AMESim – Simulationsumgebung für Motorradentwicklung
Environment for Conceptual Design of Motorcycles Using AMESim
Elysé Botellé, Michel Lebrun
Kurzfassung
Wissenschaftsbasierte Berechnungsmethoden (FEM und CFD) haben sich historisch
gesehen ausgehend vom Geometrie orientierten CAD entwickelt. Diese Methoden
basieren auf der Geometrie des Materials und dessen Eigenschaften und eignen sich
gut zur Beschreibung lokaler Phänomene. Allerdings gestatten sie nicht die
Untersuchung des dynamischen Verhaltens eines aus mehreren verschiedenen
Disziplinen zusammengesetzten Gesamtsystems. Die Weiterentwicklung zu
automatisierten multi-disziplinären Systemen erfordert einen auf einer globalen
Sichtweise beruhenden Entwicklungsprozess. Diese Vorgabe führte zur Spezifikation
und Entwicklung von speziellen Simulationswerkzeugen als Ergänzung zu rein lokale
Effekte betrachtenden Lösungen. Inzwischen spielen diese Werkzeuge eine immer
bedeutendere Rolle in der Systemauslegung. Im vorliegenden Artikel wird die
AMESim-Umgebung mit der zugrunde liegenden Konzeption vorgestellt. Anhand
eines Beispiels wird gezeigt, wie die AMESim-Plattform an die Anforderungen bei der
Entwicklung moderner Motorräder angepasst werden kann.
Abstract
Scientific computation (FEM and CFD) has historically developed running on from
geometric CAD. These computations are based on materials geometry and
properties, they are well suited for describing local phenomena, however they don’t
allow to study dynamic behavior of multi-disciplinary systems as a whole. The
evolution of automated multi-disciplinary systems requires a design process based
on a global vision. This constraint leads to the specification and development of
adapted simulation tools, being complements to local approach. These tools more
and more play a critical role in systems design. The AMESim environment with its
underlying concepts are introduced in this article. An application example illustrates
how the AMESim platform can adjust to design issues raised by modern motorcycles.
1. Introduction
In the last three decades, mechanical engineering has experimented an
exponentially in-creasing integration of electronics and information technology [1].
This fact has allowed it to evolve considerably by displaying improved even new
functionality. The systems with such characteristics are usually referred to as
mechatronic systems. These systems are characterized by the integration and
interaction of different physical domains (automatic, mechanical, power fluid,
electronic, electromechanical, optic, thermal, thermodynamic…) and miscellaneous
engineering disciplines, such as control, software, mechanical and electrical
engineering.
Due to the diversity of tasks and miscellaneous teams involved, designing and
producing such systems is not easy. This difficulty is accentuated by the high
competition, and important time-to market, cost and quality constraints. The problems
of designing such mechatronic system can be represented by the well-known V-cycle
model [2][3][4] shown in Figure 1.
Marketing
specifications
RS
ON
Products tests
DTS
IG N
Components
Sub-system tests
Components tests
AT I
Integration tests
EG R
D ES
CFS
Operational
functions
Functions
system
Functions
Sub-system
INT
GTS
Customer
requirements
MANUFACTURING
RS= Requirements & Specification
GTS= Global Technical Specification
DTS= Detailed Technical Specification
CFS=Components Fabrication Specification
Figure 1: V-cycle for the design process of mechatronic systems
The design process is an appropriate combination of top-down (design and
development) and bottom-up (validation and test) processes oriented to meet the
customer requirements, while reducing time-to-market and cost. This V-cycle model
covers the whole life cycle of a product, from the early stages until the serial
production. During the design process represented in Figure 1, the different concepts
will be developed depending on the stage of the design cycle of the system, the
intermediary tests are then necessary to evaluate possible alternatives and finally
take design decisions by means of the physical prototypes. In general, before a
design can be validated, several recursions are inevitable, during which the design is
tested and modified until the requirement specifications are met. However, for a
mechatronic system depicted above, the extensive physical prototyping which results
from this approach is no longer feasible for the reasons of quality, cost and time-tomarket. Consequently, the extensive use of modeling and simulation within a virtual
prototyping environment is essential [5]. Especially on the early phase of the design
process, simulation models are strongly required for the verification of alternative
designs and parameterization because the recursions in the virtual prototyping
environment are fast and cheap. Once models fulfill the requirements specifications,
the first physical prototypes can be done with many fewer errors than in a classical
design process since most of errors have been corrected during the early phase.
2. Integrated design environment
In most of the cases, it is not possible to answer all the questions which arise during
the design process of mechatronic systems by means of one single model. Therefore,
different kinds of models are essential to fulfill the different design tasks and thus,
their corresponding CAE (Computer Aided Engineering) tools can be considered
independently from each other. As a consequence, it is indispensable to merge them
into an integrated design environment [6] [7], which is characterized by the following
fundamental specifications.
Openness:
Within an industrial context, the required design environment must not only allow the
performance of the different design tasks within a single company, it must also allow
the model exchange with customers and suppliers. therefore, in such a context, it is
essential to use commercially available tools and standards for model description
and exchange. In this sense, it is worth to mention the examples of different
standardization efforts, such as VHDL-AMS IEEE 1076.1 (1997), which is a standard
description language for digital and analogue electronics. Furthermore, the use of
standard facilitates the process of including new tools into design environment and
the CAE tools considered in such an environment must provide interfaces to allow
the coupling with other kind of tools, enabling the possibility to expand their
capacities or to exploit the results.
Interdisciplinarity:
The design of mechatronic systems requires an environment which supports the
modeling of components from different physical domains and their integration into an
overall system model.
Scalability:
It is important that the design environment supports not only modeling at different
levels of abstraction, but also the combination of models from different levels.
Reusability:
The use of well structured and modular libraries allows the reuse of models resulting
in a very considerable reduction of time and effort for the designers.
Portability:
The design of complex products is very often distributed among teams working at
different sites, in different countries. For this reason, the design environment should
be accessible via the intra- or internet. Furthermore, it should work on different
computer platforms (e.g. UNIX, Linux and Windows).
3. Models at different levels of abstraction
Since the different kinds of CAE tools are required to produce and run the
corresponding models in the above mentioned integrated design environment, it is
necessary to classify these models and simulations tools according to their roles
during the design process described by the V-cycle model. Because the design
process is a combination of top-down and bottom-up process, models and CAE tools
can be abstracted at different levels corresponding the different phase of design
process, as shown in Figure 2. In general terms, an abstract description of a system
provides few details about its components, but it covers a large scope and provides a
systemview at the corresponding levels, whereas the detailed models which are
capable of describing physical phenomena with great precision are abstracted to
single parts. In the four levels of abstraction shown in Figure 2, there is not always a
one-to-one correspondence between the modeling and design levels, the overlap can
be often found such as both network and geometry level models are used for the
design of components.
Figure 2: Modeling levels associated to the different stages of the design process
Functional level:
Modeling is used for specification, particularly in the area of electronics and controller
design. This task is usually carried out by the customers, together with the suppliers.
The description of the model occurs typically by means of so-called finite state
machines consisting of discrete states that evolve when a certain event is produced.
Commercial tools used for this modeling level are e.g. Statemate (I-Logix Inc. 2001),
Stateflow (Mathworks Inc, 1997).
System level:
Modeling describes the dynamic behavior of the overall system (controller and plant).
At this level, models are usually represented by block diagrams containing behavioral
parameters like gains, tables, maps, curves, time delays, etc. Signal flow
communication (input/output) among the blocks is supported (i.e. no energy
conservation is required.), this input/output form is the purely mathematical
representation usually with the “black boxes” or “gray boxes” as shown in Figure 3, a
electrohydraulic system modeled at this level, it includes a hydraulic pump, a
servovalve and a hydraulic cylinder. These models of different parts are described by
the blocks with some inputs on a side and some outputs on the other side. Models at
this level are mathematically described by means of ODEs (Ordinary Differential
Equations).
The principal editor software at this level is the Mathworks Inc which develops and
commercializes the software Matlab and Simulink. Another example of tools at this
level is ASCET developed and commercialized by ETAS GmbH, which concentrates
on the automobile applications such as engine controller, ABS system, trajectory
control etc. This software assures a continuity during the design process with his
real-time code generation capacity. Some others examples are Scilab (Inria), Octave
etc. Some of these tools support also the functional level modeling described in the
previous level, which in combination with the above mentioned continuous models
results in the so-called hybrid modeling.
QP
pP
P1
QA
P2
Q1
QB
Q2
Figure 3: Block diagram representation in Simulink
Network level:
Models at this level concerns a macroscopic or lumped parameters modeling
oriented to describing the overall dynamic behavior of particular components. They
consist of a network of elements (lumped parameters) containing physical
parameters (e.g. spring constant, mass, resistance, etc.) interconnected by means of
energy-conserving interfaces, which are named ports, the connection of these ports
highlights the energy exchange between a subsystem and its environment. The
property of the energy conservation is well known in the different physical domains,
e.g. Kirchhoff’s Law in the electrical domain or Newton’s Third Law in mechanics.
The representation called multiport of these elements, can be described in Figure 4,
an example of the hydraulic system. In each connection port, there are the power
variables, when two ports are connected, the relevant variables are imposed to be
equal.
Figure 4: Multiport representation of a hydraulic system
The network level is distinguished from the system level by the modeling methods
which base on the structuring of elementary models in order to assure an interface
with the energy conservation. This structuring capacity is associated to the fact that
the construction of the system network takes into account only the just necessary
elementary models which take an real energetic role in the system [8].
The basic theory is the bond-graph [9][10], which provides a unified description of the
energetic, and thereby dynamic, behavior of mechatronic system. In the most general
case, these models are mathematically described by a set of DAEs (Differential
Algebraic Equations). Figure 5 describes the modeling at this level highlighting the
energy exchange between the different scientific and technical domains. Three
classes can be distinguished: electronic domain, multibody and the actuators and
fluid systems, the latest has more scientific and technical diversity, including
electronic power, electromechanical, electro-technique, power fluid, thermal, thermaldynamic and mechanical. In each of these three categories, there exist specialized
modeling methods which have their numerical features.
S yste m le ve l
C O -S IM U LA TIO N
POWER ELECTRONIC
E l ectro mag n eti c
C O -S IM U LA TIO N
E l ectro mech an i cal
Actu ato rs
E l ectri cal M o to r
C O D E G EN ER A TIO N
M O D EL S IM P LIFIC A TIO N
« F l u id P ow er »:
ƒ F l ui d (l i qu i d, g as,
mi xtu re… )
C O -S IM U LA TIO N
ƒ T h ermal
ƒ T h ermo -h yd rau l i c
ƒ M ech an i cal
C O D E G EN ER A TIO N
A C TU AT O R S & FL UI D S Y ST E M
MULTICORPS SYSTEMS
C o d e Ge n e ra t io n
--------------------
ELECTRONIC DOMAIN
C o n t ro l La w
NE TWORK LEVE L
D A TA EXC H A N G E
GE OME TR Y LE V E L ( F E M-CF D)
Figure 5: Technical domain covered by network level
Electronic:
The modeling of the electronic system is on the basis of the nodal and modified nodal
method [11][12]. His principal application concerns the discrete state problem. The
standard language adopted is VHDL who evolves towards VHDL-AMS (VHDL-AMS
IEEE 1076.1 1997) in order to take into account the time-continuous phenomena.
The simulation softwares in this domain are:
• SABER (Avant!Corp.,Analogy,Inc., 1999)
• ADVanceMS (/Mentor Graphics/Anacad, 2000)
• Simplorer (ANSOFT)
Actuators and fluid systems:
The idea is to find the methods which can generate the model mathematical
equations from the system topologic description. This description is at the beginning
in the form of programmed language and then evolves towards the graphic
description. For these software, the connections between the elements are realized
with the notions near to “multiport”. Some examples of these software are:
• AMESim® (Multiport multi-domains)
The emergence of Bond-Graph gives a new light to the modeling at the network level.
Based on graphics, the technique offers the unique capacity of explicitly describing
both energy transfer between the structural units of a system, and the calculation
framework that is contained within the causal information. Bond Graphs are not only
graphic representations of mathematical expressions, but a conceptual framework
offering a highly-structured language and based on considerations derived from
physics. The software based on the Bond-Graph concepts and with the most largely
extended models classified in the form of libraries is AMESim® (IMAGINE). This
software provides a editor permitting the users to easily extend the existent libraries
to their specific component models and develop their new models. Actually,
AMESim® covers the domain defined between the electronic and the mechanical
multicorps of Figure 5.
Multibody:
Since 1975, the multibody method is of growing importance in computational
mechanics, this method applies to the modeling of mechanical articulated structures,
which include the robots and the ground vehicles to some extend. Today, the later
represent the principal users as well as the aeronautic industry. The principal
multibody’ tools are:
• ADAMS® (MSC)
• SIMPACK® (INTEC)
• DADS® (LMS)
Geometry level:
At this level, models contain parameters describing the 3-D or 2-D geometry and
material properties of the simulated part. Problems are defined by PDEs (Partial
Differential Equations), which are solved using a discretization or meshing of the
geometry such as solid modeling FEA (Finite Element Analysis), and computational
fluid dynamics (CFD). This kind of modeling is well suited to analyse in detail the
distribution of a particular physical property in a continuous medium. However, its
scope is usually restricted to de detailed analysis of single parts or subcomponents,
and multi-domain dynamic interactions can not be time-efficiently simulated yet.
Examples tools at this level are:
• Ansys® (Ansys Inc, 2001)
• Maxwell® (Ansoft Corp. 2001 )
• FIRE® (AVL List GmbH, 2001)
• FLUX 2D® (CEDRAT)
Figure 6. gives an example representing the technical scheme of an electrovalve as
well the magnetic circuit meshing in FLUX 2D.
Figure 6: (a) Scheme for a electrovalve. (b) Magnetic circuit meshing in FLUX 2D.
4. Polymorphism and modeling
The design cycle process requires a significant amount of degrees of freedom, even
more so in the early stages of the project. As a result, it appears difficult, impossible
even, to foresee the models required for the design process. The development of a
limited number of basic models, from which a large number of scenarios can be built,
leads to the polymorphism concept [13]. The power of this approach mainly lies in the
relevance of the basic splitting. We realize that this choice expresses the state-ofthe-art for the associated domain and represents not only a powerful design support
tool but also a learning tool related to associated technologies and physical concepts.
Figure 7 provides an example which involves the domain of fluid energy control. It
appears that from few basic elements derived from hydraulic technologies, a very
large number of situations can be represented [14] and [15]. This diversity is even
more amplified by the fact that several levels of models can be associated to each
icon. These models can be combined to increasingly complex hypotheses up to
considering thermo-dynamic fluid properties with the intention of simulating the
thermal hydraulic behaviour of the studied system.
Figure 7 : Polymorphism applied to the fluid power command domain.
The choice of basic elements lies on the state-of-the-art in each domain, fluid energy
control in this instance. As a result, we have the different valve technologies (spool,
poppet, balls…), with other items such as elements for constructing jacks (piston,
end stops), mass elements with or without bearings… Taking friction into account, all
these elements can be used either with absolute or relative movement. The
consideration of the fluid compressibility property is included in the 4-port chamber
element “ch”. As Figure 7 briefly demonstrates, the combination of these basic
elements guarantees the construction of several hydraulic component models
ranging from a basic jack up to complex servo-valves modeling while including
engines and hydraulic pumps.
5. Application of the approach to a Motorbike :
This chapter explains how the approach presented in the previous chapters can be
applied to motorcycles. The main idea is to show how to gather basic elements in
order to build components like: transmission, brakes, engine, injection system etc.
Once we have the component models the assembly of this components leads to
system simulation: the motorbike model. This chapter is divided in two parts: the first
one presents models of components, the second part shows the full model of a
motorbike. Six components are described in the first part of this paragraph, they
correspond to :
-
fuel injection system [30, 31, 32],
-
the engine,
-
the transmission,
-
the tire mode,
-
the braking system,
-
the motorbike represented by a one dimension inertia.
The models presented below have been used and validated for automotive
applications. Only motorcycle injection systems have been extensively modeled in
AMESim. The main idea of this example is to present how a system simulation
platform like AMESim can be used for motorbike application.
5.1 Component description
A - Fuel system
We assume for this example a low pressure injection system and injectors for the
engine fuel delivery. The fuel injection model presented on Figure 8 takes into
account all the components from the fuel tank to the injector. Starting from the
reservoir we have a pump represented by a sinusoidal flow source. A pressure
regulator at the outlet of the pump is used to maintain a constant pressure in the
circuit.
Figure 8: Two cylinder fuel injection system
The connection from the pump/regulator to the injectors is done using hydraulic line
models. Some of these lines are rigid others are flexible. The rigid line models are
very well known and AMESim includes a collection of lines models from the basic
one that is represented by a single volume to more complex line models that include
wave effects. For flexible lines the volumetric expansion is required. When correlation
with measurement shows some problems with damping, we have the capability to
take into account the visco-elastic effects of the hose.
Injectors are usually characterized by static flow at a given pressure. In this example
the injectors are modeled with a variable orifice. Its dynamic behavior is represented
by a first order lag and a delay to take into account the dynamic between the electric
input signal and the injector opening. The injector actuation is achieved with a ECU
control block that set’s the pulse width modulation according to the engine speed
signal. Finally the output of this model is the injected volume of each injector. This
information is then send to the combustion chamber model described in the next
paragraph.
Once the model is created, we have the possibility to analyze the system in the time
domain or frequency domain. It is strongly recommended to first start in the
frequency domain using the linear analysis tool. Linear analysis is very well adapted
to hydraulic networks, it gives a good representation of the dynamics with very short
simulation time. Moreover this type of analysis are independent of the inputs.
Different tools are available in AMESim to understand the dynamic behavior of the
system :
•
eigen-values calculation,
•
modal shape tools,
•
transfer function using Bode plots,
•
root locus.
Once we have a good knowledge in the frequency domain the analysis in the time
domain is drastically reduced. The model is then ready to be used to sensitivity
parameter analysis, optimization etc..
B - Engine model
The engine model presented on Figure 9 takes into account mechanical, pneumatic
and thermal effects. The inputs of this model are the injected fuel quantity coming
from the injectors (see model Figure 8), the intake and the exhaust air pressure, the
cylinder head temperature and the gear box shaft angular velocity. The output of this
model is the torque delivered by the engine to gear box and transmission.
Figure 9: Two cylinder engine model
Mechanic of the engine
The mechanical components are used to describe the camshafts on the top of the
engine and all the internal mechanical components on the bottom side. The camshaft
model is very basic, it calculates the engine valve displacement as a function of the
crankshaft angular position using a data file for the cam lift. The engine model has
been written as a basic element corresponding to a mono-cylinder. This monocylinder model takes into account the inertia effects of the piston, the connecting-rod
and crankshaft. In that condition, it is very easy to extend the model to a N cylinder
engine model. In our example, a two cylinder engine is presented.
Engine breathing
The combustion in an internal engine requires two compounds: fresh air and fuel.
Thus, the quality of the engine filling is as important for performance as its total
capacity or as the amount of fuel injected. To simulate the first order behavior of the
intake and exhaust flows, simple valve models are used to calculate a mass flow rate
as a function of the pressure drop through the valve with the classical Barré de Saint
Venant equations. These equations are valid for subsonic and sonic flows (assuming
a perfect gas).
Combustion
The combustion is modeled using a Wiebe [22] single zone approach. The
combustion heat release is taken into account but no distinction is made between
burned / unburned gas. The combustion is rather evaluated as a first order lag on the
injected mass flow rate. However, the setting of the time constants for the
combustion process and for the auto-ignition delay are of great importance for
determination of the in-cylinder pressure shape and peak timing.
Heat exchange
To model convection phenomena between the in-cylinder gas and the walls, a
common formulae is used for the Nusselt number calculation as a function of the
Reynolds number. A special port on the combustion chamber allows to impose the
cylinder wall temperature. This temperature is assumed to remain constant.
Chamber thermodynamics
After calculating the heat exchanges in the cylinder, the pressure and temperature in
the chamber can be determined using mass and energy equations. The variation of
internal energy U can be expressed using the first law of thermodynamics applied to
an open system:
dU
&+ H
= W +Q
∑&
dt
C - Transmission model
The transmission model presented on Figure 10 takes into account a single disc
clutch, the gear box and the shaft from the gear box to the motorbike wheel. The
friction models used to represent the clutch are identical to the ones presented below
for the braking model. A combination of rotary friction models in series will lead to a
multi disc clutch.
Figure 10: Simplified model of a transmission (Two gears)
The shaft inertias and stiffness correspond to standard elements of the mechanical
library of AMESim. Three specific components from the transmission library have
been used in the model: gears, universal joint and dog clutch elements. The
universal joint model takes into account a possible geometrical change between both
shafts connected. Concerning the dog clutch, the shape of the teeth is taken into
account (symmetric or non), the contact forces as well as the friction when bodies are
in contact.
Different gear models are available, the simplest one is a simple transformer, it takes
into account a gear radius. The second level model introduces losses by constant
efficiency. The third level takes into account contact and backlash in-between teeth’s
using a spring plus damper and hertz contact method.
Figure 11: Teeth contact
D - Tire model
The tire model is presented on Figure 12. This model is connected to the
transmission by port number 1. The tire model receives a torque from the
transmission and gives the tire rotary velocity to the transmission. The second port of
the model is connected to the breaking system. The causality is the same as the
transmission port, the tire receives a braking torque and sends a rotary velocity. The
last port corresponds to the connection with the motorbike linear inertia. The model
provides a force to the motorbike inertia and this inertia sends back its linear velocity
to the tire.
Figure 12: Tire model
Different models are available behind the tire icon. The first model is a hyperbolic
tangent model (with saturated slope). The user provides a force at 100% sliding. The
second model available is the well known Pacejka model [28] very often used in
vehicle dynamic. Only the longitudinal direction is taken into account in that model.
Two other models have been tested and will be in standard in a close future, they are
based on the Dahl and LuGre friction models. Compared to Pacejka model which is a
mathematical representation of the tire behavior, the Dahl and LuGre models are
more related to physics. The LuGre model in particular is a distributive model, it takes
into account the contact area of the tire on the ground. From a dynamic point of view
both methods have the capability to represent the first structural model of the tire.
The range of validity in term of velocity is more important with the Dahl/LuGre model
compared to Pacejka which is difficult to use for very low velocities.
E - Braking system
The braking model is a combination of hydraulic and mechanical components. A
variable source of pressure imposes the pressure in the caliper. This pressure is then
applied to the caliper piston. This piston takes into account the caliper piston inertia.
The contact between the piston and the braking disc is represented by a stiffness
with air gap. This air gap corresponds to the air gap between the brake pad and the
disc. The contact force generated is then sent to a rotary friction model as a signal.
Figure 13: Braking system model
The rotary friction models
Five models are available either for translation or rotary motions:
-
Coulomb friction represented by a hyperbolic tangent model
-
Coulomb friction represented by a reset integrator model
-
Coulomb friction represented by Karnopp model
-
Coulomb friction represented by Dahl model
-
Coulomb friction represented by LuGre model
Except the Karnop model, all the other models are independent of the inertias
connected to the friction model. Concerning Karnopp model, it is included to the
inertia models.
The Dahl and Lugre models correspond to the last development for friction model in
AMESim. More detailed explanation about these two models is provided below.
Dahl :
Dahl proposed in [23] a differential expression for friction inspired from elasto-plastic
behavior of materials. It was introduced for the purpose of simulating systems with
friction. The model is also discussed in [24, 25]. The originality comes from an
expression function of relative angle displacement u instead of the rotary velocity
du/dt, this so called rate independence is an important property of the model.
LuGre :
With Dahl model, the LuGre (Lund - Grenoble) friction model is another dynamic
model presented by C. Canudas de Wit, H. Olsson, K.J. Astrom and P. Lichinsky
[26, 27]. Extensive analysis of the model and its application can be found in [29]. The
model uses bristles to represent friction . The friction force is modeled as the average
deflection torque of elastic springs. When a tangential effort is applied, the bristles
will deflect like springs. If the deflection is sufficiently large the bristles start to slip.
The average bristle deflection for a steady state motion is determined by the rotary
velocity. It is lower at low velocities, which implies that the steady state deflection
decreases with increasing velocity. This model permits to reproduce Stribeck effect.
The model also includes rate dependent friction phenomena such as varying breakaway torque and frictional lag.
F - Motorbike dynamic
The purpose of the model is to check the capability of the model to accelerate and
decelerate the motorbike body. In that condition a simple inertia is sufficient to
represent this phenomena. The mass model provides a velocity to the tire model and
receives a force from it. This model is presented by Figure 14.
Figure 14: Simplified model of motorbike body
5.2 Full motorbike model
Chapter 4 of this paper presents the general concept of basic element libraries. The
main interest of it is to re-use validated models to reduce the modeling time. The
validation time has been spent once by computer science and programming
engineers. The first part of this chapter shows how to re-use basic elements to build
components. At this level it is still necessary to validate and debug the models. The
difference compared to the previous validation is that the engineer is now
concentrated on his real technical problem. No sign convention problem to solve, not
C or Fortran syntax mistakes to figure out.
The motorbike model presented in Figure 15 has been obtained by assembling the
subsystems presented in the previous paragraph. Very often it is necessary to make
model simplification before building the full model or directly on the final model. This
is the case when the final model has to run for real time application and more
generally it is interesting to make simplification to reduce the CPU time consuming.
To help the user to do so it is possible to use linear analysis tools like the one
presented in point A of chapter 5.1. Another powerful tool very well adapted to model
simplification is the activity index feature. Activity index is based on the calculation of
the ratio of energy (potential, kinetic) spend in each component in comparison to the
total energy of the system. It is then very simple to visualize which component does
not spend energy and remove it from the model. From a design point of view this tool
can be used to track where the energy consumption is the most important and
improve the overall behavior.
Figure 15: Full model of a motorbike from the engine to the one DOF vehicle model
6. Conclusions
The constraints of time-to market, cost and quality result in an exigency of use of
modeling and simulation within a virtual prototyping environment. The complexity of
the mechatronic system requires the different modeling and simulation tools at the
different levels of the design process described by the V-cycle design process model,
the features of such an integrated design environment organized and merged by
these tools is introduced in this paper:
•
Openness
•
Interdisciplinarity
•
Reusability
•
Portability
At the same time, the different levels of abstraction, which correspond to the different
levels of V-cycle design process is elaborated.
•
Functional level
•
System level
•
Network level
•
Geometry level
An modeling and simulation example of the AT lock-up clutch slip control is clarified
at different levels of abstraction.
Literature
[1] Isermann R. (1999) Mechatronische Systeme. Springer. Berlin, Germany.
[2] Mc Dermid, J.,Ed. (1991) Software Engineer’s Reference Book. ButterworthHeinemann Ltd. Oxford, Great Britain.
[3] Muller-Glaser, K.D. (1997). Smart systmes engineering. In: Steuergerate-Design
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Autor/ Author:
Prof. Michel LEBRUN
CLAUDE BERNARD University,
Lyon, France
e-mail : lebrun@amesim.com
Co-Autor/ Co-Author:
Elysé BOTELLE
IMAGINE Software Gmbh
Elsenheimerstr. 15-D-80 687 München, Deutschland
e-mail : botelle@amesim.com