van_dijk_et_al.

van_dijk_et_al.
Multidisciplinary Design and Optimization
Framework for Aircraft Box Structures
Reinier van Dijka,1, Xiaojia Zhaoa, Haiqiang Wanga, Frank van Dalenb
a
Flight Performance and Propulsion, Faculty of Aerospace Engineering, Kluyverweg 1, 2629HS Delft, The Netherlands
b Fokker
Aerostructures B.V., Industrieweg 4, 3351 LB Papendrecht, The Netherlands
1 Corresponding
Author. Tel: +31152782067 Fax: +31152789564 Email: [email protected]
Abstract
Competitive aircraft box structures are a perfect compromise between weight and price. The
conceptual design process of these structures is a typical Multidisciplinary Design and
Optimization effort, normally conducted by human engineers. The iterative nature of MDO turns
development into a long and costly process. Knowledge-Based Engineering can be used to
automate this process by capturing relevant design process knowledge, which is then re-used
inside a computer application. This research will introduce a parametric, generative box model that
has been developed using KBE techniques. The generality and rule-basedness of this model allows
for the automatic generation of a wide range of box configurations and variants, thereby enabling a
thorough exploration of the design space. Structural and price analyses tools have been coupled to
the box model to generate the required discipline-specific performance data. With the product
model and coupled analysis tools ready, the goal is to automatically optimize for minimum weight
and price without human intervention. The design of Gulfstream 650 rudder is considered as initial
use case, the first experiences of which are discussed in this paper.
1 Introduction
This paper presents the results of a cooperative research project between
Fokker Aerostructures B.V. (Fokker) and Delft University of Technology
(DUT). One of Fokker’s primary businesses is the design and
manufacturing of aircraft box structures (elevator, rudder, aileron and flap)
for a wide range of aircraft integrators. The development of such products
starts with the proposal phase, during which Fokker conceptually designs
an aircraft box structure according to customer requirements, most
importantly: a target price and target weight set by the customer. After this
process a bid is made. In the near future, Fokker wants to achieve more
optimal conceptual box designs during the proposal phase and complete
this in less time. To further increase competitiveness, Fokker’s desire is to
have a software framework that is able to automatically:
1.
2.
3.
come up with an optimal concept in 1 working day
predict weight within 10% of actual
predict recurring price within 10% of actual
To achieve this, DUT has developed a software framework that automates the conceptual design
process of aircraft box structures and optimizes these for price and weight. The framework is based on
Knowledge-Based Engineering (KBE) and Simulation Workflow Management (SWFM) as the two key
enabling technologies. Using KBE techniques, a parametric box structure model has been developed.
The KBE model captures and re-uses engineering knowledge to automatically generate the master
geometry of the box structure, but also discipline-specific model representations. The latter ability is
used to automatically derive three specific representations from the same master model, required to
perform FEM, sizing, price and weight analyses. SFWM is used to integrate all software tools into a
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single simulation workflow and automate its execution. Optimization algorithms are used to perform
iterative MDO studies considering price and weight concurrently.
The paper is structured as follows. Chapter 2 will introduce KBE and SWFM as enablers of MDO.
Chapter 3 will elaborate on the generative, parametric box model. Chapters 4, 5 and 6 treat the
structural (FEM, sizing), price and weight analysis processes, respectively. Chapters 7, 8 and 9 will
introduce the optimization problem more formally, the resulting simulation workflow and some
premature results of the overall framework for a Gulfstream 650 rudder component. The paper will
conclude with the most important findings and next steps.
2 Multidisciplinary Design and Optimization
Multidisciplinary Design Optimization (MDO) problems typically require numerous design iterations
and careful balancing between often conflicting disciplines. MDO takes time, money and needs
flexibility. Hence, the need for intelligent automation. Key to the success of MDO is parametric,
generative modeling techniques on product side and Process Integration and Design Optimization
(PIDO) techniques on simulation process and optimization side. The combination of KnowledgeBased Engineering and Simulation Workflow Management Software offers these capabilities, which
will be explained next.
2.1 Knowledge-Based Engineering
Traditional Computed Aided Design (CAD) systems are able to expose product parameters and the
external manipulation thereof through an Application Programming Interface (API). However,
parameterization is usually limited to a single product topology, making configuration changes hard
to perform during MDO studies. Moreover, parameterization is limited to geometrical aspects.
Today’s complex engineering designs, like aircraft box structures, require the automated analysis of
multiple configurations (leading to discrete variables) and multiple disciplines (not only geometry).
Knowledge-Based Engineering overcomes the CAD system limitation by capturing relevant
engineering knowledge that “teaches” the system how to automatically generate multiple product
topologies and inner-topology variations from high-level parameter inputs. Moreover, KBE allows for
the capture of other discipline-specific knowledge to derive structural, price and weight aspects. It is
not at all limited to the geometry domain.
In this research, the General-purpose Declarative Language (GenDL) was used as the KBE
platform [3]. GenDL is a generative application development system for creating web-centric KBE and
business applications. The GenDL language is aimed at the general design engineer/programmer,
enabling him/her to quickly express the design problem at hand in high-level, object-oriented terms,
while still allowing for full access to the functionality of the underlying general-purpose programming
language. The GenDL suite is an open platform that blends the power of (ANSI) Common Lisp and
NURBS-based geometry kernels, thereby providing both the geometric capabilities of a typical CAD
system with the expressiveness/flexibility of a full programming language. With GenDL, an intelligent
box model has been developed, which will be discussed further in section 2.3 and chapter 3.
2.2 Simulation Workflow Management
Next to KBE, Simulation Workflow Management software is a key ingredient to MDO. Since MDO
processes are typically simulation intensive, involving heterogeneous CAx platforms, SWFM software
takes care of workflow automation, including pre-processing, analysis and post-processing steps.
Moreover, most of the SWFM systems include various optimization algorithms that allow for
optimization studies. In this research, NOESIS Optimus has been used to develop the workflow that
integrates the central KBE application, PATRAN, NASTRAN and MS Excel. The optimization results
can also be post-processed and visualized in Optimus.
KBE and SFWM are key enablers of MDO, as explained in the foregoing sections. The combination of
both technologies allows for a fully automated software framework, known as a Design and
Engineering Engine. This will be discussed next.
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Figure 1: general Design and Engineering Engine (DEE) framework for aircraft. The
framework shown is adapted from [1].
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Figure 2: specific Design and Engineering Engine for aircraft box structures. This
framework instance has been used in the current research.
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2.3 Design and Engineering Engine
Figure 1 shows the Design and Engineering Engine (DEE), a framework conceived for solving MDO
problems. It is inspired by the typical system engineering process in which an optimal design is
derived from a set of requirements. It translates this philosophy into the software domain. At the heart
is a Multi-Model Generator (MMG) component. This KBE application generically describes a product
family (hence multi-model) using parametric, generative modeling techniques. Engineering rules are
captured inside the program code and take care of automatically generating a product instance from
several input parameters, the initial values of which are provided by the user or determined in the socalled INITIATOR component. The INITIATOR can be a DEE framework on its own and captures the
product model definition on a lower-fidelity meta-level. It derives an initial parameter set from the
top-level requirements that define the design problem. The MMG uses High-Level Primitives (HLPs)
as the main building blocks to generate a master (geometry) model. Capability Modules (CMs) are
functional, more procedural software artifacts that generate discipline-specific perspectives from this
master model. On the basis of these perspectives, CMs may generate input data for external analysis
tools or drive these tools through an API or over HTTP in case of web services. CMs can also hold
complete functionality to perform a disciplinary analysis autonomously, eliminating the need for
external tools. In this constellation, CMs turn into complete “analysis modules”. Finally CMs may also
update the master model on the basis of analysis outputs. Based on the outputs of the analyses that are
considered useful inside an optimization loop (“performance data”), a CONVERGER checks for
convergence of the solution. In case no convergence is achieved, a new iteration is set in motion by
calling the MMG with slightly updated parameter values. At convergence, the EVALUATOR checks
for compliance with requirements. In case of compliance, an optimal feasible solution has been
derived. However, without compliance a new design iteration should be initiated with different
requirements / constraints. The DEE approach is not limited to the aerospace domain, but applies to
the general engineering design field. Van Dijk et al. provide a good example of a DEE implementation
in the automotive industry [2].
The DEE instance used in this research is shown in Figure 2. The INITIATOR component is in
practice absent. However, it has been kept in place to illustrate the initial sizing strategy of the
structure. Chapter 4 will explain that initial thickness values correspond to a minimum thickness
laminate. The MMG component uses an HLP building-block approach to model a generic aircraft box
structure. Four CMs are developed to extend the MMG with FEM, sizing, pricing and weighing
functionalities. These are linked to three external tools, as described in chapters 4, 5 and 6. Finally,
Optimus takes on the CONVERGER & EVALUATOR functionality (chapter 8).
Figure 3: Aircraft Box Multi-Model Generator
3 Aircraft Box Multi-Model Generator
The Aircraft Box Multi-Model Generator is a parametric representation of aircraft box structures. This
KBE application is at the heart of the DEE (Figure 3). On the basis of the Outer Mold Line (OML)
geometry and high-level parameter inputs, this model can automatically generate a wide variety of
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inner structure configurations and apply continuous parameter variations within a chosen
configuration. Some high-level input parameters for the MMG are given in Table 1. These inputs are
mainly related to product configuration or geometry. The different objects in the table can be
considered the HLPs of the master (geometric) model, viz. the objects in the software from which any
box structure instance is assembled. The configuration related inputs are discrete by nature, e.g.
number of hinges, actuators, ribs and spars. Continuous parameters are available to, for example,
position structural elements or to describe panel thicknesses. It is important to realize that all inputs
may but do not have to be provided by the user, most values are defaulting on the basis of captured
engineering knowledge. These values then represent best engineering guesses or are derived from
standards. If defaulted values are not appropriate, it is always possible for a user (human or computer
agent) to override the inputs.
Table 1: selected set of input parameters for MMG related to geometric configuration
Objects
hinge line
actuator line
hinges
actuators
spars
main ribs
LE ribs
splice plates
skins, ribs,
spars,
brackets
rib flanges
spar flanges
Aspect
position
position
number
position
min. offset
number
positions
min. offset
number
position,
orientation
number
position,
orientation
min/max. pitch
number
position,
orientation
min. offset
max. pitch
number
position,
orientation
min. pitch
thickness
Default
classified
classified
4
rule-based
classified
2
rule-based
classified
2
((0
0)
(user user))
14
rule-based
Flight Direction
classified
rule-based
rule-based
Flight Direction
classified
classified
1
equally spaced
Flight Direction
classified
min. t laminate
number
direction
number
direction
rule-based
rule-based
2
rule-based
Comment
Two perpendicular offsets from the front spar.
Two perpendicular offsets from the front spar.
Minimum of 2.
A span-wise fraction (0-1) or offset (mm) along the hinge line.
The minimum distance between structural elements.
Fixed value of 2 actuators for G650 (customer requirement)
A span-wise fraction (0-1) or offset (mm) along the actuator line.
The offset to hinges and between each other.
Minimum of 1.
Two chord-wise fractions (0-1) at bottom and tip.
Any number is allowed.
Combination of span-wise fractions (0-1) and / or angles.
The minimum/maximum distance between structural elements.
Any number is allowed.
Combination of span-wise fractions (0-1) and / or angles.
The minimum offset from hinge-actuator brackets / closure ribs.
Maximum distance between LE ribs (stiffness of LE skins).
Any number is allowed.
Combination of span-wise fractions (0-1) and / or angles.
The minimum distance between structural elements.
These values are updated during the sizing step. The maximum
attainable value corresponds to the feasibly produced laminate with
maximum thickness.
Number of flanges is anywhere between 2 and 3 or 4.
Flanges can point inward out outward.
Fixed value of 2 flanges for G650.
Flanges can point in Flight Direction or opposite.
Table 2: selected set of input parameters for MMG related to other disciplines
Objects
hinges
all structural
objects
connection
load cases
Aspect
type
material
Default
rule-based
classified
manuf. method
rule-based
assy. method
failure modes
rule-based
rule-based
Comment
standard, sliding or swiveling.
Various metallic / composite materials from a materials library with
different number of layers, stacking sequences and fiber orientations.
Various methods can be selected from a production method library.
Certain unfeasible product-production-method combinations are
excluded from the choice list in real-time.
Describes the full set of failure modes that can happen because of
aerodynamic pressure loading, imposed hinge displacements and
actuator jamming (and a combination thereof).
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Configuration or geometry related objects and parameters are mostly relevant for the master
(geometry) model. Various CMs inside the MMG take care of automatically generating disciplinespecific models from this master model. However, some these CMs bring new primitives and inputs,
of which Table 2 shows a limited overview with aspects from structures and production disciplines.
Besides these high-level input parameters, a great deal of model parameters consists of calculated
responses based on engineering rules.1 This may for example apply to flange widths, the number of
fasteners required to mechanically join a connection or the rib instance through which hinge loads are
transferred. A formal description of the master model and captured engineering knowledge are
provided in the next sections. More detail about the CMs is found in chapters 4, 5 and 6.
3.1 Product Model
The aircraft box model is object-oriented. The overall product is decomposed into classes with
attributes. Figure 4 shows a high-level UML class diagram with composition (“is part of”) and
generalization (“inherits from”) associations. The top-level assembly class is composed of an Outer
Mold Line (OML) that Fokker receives from the customer. The GenDL software is able to import
foreign geometry formats. For this work the STEP standard was used to transfer the OML geometry of
the G650 rudder. The OML forces inner structural elements to conform to its shape. Constrained by
OML dimensions, dedicated input parameters control the position and orientation of one or more spar
planes that cut the OML into two or more boxes. The model is assumed to always consist of a Leading
Edge (LE) box (either open or closed at the nose) and at least one center box. Several configurations
can be achieved. On the basis of two spar planes a 2-spar concept (like the G650 rudder) can be
created, in which case the model will also consist of a Trailing Edge (TE) box. Even more spar planes
can be used, resulting in a multi-spar configuration with multiple center boxes. All boxes can be
further equipped with structural elements. The LE box is composed of two or more hinges and two
actuators, each of which can be uniquely positioned along a hinge or actuator line, respectively.
Moreover, multiple LE ribs can be placed in the LE box. A center box consists of one or two spars and
several ribs. The TE box may have splice plates, dividing the overall box into one or several
manufacturable compartments. Spars and rib-like elements (LE ribs, main center box ribs and splice
plates) have flanges facing in one direction or facing both directions. The triangular splice-place is
usually a double-flanged machined part, resulting in a total of 6 flanges (hence, the cardinalities in the
UML diagram). Most of the objects in Figure 4 are further decomposed into other objects, however for
simplicity only top-level concepts are shown in class diagram. In practice, the hinge (bracket) is not
further detailed to consist of bolts, axle or bearings, because there’s typically no knowledge required
beyond an empirical total weight estimate and hinge actuation point in the conceptual design phase.
hinge
2..4
actuator
2
OML
1..*
spar-planes
LE-box
box
center-box
0..*
LE-rib
1
assembly
1
structure
spar
1..2
2..*
{ordered}
2
2
4
4..*
flange
6
rib
1..*
{1 LE wingbox
1..* center wingbox
0..1 TE wingbox}
1
FEM-specific
model
1
manufacturingspecific model
skin
TE-box
0..*
splice-plate
Figure 4: UML class diagram showing the structural breakdown of the aircraft box model
In GenDL-KBE lingo input parameters are called “input slots”, while calculated responses are called
“computed slots”.
1
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Table 3: various examples of engineering rules used inside the MMG for the G650 rudder
ID
A1
A2
Objects
actuators
actuators
Aspect
position
position
A3
LE ribs
number
position
A4
main ribs
A5
LE skin
number
position
number
dimensions
A6
hinges
weight
A7
hinges
dimensions
A8
flanges
width
A9
fasteners
number
A10
fasteners
type
A11
fasteners
type
A12
ribs, spars
geometry
A13
FEM
C1
hinges,
actuators
hinges
C2
skins
dimensions
C3
C4
splice plate
hinges
position
position
C5
main ribs
position
C6
spar flanges
orientation
C7
skins
C8
ribs, spars,
brackets
LE ribs,
ribs, splice
plates
skins, ribs,
spars
structural
integrity
structural
integrity
position
C9
C10
2
number
FEM
Rule2
Maximize horn arm.
First actuator is located at a fixed offset from middle
hinge, second actuator has fixed offset with respect to first
actuator.
Two ribs should surround each hinge or hinge-actuator
bracket, min/max offsets apply. However, when brackets
are close enough to each other, no intermediate rib is
required. If the distance between LE ribs is bigger than a
maximum pitch X, add extra ribs in between to stiffen the
LE skins.
One rib behind each hinge and actuator bracket and one
in front of each splice plate to transfer loads.
Each LE rib directly facing a hinge-actuator bracket will
divide the upper skin in removable panels in order to
allow for inspection of the brackets.
Empirical formulas are used to calculate weight. A total
fixed weight of X kg will be distributed over all brackets
according to empirical rules.
Crack growth analysis requirement: e/D ≥ X
Depends On
hinge line position
hinge positions
Minimum width is X mm for welded joints, for
mechanically fastened joints minimum edge distances
apply (double row of fasteners in case 3 parts connect).
At the end of each weld, so-called peel fasteners are
installed.
Usually the customer only allows for a limited set of
fasteners types, resulting in a limited fastener library.
The fastener selection rules depend on various aspects,
such as:
is the application area on the OML?
is the application area a high-strength area?
is the connection welded?
are connecting member metallic or composite?
etc.
Make a BREP intersection between the rib reference plane
and the OML.
model the elements as points in the FE model, use RBE2
elements to transfer loads into the rear-facing ribs.
Minimum number of hinges is 2, in order to meet with
fail-safe philosophy.
In order to allow for a welding process, the max. skin
thickness < X mm.
Don’t put splice plates in highly loaded areas.
Hinge brackets should have a minimum clearance of X
mm between each other.
Weld tooling requires rib spacing of > X mm for thin skin
areas and > Y mm for thick skin areas.
Flange-web  >90° to make sure product can be removed
from mound. Moreover, prevent clash with LE skins.
No skin is allowed to buckle below X% Limit Load (LL),
however thin skins are allowed to buckle below 100% LL.
Stable up to Ultimate Load.
assembly method
connection topology
Align ribs, LE ribs and splice plates in order to make
secure joints during assembly. This may also reduce the
number of fasteners considerably.
All generated FEM surfaces should comply with
maximum aspects ratio and skewedness values.
LE ribs, ribs, splice plates
hinges and actuators
positions
hinges, actuators and
splice plate positions
LE rib positions
N/A
N/A
assembly method
customer
location on structure
assembly method
customer requirements
corrosion protection
positions
OML shape
N/A
design philosophy
assembly method
internal stress
hinges
rib position, assembly
method
OML, spar position,
assembly method
internal stress
internal stress
N/A
Values marked X are classified
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3.2 Engineering Knowledge
The object-oriented decomposition of the product model is on one hand the formal result of the
engineering knowledge underlying the product and process. The result is a generic set of primitives to
model the entire design space for the problem under consideration. On the other hand it forms a
domain-specific language (or umbrella) for the capture of all other engineering knowledge. Figure 3
provides a semi-formal overview of some important engineering rules for the rudder use case. These
rules are mostly expressed in the form of parametric relationships between class attributes, creating
dependencies. Their scope might be bound to a single class, however they may also relate attributes of
different classes to each other traversing one or multiple association links. All engineering knowledge
inside the KBE application will either apply to the aggregation of the product (rules A10 and A11
define which types of fasteners to use) or the values of class attributes. However, it may either
manifest itself in a more assertive way or might constrain the feasibility range of certain values.
Assertions in this case are factual statements, like the “number of actuators is 2” or “one rib behind
each hinge or hinge-actuator bracket” (assertion A4). These knowledge artifacts are used at
instantiation-time; they define how objects should be instantiated and what attribute values they will
have. Constraints in this case are used after instantiation and are used to check if the resulting product
variant is feasible by complying with all requirements, for example “a box structure should always
have a minimum number of 2 hinges in order to meet with fail-safe philosophy requirements”. It may
have happened that a certain product instance might deviate from this through wrong user inputs or
automated computations, hence these constraints should be used to check the feasibility of the
instance after it has been created. Whenever possible, constraints should be converted into assertive
expressions used at instantiation-time. This will assure that only feasible solutions are generated.
Constraint C4 was actually converted into a set of assertive expressions that make sure that hinges
will never be placed too close to each other. This is sometimes impossible however, in which case
constraints are evaluated after instantiation during optimization. Hence, these sources of knowledge
will then be captured and re-used inside the optimizer. Product instances that violate constraints, will
then be discarded.
The rules in Table 3 are very diverse and may differ by application area or fuzziness. Most
rules either influence the number (A3, A9, C1), position (A1-5, C3-5, C9), orientation (C6) or
dimensions (A5, A7, C2) of structural members. Other rules define how geometry should be generated
(A12). Discipline-specific rules influence either structural (A7, C7, C8), FEM (A13, C3, C10), weight
(A6) or production (A8-11, C2, C5, C6, C9) related aspects. The process of knowledge capture and
formalization is not straightforward and typically consumes most of the time in KBE application
development. Only through iterative use / demonstration of the KBE application and validation of
results one can discover if all knowledge has been captured and if the rules are detailed enough.
Knowledge formalization is often more complex than initially expected on informal level or made
impossible by lack of preciseness. For example, A1 states that the actuator line should be positioned
such that a maximum horn arm results. To guarantee this, an extensive set of geometrical operations
was required to position this line as close as possible to the LE skin. The implementation of LE rib
positioning conform A3 resulted in a 100 lines of code algorithm with many conditionals. Constraint
C6 was not precise enough and needed another round of detailing, hence answering “when is the
angle exactly big enough?”. Or the logic underlying a rule might be too fuzzy. It was impossible to
accurately convert C9 to an assertive expression: what component takes on the leading role in rib
alignment? Does the main rib influence the positions of LE ribs and splice plates, or is it the other way
around? Sometimes rules may not end up inside the KBE application, but for example in the
optimizer. C1 defines the minimum bound of a variable in the optimization problem, while C6 and C7
define constraints that the optimizer should check. Not satisfying this, results in infeasible structures.
Rules are enablers of the KBE approach, however they also significantly limit the validity of
the application. For example constraint C7 (Limit Load requirements) renders the application only
valid for post-buckled concepts like the G650. Constraint C9 (rib alignment) is specialized to
mechanical fastening, the alignment of structural members may be a bad choice in case of welding.
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Assertion A6 defines the strategy of how to assess hinge (-actuator) bracket weights through empirical
rules. This may limit the accuracy of results and might require more sophisticated modeling strategies
for bottom-up calculations. One has to keep in mind that the model is as good or valid as the rules that
went inside. Traceability of engineering knowledge that goes inside a KBE application is of utmost
importance in this respect.
Finally, KBE application scope should be clearly agreed upon before implementation. Fastener
selection rules (A11) turned out to be so exhaustive that it would require extra extensive interviews
and knowledge capture sessions, while the benefits are not immediately clear. As a result, it was
decided to use a single averaged fastener type in the conceptual design phase.
4 Structural Analysis and Sizing
This section presents the structural analysis module that was developed in order to facilitate the
weight-price trade-off. Due to its tight coupling with the MMG, designers can directly obtain
performance results from high-level inputs and relate these back to the various design options they
explore in a conceptual design phase.
4.1 Strategy
It has been the goal of this research to increase the level of
fidelity of structural analysis in the proposal phase to
increase the accuracy of weight estimates. For this reason,
the MMG has been coupled to a FEM solver (NASTRAN).
The creation of the FEM-specific model has been fully
automated, by capturing relevant domain knowledge that
underlies these activities. While the actual meshing of the
geometry is “delegated” to the standard pre-processor of
the FEM solver (PATRAN), rules are used to subdivide
the overall geometry into easily meshable surfaces,
thereby
assuring
congruency
constraints.
From
computational performance standpoint, the sizing
Figure 5: structural analysis process in DEE
strategy was decided to follow a single-step approach. In
this approach, all surface thicknesses are initially set at their potential minimum; each value equal to
the thinnest laminate that can be manufactured. On the basis of this minimum-thickness model,
running loads are derived in the FEM solver. These results are then transferred to a Fokker in-house
developed sizing tool. This tool can determine the most appropriate laminate from a laminate library
that can sustain the respective loading pattern on the basis of panel dimensions and running loads.
The single-step approach is never 100% accurate as updated thicknesses / layers will influence the
structural behavior and ideally several iterations are performed. However, Fokker experience dictates
that differences are not significantly big and this strategy is adequate for proposal purposes. This
compromise obviously greatly benefits the computational performance.
4.2 Implementation
The structural analysis module is spread over two branches of the DEE, as shown in Figure 5.
Moreover, a detailed activity diagram is shows in the “FEM” and “sizing” partitions of Figure 6. Four
software components are involved: a CM for FEM pre-processing, PATRAN/NASTRAN as FEM preprocessor/solver, a CM for surface sizing wrapping around an Fokker in-house developed sizing tool.
4.2.1 Capability Module: FEM pre-processing
The CM for FEM pre-processing transforms the master geometry into a FEM-specific perspective with
appropriate geometry and object properties. This perspective conveniently allows for the automatic
generation of PATRAN input files that contain model pre-processing instructions and geometric data.
The surface subdivision unit extracts all surfaces from the master geometry and splits them
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FEM
generate
master geometry
Sizing
Weight
master geometry
in state n
master geometry
in state n+1
subdivide geometry
update
master geometry
subdivide geometry
assess
structural mass
MMG
parse FEM results
generate
FEM-specific model
write PCL files generate STEP file
PCL files
[no]
STEP file
.NET API
assess
non-structural mass
[yes]
map to
FEM-specific model
Price
derive
manufacturable
parts
derive
production
processes
derive
assembly
interfaces
derive
complexity
sized all
surfaces?
update surface
thickness / stacking
derive part family codes
retrieve price results
generate extended BOM
generate price report
generate weight report
import STEP
File to PATRAN
.NET API
.NET API
assign properties to panels
External Tools
.NET API
size surface
seed surfaces
mesh the geometry
derive cost
driving
parameters
TXT file
TXT File
collect
dimensions
collect
collect
cost drivers family codes
apply the loads and
boundary conditions
TXT file
collect CERs
configure the analysis job
run Nastran
calculate cost
import analysis
results to PATRAN
calculate price
Perf
generate running loads
analyze weight results
analyze price results
Figure 6: UML Activity Diagram with two-dimensional partitioning. Horizontal swimlanes relate to software components, vertical swimlanes to engineering disciplines
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into smaller ready-to-mesh surfaces. This transformation assures surface congruency and satisfies
aspect ratio / skewedness constraints. The process is visually depicted in the left branch of Figure 7.
The subdivision unit first separates the entire OML into boxes by intersection with all spar planes. It
orders these from LE to TE. Next, all rib-like parts are collected (LE ribs, main ribs and splice plates).
Although ribs may only exist in a single box, all their reference planes are extended over the entire
chord of the OML. In case ribs connect over different boxes, their reference planes are merged. Each
reference plane is extended in a direction that will assure maximum separation. For each box the ribs
planes are collected that intersect the box geometry and an assessment is made whether the plane is
physical (present in box) or virtual (resulting from the extension). Next, each box is recursively
separated into smaller solids (segments) by intersection with the rib planes. Finally, all solid faces are
collected into ordered lists. This collection of faces should only include physical faces and therefore
follows two filtering steps. First, all duplicate faces are removed in both chord-wise and span-side
directions. Second, top (tip) and bottom (root) faces of a segment are removed in case they were the
result of intersection with a virtual rib. The final collection of surfaces is ordered and a unique ID is
assigned to each surface for traceability. At this stage the master model has been transformed into
meshable FEM geometry and all surfaces are written into a single STEP file ready to be imported by
PATRAN.
Figure 7: two subdivision steps are required to derive the appropriate perspective for both FEM analysis (left)
and manufacturing / weight analyses (right). The red boundaries highlight the domain-specific segmentation.
Each surface in the FEM-specific model is not limited to geometrical aspects only, an associative link
with the product model is kept intact. Hence, each surfaces holds non-geometrical properties relevant
for analysis, e.g. material properties. Next, all non-surface like elements are collected, namely hinges
and actuators. Also these objects are ordered and uniquely numbered for traceability. Moreover, an
association is made between the hinge/actuator and the rear-facing rib surface into which the
introduced loads should be transferred.
The final step in the FEM pre-processor is the creation of a set of PATRAN instructions, using
the PATRAN Command Language (PCL). This part of the software is named “PCL-writer” and
contains a general set of utilities that can generate PCL code from high-level inputs. The PCL-writer
makes full use of the KBE system language to assess all information available in the FEM-specific
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model. The PCL-writer is a means to automate the PATRAN model set-up and meshing steps (Figure
8), but is also the means to re-assign the properties of finite elements inside PATRAN that would
normally go lost when only a STEP file is imported. The PCL-writer manifests itself in the creation of
several PCL files. The PCL writer component is generic enough for any GenDL-developed KBE
application to seamlessly link with PATRAN/NASTRAN. Wang et al. [10] re-used it for the structural
analysis of aircraft fuselage panels.
import
STEP file
seed
surfaces
assign
properties
configure,
solve
create
pressure field
assign
load case
mesh
create
hinges
elements
Figure 8: an execution example of the automated structural analysis
4.2.2 Model Generation and Execution
The rudder FE model is generated in PATRAN and analyzed in NASTRAN. The skin plates and
spar/rib webs are modeled as CQUAD elements, and the spar caps and rib flanges are modeled as
CBAR elements. RBE2 elements are used to simulate the load transfer from hinges and actuators into
the connected ribs. A pressure field is applied to the rudder skin to simulate the aerodynamic pressure
and an enforced displacement is applied to hinges to model the loads due to vertical tail bending. The
solver calculates the running loads for each surface for a multiple of load cases as a result of
aerodynamic pressure loading, imposed hinge displacements and actuator jamming (and a
combination thereof). In a manual human-driven modeling process, the following activities are
identified as repetitive work when making FE models:







importing geometry
assigning properties to the geometric entities
meshing the geometry
applying the loads and boundary conditions
configuring the analysis problem
submitting it to the analysis solver
post-processing the analysis results
All these activities are completely automated through the PCL file medium. Figure 8 shows the
automation process for the G650 rudder FE model. The output consists of all running loads around the
different skin surfaces, spar and rib webs.
4.2.3 Capability Module: sizing
The PATRAN results file generated through PCL automation is retrieved and parsed by the sizing CM
inside GenDL. This module maps running load results back to all surfaces in the FEM-specific model
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by matching of IDs. Next, an iterative connection is made to the Fokker in-house developed sizing
tool. To this end, a bi-directional Excel link is established between GenDL and Excel through .NET
libraries using the RDNZL package for Common Lisp [13]. For each surface, running loads and
dimensions are automatically written into the Excel sheet and the best laminate is retrieved from the
tool. All laminate properties like thickness, number of layers, stacking sequence and fiber orientations
are then mapped back onto the surface instance inside GenDL. While iterating over all surfaces, the
same software link is kept alive (and not continuously re-established), greatly improving computing
performance. Finally, the thickness results of all individual surfaces need to be mapped back to the
original master geometry model. The idealized thicknesses on surface level need to be translated to
the higher-level physical model. Therefore, the CM will update the original minimum thickness model
(instance n in Figure 6) in the master geometry with new thickness values (instance n+1 in Figure 6)
for all parts. The algorithm first adds thickness to the areas where strong ply drop-off rates are
encountered, and finally calculates a single volume-averaged thickness. For the first software
prototype this approach is deemed adequate. However, a next version might optimize all stacking
sequences to assure consistent interleaving solution and hence a minimum addition of weight. The
volume-averaged thicknesses are the starting-point for price and weight analyses.
4.2.4 Sizing Tool
A Fokker in-house developed sizing tool was used for each surface to find a laminate with minimum
thickness and associated stacking sequence that can sustain local failure modes, such as buckling and
static failure. The inputs for the tool are surface dimensions, compressive and shear loads. All
laminates in the laminate library have a fixed stacking sequence of [0 ±45 90] s, which is common
practice for box structure conceptual design. The sizing tool was validated by the NASTRAN 105
solver and is considered as a black box for sizing purposes.
5 Price Analysis
This section presents the cost and price analysis module that was developed in order to facilitate the
weight-price trade-off. The analysis has been developed particularly for aircraft box structures and
was embedded in the DEE framework. Due to its tight coupling with the MMG, designers can directly
obtain cost-price information from high-level inputs and relate the results to the various design
options they explore in a conceptual design phase. Moreover, KBE techniques, such as knowledge
extraction and analysis process automation, are intensively used for the price analysis, which largely
reduces the response time compared to traditional excel-based price estimations and establishes a
consistent and maintainable tool. Figure 9 shows the positioning of this module in the DEE.
5.1 Strategy
Typically, cost estimation methods are categorized into
two classes: top-down and bottom-up. The top-down
method delivers an overall estimate of a whole product,
which then can be broken down into its sub-components
according to either the product structure or an associated
cost breakdown structure. Rand corporation developed an
airframe acquisition cost model based on the parametric
relations associated with aircraft unit weight and speed in
the mid 70’s [6]. Raymer incorporated some modifications
on the Development and Procurement Costs of Aircraft
(DAPCA) IV model, although high level parameters were
still utilized for the cost estimation [7]. The bottom-up
Figure 9: price analysis process in DEE
method establishes detailed estimates based on individual
components or manufacturing processes, which can be then accumulated to provide the full estimate.
Other work of interest includes that of Hanffner who developed cost models for composite materials
w.r.t. manufacturing processes in 2002 [8] and Van der Laan implemented a process-based cost
estimation for aircraft movables [9].
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Top-down estimates are limited in that they cannot provide a detailed insight of a product cost,
whereas, bottom-up estimates can be incomplete as it is difficult to obtain all the required inputs at the
same level of fidelity from the conceptual design phase. As a consequence, a cost estimation facility
has been developed that is a compromise between top-down and bottom-up methods. It is able to
provide an estimate with greater accuracy than a top-down method and on the basis of a smaller
amount of parameters than would usually be required in a bottom-up approach. The estimation
facility follows a part family philosophy, in which manufacturable parts and assembly interfaces are
grouped in families according to their main characteristics. For each part family, Cost Estimation
Relationships (CERs) have been developed using statistical and empirical relations. Driving
parameters such as length, area, weight, part number are included in the corresponding part family
CERs, as well as other reference values. The overall price estimate is then based on associating CERs
to parts on the basis of their part family code and accumulating results. Of course, the entire set of part
families should be general enough to handle all variations in the box structure model. In this research,
a total of 258 part families have been derived from characteristics like size, shape complexity, material,
manufacturing method and in-house development / outsourcing. In addition, KBE techniques were
employed to capture all knowledge to automatically derive the aforementioned property values from
the geometric master model and high-level user inputs.
The price estimate is built up from recurring and nonrecurring cost and incorporates extra (profit) margins. A highlevel breakdown is shown in figure. 2. In this research, nonrecurring aspects are considered out of scope. The recurring
cost items that are most dominant and are grouped into
material and labor cost (thereby ignoring cost associated to
support, inspection, etc.). The material cost is derived from
material rate, part length, width, height, density and chipped
rate, as shown in equation 1.1. The labor cost is estimated
Figure 10 high-level price breakdown
based on manufacturing hours and labor rate, where the
manufacturing hours is derived from the driving parameters that help define the part family, see
equation 1.2. The rates for both material and labor cost vary over the different part families.
Cmaterial  rmaterial wlh n(1 rchipped )
(1.1)
Clabour  rlabour  h( x)
(1.2)
The price estimate is then a summation of recurring cost and includes an extra margin that is driven
by economic factors such as exchange rate, profit, surcharge rate, learning curve and General and
Administrative (G&A) expenses. In this research, a financial rate f based on factorization of the
recurring cost was defined to synthesize the aforementioned economic factors.
n
P   ri  C  f  C
(1.3)
i 1
5.2 Implementation
The price analysis module is a single branch of the DEE, as shown in Figure 9. Moreover, a detailed
activity diagram is shown in the “Price” partition of Figure 6. Two main software components are
involved: a CM for price analysis pre-processing and a Fokker in-house developed pricing sheet. The
MMG provides the master geometry that the CM pre-processes to enable an analysis. An extended Bill
Of Materials (BOM) is generated as input for the actual price calculation. The price is then calculated
within the analysis module and a final price report is generated.
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Figure 11: execution examples of rib part and rib-skin connection
5.2.1 Capability Module: manufacturing pre-processing
This CM integrates the parametric box model with the price analysis module. To support the analysis,
a manufacturing-specific model is required. Moreover, this CM is implemented in GenDL [3], while
the analysis module is an Fokker in-house developed Microsoft Excel workbook. Hence, a data
transformation is required that transforms the manufacturing-specific model properties into an Excel
worksheet. A total of four sequential pre-processing steps implement this transformation.
1. subdivide geometry
The manufacturing perspective should include manufacturable parts and all assembly steps
(Figure 11a). To this end, manufacturing/maintenance rules have been captured to derive this
perspective automatically. Figure 7 (right panel) shows that for the G650 configuration, the TE box
is divided into two skin panels. This knowledge is inferred from dimension constraints on skin
parts. Moreover, the fixed LE skin on left-hand side actually consists of several panels, due to
accessibility requirements to the hinge-actuator brackets. Besides parts, all connections between
parts are derived. Connection types include skin-rib, skin-spar, skin bracket, spar-rib and rib-rib.
2. derive properties
The individual part and connection properties, such as manufacturing/assembly process type,
shape complexity and cost driving parameter, are also derived from captured rules. For instance,
the manufacturing process is derived from the combination of part type (skin, rib, spar, and
bracket) and material. Similarly, the assembly process is derived from the connection type and the
combination of materials of each part in the connection. Figure 11b illustrates this rule-based
inference mechanism.
3. assign family code
In order to uniquely characterize a part or connection from a costing perspective, the properties
derived in the previous steps are condensed into an alphanumeric family code. Those symbolic
codes are used inside the analysis module to associate the part with a CER from the part family
library. The rules to transform general part properties into a part family code have been captured
inside the CM (Figure 11c).
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4. generate extended BOM
The previous steps create a set of knowledgeable parts and connections for each product topology.
This final step transforms all information into an Excel worksheet that represents the “extended”
BOM (BOM + cost driving parameters) and serves as input for the Fokker analysis tool. The same
Excel interface as described in section 4.2.3 was used for automation (Figure 11d).
5.2.2 Analysis Module
Once the extended BOM is written to the Excel environment, product cost and price are automatically
determined. For each BOM entry the part family code relates a part to CERs for material and labor
cost in the part family library. As input to the CERs, properties such as dimensions and cost driving
parameters are also extracted from the BOM. Finally, a cost aggregation is carried out for the entire
BOM and a total price estimate is generated that includes both cost results and economic factors.
5.2.3 Results Pre-Processing
The total price results are summarized on a dedicated worksheet in the Excel workbook. It doesn’t
only categorize results into recurring material and labor price, but also logically distributes results
over different family codes and production processes. Cost driving parameters are also shown,
providing insight in the most important sources of cost. In this way, designers will be aware of the
impact of their choices and adopt a design-for-cost philosophy during the proposal phase. To ease the
interaction with the optimizer and keep a single source of truth, the bi-directional GenDL-Excel
interface is used to extract the key price figures from the workbook (Figure 11e) and store everything
centrally in the product model. From there, a simple computer-readable TXT file is generated that
contains only price responses relevant for the optimization study.
6 Weight Analysis
The weight analysis module is a single branch of the DEE,
as shown in Figure 12. A detailed activity diagram is
shown in the “Weight” partition of Figure 6. Only one
software component is involved, namely a CM for weight
analysis. This CM depends on the geometry subdivision
unit introduced in section 4.2.1. The complete analysis
code was written inside GenDL turning this CM into an
“analysis module” inside the MMG. A “Class 2.5”3 weight
estimation method has been developed based on the
updated thickness model and extra empirical formulas.
All areas of skin panels, spar and rib webs are computed
in GenDL as well as the length of spar caps and rib
Figure 12: weight analysis process in DEE
flanges. The mass is then easily calculated by multiplying
the material density and surface area with thickness values from the manufacturing-specific model.
Since numbers of fasteners and fastener types are also explicitly modeled, their mass is calculated
bottom-up. For sealing and painting masses, average thickness assumptions are made. In combination
with exterior surfaces areas, the mass values can be assessed. Only the non-structural masses for
hinges brackets, hinge-actuator brackets are empirically assessed. At this stage, all weight results are
now also present in the product model, creating a single source of truth for all design information. A
simple computer-readable TXT file is generated that contains only weight responses relevant for the
optimization study.
Most of the weight assessment is done on Class III [5] bottom-up basis, however for hinge and
actuator weights, Class II [5] empirical formulas have been employed, rendering the overall weight
assessment method “Class 2.5”.
3
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The chain of operations described in chapters 3, 4, 5 and 6 is fully automated. Hence, when product
variations occur inside the MMG, analysis models are updated and price-weight calculations will be
re-executed. This ability to iterate enables optimization studies.
7 Optimization Problem
The objective of the optimization is to optimize the rudder design to reduce cost (maximize price) and
weight. The product focus is on skin panels (with varying thickness), ribs and spars and
hinges/actuators (as black boxes). The optimization problem deals with the overall topology (“master
geometry”) and the sizing of individual parts. Ideally, the objective function to be minimized in the
optimization is cost, while structural strength and weight are constraints. In case the weight and
strength constraints cannot both be satisfied, the objective function becomes a weighted function of
weight and price, while the strength constraint must in every case be satisfied.
Figure 13: graphical representation of the optimization problem
Figure 13 illustrates that the commercial constraint generally cannot be satisfied due to the challenging
levels of Target Price and Target Weight. In such cases, Cost is abandoned as the optimization
objective, and the Commercial Constraint becomes the objective function to be minimized instead. The
resulting problem formulation is given in equation 7.1.





minimize J  x   A  Price x1T , xT2  B  Weight x1T , xT3 , x  x1T , xT2 , xT3
x
subject to: gi (x)  0, i  1, ..., m
hi (x)  0, i  1, ..., p

(7.1)
Hard constraints which must always be satisfied are related to the structural integrity of the product:
Reserve Factor  1
(7.2)
Price will be estimated using, as a basis, the existing Fokker cost sheet. Weight is assessed using a
Class 2.5 estimation method. Reserve Factors are calculated for each finite element, every load case
and every failure mode. Failure modes include material strength, damage tolerance, bearing-bypass,
open hole tension etc. Local buckling and Euler buckling are not evaluated at element level, but at the
level of a web, skin pocket, super stringer or complete structural component using the Fokker sizing
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sheet. The major design question that the optimizer should be able to answer from a
price/weight/performance perspective is: “Is it more valuable to add/remove single ribs or
increase/decrease skin pocket thickness?”. Table 4, Table 5 and Table 6 give an overview of the most
important parameters, variables and constraints in the optimization.
Table 4: fixed parameters
Name
No. of spars
Initial panel thicknesses
Rib material
Spar material
Skin material
Actuator positions
Hinge line offsets
Actuator line offsets
Total hinge mass
Non-structural mass factors
value4
2
X
T300/PPS, ply thickness = X
T300/PPS, ply thickness = X
T300/PPS, ply thickness = X
X mm offset from root
X
X
X
X
Unit
mm
mm
mm
mm
kg
-
Table 5: optimization variables
Name
Type
Range
Unit
A (weight factor price)
B (weight factor weight)
No. skin panel plies
Ply layup method
Ply width
(ATL/AFP only)
Laminate library
Continuous
Continuous
Discrete
Discrete
Continuous
X
X
Min: X max: X
Hand layup / ATL / AFP
X mm – X mm
mm
Discrete
degrees
Discrete
Discrete
Continuous
Discrete
Discrete
Continuous
Continuous
Continuous
Continuous
Discrete
1
No. of center box ribs
No. of hinges
LE rib pitch
Rib thickness
Spar thickness
Rib positions
Rib orientation
Spar positions
Hinge positions
Hinge type: suppressed
Degrees Of Freedom (DOF)
Load case
1
Structural Configuration
Discrete
Set of n variables, i.e. fiber orientations for each next
ply added to the laminate
Range from X – X, step 1
2-5, step 1
Range from X-X
(X-X)
(X-X)
Root to tip of rudder (0-1)
Range from X-X, step X (angle)
(0-X) fraction of chord
Root to tip of rudder (0-1)
For each hinge type, 6 digits having values 0 (free) or
1 (suppressed) indicate DOFs which are suppressed.
Set of load cases for Air Loads plus imposed
deformations on hinge line to represent Fin bending.
Multi-rib
2
Structural Configuration
Discrete
1
2
Continuous
Discrete
2
Local buckling onset
Spar and Rib flange
orientation
Type of part tooling
1
2
Joint type
Joint type
Discrete
Discrete
2
Joint shimming
Discrete
Priority5
1
1
1
2
2
1
1
2
1
1
1
1
1
1
2
2
Discrete
Discrete
Multi-spar
Sandwich
Function of laminate thickness (% LL)
Fwd./Aft, Up/Down
Male (Inner Mold Line, IML) or Female (Outer Mold
Line, OML)
Bolted / Induction Welded
Single Step Assembly/ Welded/ Buttjointed/Bonded/Co-cured
Yes / No
mm
mm
mm
degrees
-
-
Values marked X are classified
The first version of the framework includes items with priority “1”, and the architecture should
support the later implementation of items with priority “2”.
4
5
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Table 6: optimization constraints
Priority
1
1
1
1
1
1
Name
Target weight / price
Reserve factors
No. of skin plies
panel thickness
Laminate Library
Ply drop-off rates
2
Tape layer constraints
2
OML Tolerance
Expression
A * Weight + B * Recurring Price < 1
RF > 1 (Note: applies to each load case and each failure mode.
n > n_min
constrained by manufacturing method
Local build-up of ply orientation according to Laminate Library
t / x < X
t / y < X
Ply contour shape to be manufacturable by specified tape laying machine
(if applicable)
Deviation of outer aerodynamic surface contour w.r.t. nominal
Unit
mm
mm
8 Optimization Workflow
In order to perform the actual MDO study, a simulation workflow is under development. NOESIS
Optimus has been selected as SWFM software. The current version of the OPTIMUS workflow is
shown in Figure 15. For convenience, it has been drafted in a way that closely resembles the
theoretical DEE schematic (Figure 2). Red dashed frames highlight different fundamental process
aspects. The overall workflow integrates three GenDL modules: the Aircraft Box MMG (MMG) and
capability modules that are responsible to create the appropriate perspectives for FEM and
manufacturing disciplines (CM1, CM2). The workflow drives PATRAN/NASTRAN for automatic
stress analysis (A1) and two other custom-made Fokker/GenDL applications for price/weight
estimates, respectively (A23). All GenDL applications are set up as web services, which means they
run on a remote server with internet access. This gives rise to a Service-Oriented Architecture, where
communication follows the HTTP REST protocol. File exchanges between GenDL and PATRAN occur
twice and use the SFTP protocol. The workflow starts
with an array of design variables, a limited overview
of which is given in Figure 14. Only for the first
iteration of the MDO study, a new rudder instance is
created in the MMG with appropriate inputs.
Subsequent iterations update that instance in realtime by changing parameter values. For each
instance, a feasibility check (F1) is performed to
check if a proper model was created. Since a great
majority of the involved constraints are geometric in
nature, it was a natural choice to evaluate these
constraints inside the MMG and let GenDL
communicate a single flag value back to Optimus. If
the model is feasible at this stage (flag = 1), the
workflow continues with a request to the structural
Figure 14: limited list of design variables
capability module (CM1) to derive the FEM
perspective and generate the related CAD geometry and PCL scripts. It transfers these to PATRAN,
after which PATRAN is executed (model set-up), NASTRAN is executed (solver) and PATRAN is
once more executed to generate an output report with stress results per FEM surface (A1). The results
are then transferred back to the MMG, which uses these to perform a one-step sizing method (chapter
4) and update all surface thicknesses and material compositions. At this stage, a second check is
performed to see if the rudder instance is structurally feasible (F2). Only if for each surface an
adequate material was found from the laminate library and all reserve factors are met, or in other
words if the rudder is structurally sound, the workflow continues. In this case a manufacturing
perspective (CM2) is derived (chapter 5) and customized modules automatically derive price and
weight estimates (A23) from this perspective. The final report files contain all important performance
data from which the output variables are bound. In case these outputs do not yield an optimal
solution, a new iteration is started with a different set of design variable values.
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MMG
F1
CM1
F2
CM2
A1
A23
Figure 15: Optimus workflow developed for this research.
9
Results
At this stage of the research, there are no MDO results yet. The delivery of the framework and
description of it, are therefore the major results in itself. However, with the development process in
hindsight, two important results can be derived from a business point and code complexity point of
view.
9.1 Business Case
Verhagen et. al [14] show that former KBE research rarely quantifies the savings resulting from a KBEenabled process. Moreover, development costs are never shown. This section will therefore present a
quantitative KBE business case for this research by comparing the expecting savings and actual
software development investments. The authors challenge future KBE publications to use a similar
concrete approach. In this way, literature can also clearly proof industrial relevance.
The goal of this research has been to develop a software framework that can automatically design
and optimize an aircraft box structure for minimum price and weight in the concept proposal phase.
The main benefit of the resulting level of automation lies in a reduction of engineering time spent on
repetitive work. In a human workflow, activities that normally qualify as repetitive work are CAD
model set-up, discipline-specific model creation (FEM, price, weight) and simulation execution. Extra
time is usually also spent on the search for, transformation of and distribution of data and
information. This software framework completely eliminates the needs for such wasteful activities
through workflow integration. The interaction between the new software framework and human
engineers, results in a shift of responsibilities. While the software framework now takes over the very
procedural tasks, the new role of design engineers is to be creative and come up with best guesses of
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initial model inputs, definition of problem objectives and constraints, to interpret final results and
respond with different design inputs.
The software framework affects the bottom-line by reducing the engineering hours required for
proposals. This reduction has a direct value in cost savings. Moreover, Fokker Aerostructures can use
the extra available time resulting from automation in two ways:
1.
2.
reduce lead time and take on extra projects, thereby increasing capacity.
perform more design iterations, resulting in higher quality end products.
The extra benefits associated to an increase in capacity or improvement of design quality, is complex
to valuate. This research will therefore only identify its presence and its potential, but will not try to
quantify its value. The value of cost savings on labor is a more reasonable and straightforward
calculation. The total value of cost savings is a comparison between business-as-usual and business
with software automation. It becomes a trade-off between the cost of software development and
maintenance vs. cost of labor. Some critical premises underlying the calculations are:
-
one proposal takes 12 Man Months (MM) on average
six proposals for box structures per year6
engineering labor cost 75€/hr. or 10k€/MM
The calculation of software development cost was based on actual measurements during the various
phases of the development cycle 7 , actual license costs and an estimate for maintenance. Extra
hardware costs are assumed to be absent (existing hardware is adequate). Important premises are:
-
software is developed in-house
programmer labor cost 75€/hr. or 10k€/MM
initial license costs at 60k€, with an 15k€ annual renewal fee (including support)
continuous software maintenance and improvements 25% of initial development cost
Man Months
5
4
3
2
1
0
duration
Specification
Capture
Structure
Implementation
Validatation
Deployment
0.5
2
4
4
2
1
Figure 16: software development cost
The final software framework is conservatively estimated to cut 50% on labor cost. As the annual labor
cost for proposals equals 720k€ for business-as-usual, yearly savings of 360k€ can be realized. Figure
16 forms the basis for the non-recurring software development cost of 135k€ (195k€ incl. 1st year
licenses). Recurring cost for updates and maintenance is estimated at 48.75k€ per year. Figure 17
provides the expenditure profiles of traditional and KBE-enabled design processes. Assuming the
software framework can be completed in one year (spanning six projects), the initial expenditure is
higher because of the development/licensing cost. After the first year, initial savings are realized and
Flaps are excluded from this estimate, because they are relatively more complex when compared to
rudders, elevators and ailerons for which the current software framework is assumed to be adequate.
7 All time spent on project definition and scoping, meetings, interviews and programming effort (by
three of the authors) have been accurately measured throughout the process.
6
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the break-even point is reached at termination of the 10th project or 20 months from the start of the
programme. A second scenario is shown where the software release process is phased. By targeting
different releases on focused aspects of the overall design process and with clever scheduling, earlier
savings can be realized. Assuming a linear savings profile, first gains are expected during the 7th
project or after 14 months. Total savings (phased) after 2, 3, 4 or 5 years are estimated at 0.51M€ [ROI =
109%], 0.87M€ [ROI = 197%], 1.29M€ [ROI = 269%] and 1.59M€ [ROI = 308%], respectively. The high
Return on Investment (ROI) justifies the initial risk of investing.
Total Expenses (M€)
2
1.5
1
180%
KBE
160%
KBE phased
140%
ROI
120%
ROI phased
100%
ROI
200%
traditional
80%
60%
0.5
40%
20%
0
0%
1
2
3
4
5
6 7
(Y1)
8
9
10 11 12 13 14 15 16 17 18
(Y2)
(Y3)
Projects
Figure 17: comparison of total expenses between traditional and KBE-enabled design
9.2 Programming Effort
This section will break down the total programming effort for the overall software framework on a
per-module basis. The specialization / categorization of which may help in providing a concrete, more
extensive insight into the most time-consuming aspects of developing an MDO framework and the
requirements that this poses for (next-generation) MDO software development platforms. Inspired by
Halstead’s complexity measure [12], programming Effort is defined here as the product of Difficulty
and Volume,
E  D V
(9.1)
In contrast to Halstead, both concepts are not rationalized on the basis of measurable software
properties (operations and operands), because the pure software focus is too narrow and does not
cover the knowledge engineering activities involved in KBE application development. With a lack of
alternative quantification schemes, Difficulty in this research is a subjective measure of the complexity
experienced during the complete software development process. It starts with problem description,
goes through formalization and ends with programming. Complexity during this research has
manifested itself in the number of iterations involved in a total programming effort. Many iterations
indicate that the formalization of the problem wasn’t straightforward. Typically, a procedural script
that reads something from or writes something to another data source is easily understood,
implemented and hence, perceived easy. On the other hand, an generic object-oriented decomposition
of a product model, is observed as a difficult task. The Volume measure is directly related to lines of
code. Logically, something that is both difficult and requires a lot of code, requires the biggest Effort.
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Figure 18: software complexity expressed on the basis of volume and difficulty
Figure 18 shows 16 elementary developments steps that were taken in this research. They are logically
grouped conform their discipline. The figure shows that especially the HLP definition process and
discipline-specific model generation required the highest efforts. The set of HLP primitives (1a, 1b, 1c)
has to result in a generic enough model, typically a highly iterative process. A KBE system language
should make it easy to model individual objects and associations between them. A language that is
high-level, object-oriented and that hides explicit typing can increase effectiveness. Both subdivision
steps involved numerous rule-based geometry manipulations (2a, 4a), where some debugging steps
where necessary to robustly handle geometry variations. Hence, a KBE system should build upon a
powerful geometry kernel. Moreover, a KBE system should have high computational performance.
Aspects like dynamic dependency tracking, lazy evaluation, runtime value caching and compilation
are imperative in light of computation time. These observations usually render CAD-centric or
MATLAB-like approaches useless. The generation of the discipline-specific perspectives (2b, 4b)
involved a lot of engineering rules (especially the manufacturing model). In this respect, it is
important that next generation KBE platforms support a model-driven software engineering
approach, in which engineering knowledge can be discovered and stored outside the software
implementation. This will capture knowledge independent of a KBE platform, increases transparency
and traceability. Automatic code generation might lower the perceived programming threshold. The
development of a Graphical User Interface (GUI) for this research has turned out to be a voluminous
activity, not per se a very difficult one (6a). KBE systems should provide high-level language driven
functionality to easily set-up a user interface and define how the user interacts with the product
model. In this research, GenDL’s General-purpose Web Language (GWL) package has been used to
create web interfaces for the MMG. The set-up of a simulation workflow (7) is difficult since it needs a
precise definition of the optimization problem. Once the definition is settled, Optimus makes the
software workflow development highly interactive, user-friendly and therefore a straightforward
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exercise. It must be noted that the already custom-developed Optimus-GenDL interface significantly
lowered development time. Finally, on the other side of the extreme are data translators and interfaces
with foreign data files or heterogeneous software tools (2d, 3a, 4d). Mostly because these scripts are
rather procedural in nature and not dependent on formal knowledge capturing, few iterations were
required. However, it was only straightforward because the GenDL platform is built upon a generalpurpose programming language. For such languages a vast set of 3rd party packages (often opensource) are available, like the RDNZL package that was used for Excel interfacing. Only a generalpurpose programming language based KBE platform can leverage these existing software
developments.
10 Conclusions and Future Work
On the basis of Knowledge-Based Engineering and Simulation Workflow Management is has been
shown that it’s possible to develop a software framework suitable for Multidisciplinary Design
Optimization of aircraft box structures. A KBE approach has been chosen over the traditional CADoriented approach due to the complexity of the product in terms of parameterization and the range of
configurations under consideration. Both product and process knowledge has been captured inside
the Aircraft Box Multi-Model Generator to automatically generate the master geometry of a box
structure and derive discipline-specific models from there. SFWM automates the software workflow
and turns the execution of the KBE application and FEM, sizing, weight and price analysis modules
into one seamless process. The next step is to run the actual optimization process. To this end, an
algorithm will have to be selected that can deal with a high amount of discrete variables and dynamic
updating of the optimization problem definition. When the first MDO results are produced,
comparisons will be made against actual G650 rudder design data. Finally, one or more updates will
be implemented until the original goals set out in the use case, are achieved.
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