. The parameter can be

associated with one or several geometrical properties. If the

parameter gets changed, all geometrical properties referring

to this parameter will be changed. Parameters are a kind of

persistent variables in a CAE system. They can not only be

Fig. 11. Schematic representation of different levels of parameterisation.

used to be assigned to geometrical properties but also to save

any kind of information using different data types.

Formulasinterrelate different geometric properties or pa-

rameters. If one parameter or property is changed, the related

one will change as well according to the predefined rule

of the formula. Formulas represent a possibility to integrate

knowledge into a geometric model.Object Patterns,PowerCopiesorUDFs(user defined fea-

tures) are more sophisticated possibilities to integrate knowl-

edge into geometric models. The user just defines once how

a certain object looks like or how it is built. Patterns allow

then to instantiate exactly the same geometry dynamically as

many times as desired in user defined patterns. PowerCopies

and UDFs allow to apply the same design rules to a differ-

ent context. However, patterns as well as UDFs have certain

disadvantages. Patterns instantiate dynamically, but all in-

stances of patterns together are just one object and cannot

be modified independently. On the other hand PowerCopies

and UDFs are context dependent, and each instantiation rep-resents an object on its own. Unfortunately, they are not

instantiated dynamically.

Dynamic Objects are a concept that combines the advan-

tages of both, the dynamic object patterns and the context

dependency as well as the real object instantiation of Pow-

erCopies or UDFs, respectively. The next two sub-sections

will explain why this concept is needed and how it can be

implemented.

2.4.1. Importance of dynamic objects

In aerospace engineering a lot of repetitive structures can

be found. Frames, stringers, ribs, and windows are someexamples. There are always groups of such repetitive struc-

tures, where each single individual is designed in the same

way. However, due to the different context the individual will

look different. In the early pre-design phase all frames may

be designed in the same way and all of them look similar.

This means a lot of repetitive work for the CAD engineers.

Nevertheless, the different frames are different due to the

context, i.e. fuselage cross sections at different locations. If

patterns are used, the resulting geometry will just be copied.

As can be seen in Fig. 12 this leads to an undesired result.

UDFs are context sensitive and will not lead to this prob-

lem. Furthermore, each UDF represents an object on its own

and can be modified independently.But if the length of a fuselage section needs to be modi-

fied or if the frame pitch has to be changed, the supernumer-

ary frames need to be removed or missing frames have to be

Fig. 12. Fuselage section with a pattern of frames.


7/11


Fig. 13. Implementation of dynamic objects for the variation of the number

of frames and stringers.

created, respectively. This means a lot of work and is not in

line with the concept of associativity.

To solve this problem, Dynamic Objects were suggested

and implemented to show their basic functionality. The fol-

lowing sub-section explains how these objects were imple-mented.

2.4.2. Implementation of dynamic objects

A fuselage section was created in CATIA V5, and among

a lot of other parameters there are two parameters, which

define the number of frames and stringers. As soon as

one of these parameters gets changed an event is fired. In

CATIA V5 these events can be intercepted by so-called re-

actions (knowledge ware objects). The short VB script at-

tached to the reaction executes then the required sub rou-

tines and functions of a more comprehensive Visual Basic

library.

These routines adjust the positions of the existing frames

and stringers, remove the supernumerous ones, or create

new ones if needed by instantiating UDFs from external

files.

This procedure proved to work fine, and the number of

frames or stringers can just be handled as parameters, which

is very much in line with the parametric associative philos-

ophy. The implementation of these automation routines was

even an indispensable prerequisite for the performed optimi-

sation described in Section 3.

Unfortunately, it is time-consuming writing such proce-

dures and if other objects should be instantiated dynamically,

some coding work would have to be done again. Further-more, the routines run quite slow due to the uncompiled

scripting language. Because this feature is of general use and

can save a vast amount of time, it would be of great benefit to

have this functionality implemented by the software supplier

in a compiled and, thus, faster language.

2.5. Weight estimation

Hierarchical parametric-associative geometry models

need to have a clearly defined data flow, as it was described

in the previous sections. In contrast to the design process

and the main data flow, which is always from top to bottom,

there is some information flow needed to go the opposite

way. This holds mainly for all measurements which have an

informal character for the top levels of the design or which

have to be aggregated.

Mass estimation is one of these examples. Because the

weight is a very important factor for the efficiency of an air-

craft, it is important to have a good weight estimation of theaircraft at each point in time of the pre-design.

Since mass estimation in parts has a different character

than mass estimation in assemblies, the two cases will be

discussed separately. The following sections are referring to

the current situation in CATIA V5.

2.5.1. Mass estimation in parts

If a geometric model is designed as solid, the mass is al-

most calculated for free. One only needs to assign a material

to the part whose total volume is automatically calculated.

If a surface model is used, there is some minor labour

to be done. Areas as well as curve lengths can be measuredand kept as parameters. Using simple formulas to multiply

by predefined thicknesses or cross section areas are an easy

way to get to mass measurements. If the number of elements

changes, a script needs to make sure that all changes are au-

tomatically taken into account for the mass estimation.

In a demonstrator model, a fuselage section with a vari-

able number of stringers and frames, the lengths of all these

objects are measured each time the number changes. This is

done by a Visual Basic script for automation.

The native algorithms for curve length, area, volume or

mass estimation are very efficient. Correction terms and fac-

tors for geometry not incorporated in the model can be taken

into account by knowledge-based features and scripts.

Therefore, modern CAE systems are very suitable for

directly deriving and estimating masses from parametric-

associative geometry.

2.5.2. Mass estimation in assemblies

In assemblies, the weight is not directly derived from

geometry, but it needs to be aggregated in a smart way. This

section presents the concept ofSmart Dataelements, which

allow aggregating calculated, measured and estimated data

like mass. This concept is not implemented in the current

version of CATIA V5.

Smart data elements are a combination of parameters andreactions. They can be defined as formula depending on any

other Smart Data. Additionally, they have the capability to

inform others.

On each level of detail of an assembly there is a Smart

Data element, which aggregates estimated and calculated

masses. As soon as one value has changed, the element in-

forms the one of its superior assembly. Instead of updating

all masses of all assemblies at all levels of detail after one

minor part has changed its weight, only the affected parts

will be informed and updated from bottom to top. This up-

date mode has major performance advantages for large as-

semblies compared to conventional update.


8/11


Fig. 14. Conventional updated compared to update with Smart Data ele-

ments.

Additionally Smart Data elements are capable of esti-

mating rather than just aggregating. They use the lower and

upper limits of parameters. Aggregating the possible upper

and lower deviations would lead to conservative results. In-

stead of doing so, they use the theory of errors to retrieve

reasonable estimation bounds. The ongoing implementation

of Smart Data elements is done with Visual Basic.

2.6. Costs of parametric associative models

In a first phase of a project it is much more costly creatingparametric-associative geometry models than creating con-

ventional models, because all the design knowledge needs

to be defined and integrated. Parameters need to be set and

related to others, possible rules and checks have to be de-

fined, and automation routines need to be written.

On the other hand, this process fosters the conceptional

thinking and forces the designer to think about which part

will be dependent on which parameters or other parts.

The further the project gets and the more complex a

design is, the more parametric-associative models pay off.

A change of basic parameters like wing span at a rela-

tively late stage of the pre-design phase would be disastrouswith conventional design. Depending on the quality of the

parametric-associative model, such big changes could lead

to a major computational effort only.

Compared to conventional design, parametric-associative

design requires more involvement of CAE models in the

early stages of the project like the feasibility phase and the

concept phase. This will lower the overall design costs and

development risks. It will also result in a more balanced cost

distribution during the aircraft design process [10].

Nevertheless, one needs to weigh up how comprehen-

sive the parameterisation of a model should be. Basically,

a model should be as much parameterised as required to

map all possible configurations that might be needed. It is

not necessarily the aim to parameterise as much as possi-

ble.

Fig. 15. Load case with boundary conditions.

3. Optimisation of parametric models

Parametric associative geometry models allow efficient

calculation of different configurations. This is the first step

towards optimisation. This section explains how optimi-

sation can be performed based on parametric associativegeometry models.

As an example, a fuselage section shall be optimised. The

optimisation task can be formulated as follows.

The weight of a fuselage section has to be minimised by

changing the number of frames and stringers as well as the

thickness of the shell meeting some minimum stiffness re-

quirements regarding bending and torsion.

In order to solve this problem it will be formulated more

precisely. The objective of the optimisation is to minimise

the weight of the fuselage section. The objective function

can be written as

minf (

x)

wheref (x) is the weight of the fuselage and x is the vector

of free parameters. The free parameters are

x1 number of frames

x2 number of stringers

x3 thickness of the fuselage shell.

Without any stiffness restrictions, the optimisation would

obviously find a minimum for zero frames, zero stringers and

a shell thickness of zero. Therefore, the following boundary

condition is defined as stiffness restrictions.

maxu(x,T ,F) umax

whereu is the total displacement at the node with maximum

displacement,T is a given torque to define the torsional stiff-

ness, Fis a given force to define the bending stiffness and

umaxis a given limit for the maximum displacement.

In order to retrieve a maximum displacement u for the

given loads F and T , a complete finite element analysis

needs to be performed. This analysis was done in Ansys be-

cause automatic meshing in batch mode with shell elements

and a variable number of geometrical objects is not possible

to achieve with release R13 of CATIA V5.

The given optimisation problem deals with discrete de-

sign variables and optimizing complex geometrical models


9/11


Fig. 16. Schematic representation of the optimisation procedure.

Fig. 17. Displacement representation as a result of a finite element calcula-

tion in Ansys.

Fig. 18. Different individuals of an optimisation.

of real-world structures often leads to non-convex problems.

These problems are difficult to solve with mathematical ap-

proaches like gradient methods. Thus, the optimisation was

performed with the help of an evolutionary algorithm using

DynOPS [3].

Fig. 16 shows a schematic representation of the whole

optimisation loop. Starting point of an optimisation is the

geometry as depicted in the left upper edge. With the help of

a Visual Basic script the CATIA V5 geometry can automat-

ically be exported as model file. With an APDL script the

geometry will be imported into Ansys. The defined loads FandTare applied to the structure as shown in Fig. 15. Since

the number of frames and stringers is variable, the force and

the torque need to be equally distributed to the correct nodes.

After the mesh and the loads are defined, the solution of the

FE problem can be started. It will yield a displacement dis-

tribution as shown in Fig. 17

As can be seen in Fig. 17, maximum displacements can

be found between the stringers. The maximum displacement

is used for further optimisation. If the displacements exceed

the allowable limit ofumax, the optimisation routine needs

to increase the number of stringers, the number of frames or

the thickness of the shell.

The total weight of the fuselage section is calculated in

CATIA V5 and used as input for the evolutionary optimisa-

tion algorithm.

4. Multi-disciplinary optimisation

4.1. Problem statement

As stated in the introduction, optimisation of geometry

regarding one discipline does not necessarily yield the opti-

mum for a coupled multi-disciplinary problem. There may

be different reasons why coupled multi-disciplinary optimi-

sations are not established in todays standard development

processes, although they would be very beneficial for the air-

craft pre-design phase [6].

One of the major reasons is that different disciplines have

different needs and different approaches and, as a conse-

quence, developed their specialized tools over the past fewdecades.

This section illustrates with the help of an aeroelastic

coupling example how two different disciplines can be com-

bined without depending much on the approaches and tools

of each discipline. Furthermore it shows, how important it

is, to have one common geometry basis for different disci-

plines. It also outlines that automated optimisation of such a

multi-disciplinary problem is only possible with the help of

parametric associative models.

4.2. Example: aeroelastic simulation

A parametric sailplane wing shall be optimised taking

into account aero-elasticity. Modifying the sweep angle or

the profile of the wing will influence the aerodynamics as

well as the structural aspects. Generally, the leaner a pro-

file is the better it is for aerodynamics due to the reduced

drag. On the other hand, very lean profiles lead to in-

creased stresses and strains in the structure, which need to

be compensated by additional structural elements of a cer-

tain weight.

For structural analysis, an equally spread and fine rectan-

gular mesh on the structure itself is ideal to retrieve accurate

simulation results.

If the wing shall be used for CFD simulation, the needsare very different. The mesh in the structure is not important

at all, but the mesh around the structure is needed. The CFD

mesh should be fine close to the wing and can be coarser at a

certain distance from the wing. While the structural meshes

for surfacial structures are ideally made of shell elements,

the CFD analysis needs volume elements.

Some approaches try to define meshes which can be han-

dled by both kinds of applications. However, this will always

be a compromise. Neither the results of the CSM simulations

nor the ones of the CFD simulations will be ideal.

Therefore the idea of coupling independent and spe-

cialised software is much more promising. A necessary pre-


10/11


Fig. 19. Parametric CAD model in CATIA V5.

Fig. 20. Coupling of two different FE meshes.

requisite to do so is that both simulations use information

based on the same geometrical model.

Meshes can be derived in domain specific software based

on the common geometry. The simulation results of one

mesh can then be mapped to a mesh of very different char-

acter using interpolation algorithms.

MpCCI (Mesh-based parallel Code Coupling Interface)[5,12] is a coupling library that is capable of doing this in-

terpolation called neighbourhood search. It was developed

at Fraunhofer Institute SCAI.

Aeroelastic simulations of a sailplane wing by means

of MpCCI have successfully been realised at the Centre of

Structure Technologies coupling Fluent and Ansys [6].

4.3. Aeroelastic optimisation

Due to the above mentioned reasons aeroelastic simu-

lations are not yet very established in aircraft pre-design

processes. Aeroelastic optimisation goes even one big stepfurther. In order to achieve a smooth optimisation process,

the geometrical model needs to be fully parametric and as-

sociative in order to quickly modify a complete assembly.

Furthermore, all involved meshing routines and simulation

tools need to be run in batch mode.

After structural optimisation as well as aeroelastic sim-

ulations have successfully been performed with parametric

models [3,4,6,7], multi-disciplinary optimisations are gain-

ing interest since they have the potential to find global op-

tima instead of the discipline specific optima. Efforts will be

made to use evolutionary optimisation algorithms also for

multi-disciplinary optimisation.

Fig. 21. Coupling of two independent and domain specific simulation pro-

grams.

5. Summary

This paper illustrated how beneficial parametric-associ-

ative CAE methods are in aircraft pre-design. The knowledge-

based geometry can serve as basis for different domains like

structural analysis, computational fluid dynamics, and oth-

ers.

It could be shown that it is possible to build hierarchi-

cal associative structures with a well defined data flow in

order to avoid circular references. Besides the native organ-

isational structure with components it is important to have

levels of detail. The design process runs top-down the hierar-

chical structure and reveals more and more detailed geome-

try. While the most of the data flows top-down the structural

tree to make sure that requirements at top level are met at

any level of detail, some information also needs to informthe hierarchically superior elements. This holds mainly for

measurements or conflicts.

The concept of dynamic objects was presented, and the

feasibility of such objects is shown by implementation of a

parametric associative fuselage section with a variable num-

ber of frames and stringers. The main weakness of this script

based implementation is the required calculation time be-

cause the scripting language based on Visual Basic is quite

slow and cannot be compiled. The same concept could be

implemented much more efficient by the software supplier

itself or by using faster and compiled code.

Parametric-associative geometry is very suitable for de-riving masses from it. For solid models this is practically

obtained for free while calculating the mass of surface mod-

els can be obtained by minor efforts. Correction terms and

factors for geometry not incorporated in the model can be

taken into account by knowledge-based features and scripts.

It could be shown that parametric associative geometries

are very suitable for optimisation. For example structural op-

timisations are very important in aircraft design since they

allow finding configurations of minimum weight meeting

all boundary conditions like stiffness requirement or design

space restrictions. The example with the fuselage section

showed how such an optimisation can be implemented.


11/11


6. Outlook

After different concepts were successfully presented as

demonstrators, they will be implemented more detailed in

a hierarchical geometric-associative geometry model of a

generic aircraft for the pre-design.

Collaboration with software suppliers is essential to makesure that some of the concepts can be realised more effi-

ciently.

The concept of Smart Data elements is promising to have

major performance advantages in the mass estimation of

large and complex assemblies. The implementation of smart

elements is a further task for the future.

After structural optimisation tasks have successfully been

performed with parametric models, multi-disciplinary opti-

misations are getting interesting as well for parametric asso-

ciative models, because they have the potential to find global

optima instead of the discipline specific optima.

References

[1] P. Aspettati, S. Barone, A. Curcio, M. Picone, Parametric and feature-

based assembly in motorcycle design: from preliminary development

to detail definition, in: XII ADM International Conference, Italy, 57

September, 2001.

[2] F. Bianconi, P. Conti, N. Senin, D.R. Wallace, CAE systems and dis-

tributed design environments, in: XII ADM International Conference,

Italy, 57 September, 2001.

[3] U.M. Fasel, O. Knig, M. Wintermantel, N. Zehnder, P. Ermanni,

DynOPS an approach to parameter optimization with arbitrary sim-

ulation software, Centre of Structure Technologies, ETH Zurich.

[4] U.M. Fasel, O. Knig, M. Wintermantel, P. Ermanni, Using evolution-

ary methods with a heterogeneous genotype representation for design

optimization of a tubular steel trellis motorbike-frame, Centre of Struc-

ture Technologies, ETH Zurich.

[5] M. Hackenberg, P. Post, R. Redler, B. Steckel, MpCCI, Multidiscipli-nary applications and multigrid, in: European Congress on Compu-

tational Methods in Applied Sciences and Engineering, Proceedings

ECCOMAS 2000, Barcelona, 1114 September, 2000.

[6] F. Hrlimann, Multi-disciplinary optimisation of a sailplane wing,

semester thesis IMES-ST 04-114, Institute of Mechanical Systems,

Centre of Structure Technologies, ETH Zurich, 2004.

[7] O. Knig, R. Puisa, M. Wintermantel, P. Ermanni, CAD-entity based

evolutionary design optimization, Centre of Structure Technologies,

ETH Zurich, and VGTU, Faculty of Mechanics, Vilnius, Lithuania.

[8] M. Mntyl, S. Finger, T. Tomiyama, Knowledge Intensive CAD,

vol. 2, Chapman & Hall, 1997.

[9] M. Pellicciari, G. Barbanti, A.O. Andrisano, Functional requirements

for a modern CAD system, in: XII ADM International Conference,

Italy, 57 September, 2001.

[10] T. Richter, H. Mechler, D. Schmitt, Integrated parametric aircraft de-sign, in: ICAS 2002 Congress, Institute of Aeronautical Engineering,

TU Munich.

[11] D.E. Whitney, R. Mantripragada, J.D. Adams, S.J. Rhee, Designing

assemblies, Res. Engrg. Design 11 (1999) 229253.

[12] K. Wolf, B. Steckel, Mesh-based parallel code coupling inter-

face, Fraunhofer Institut SCAI, Institut fr Algorithmen und Wis-

senschaftliches Rechnen, GMD Forschungszentrum Information-

stechnik GmbH, ISSN 1435-2702.

Documents

Associative Parametric CAE Methods