Advanced Simulation Methodologies for Chassis ...

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 Information of the author / reachability: first name / name academic title company

Stijn Donders Dr. Ir. LMS International

department

Simulation Division

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Interleuvenlaan 68

code – place - country telephone e-mail

B-3001 Leuven, Belgium +32 16 384 531 [email protected]

Advanced Simulation Methodologies for Chassis & Suspension Engineering 1

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Stijn Donders , Alessandro Toso , Izabela Kowarska , Tadeusz Uhl , Dariusz Michalak

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1 – LMS International, Leuven, Belgium 2 – AGH University of Science And Technology (AGH), Krakow, Poland 3 – SOLARIS Bus & Coach SA, Owinska, Poland

Abstract In the highly competitive global environment, automotive manufacturers must deal with conflicting demands from customers and regulatory bodies regarding the functional performance and environmental impact of their cars and buses, and are forced to develop products of increasing quality in even shorter time. Primary functional performance attributes are the driving dynamics (handling, ride comfort, durability and NVH) and active (and passive) safety. At present, automotive manufacturers must deal with the challenge to incorporate more and more electronic and mechatronic content in vehicles, and answer the customers need for mass customization (more vehicle variants on a lower number of platforms). Internally, there’s a need to streamline virtual design engineering processes and allow a tighter collaboration between departments and with the supply chain. For this purpose, manufacturers are currently reviewing their design processes, and enriching these through advanced simulation methodologies. This paper reports on the results achieved within EUREKA R&D project “CHASING”, a consortium of R&D partners LMS (in Flanders) and AGH, EC Engineering and bus manufacturer SOLARIS (in Poland). The dual objective has been to develop advanced simulation methodologies for chassis & suspension engineering, and to develop an innovative chassis design concept according to industrial bus standards in a simulation-based design approach. A number of methodology and process innovations in this key design area are presented in this paper.

Keywords: Bus and passenger car, chassis and suspension, durability, handling and ride comfort, CAE.

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1 Introduction: Industry challenges and trends in chassis and suspension design In the highly competitive global environment, vehicle manufacturers must deal with conflicting demands from customers and regulatory bodies regarding the vehicle functional performance and its environmental impact, and are forced to develop products of increasing quality in even shorter time. Primary vehicle functional performance attributes are the driving dynamics (handling, ride comfort, durability and NVH) and active (and passive) safety. A major evolution in the vehicle industry is the increasing electronic and mechatronic content. Since the use of electronic control units enables new strategies to improve the vehicle performance, the bulk part of the manufacturer R&D budget is invested in this area. The increasing integration of electronic and mechatronic content, however, also leads to a drastic complexity increase. It is a major challenge for vehicle manufacturers to deal with this complexity while further improving the product performance. The importance of tackling this challenge is underlined by the fact that more than 50% of defects and warranty costs in the automotive industry are related to software and electronics issues. Product quality, safety, and customer satisfaction can only be achieved when engineers are able to manage the complexity in the product development process from the concept stage onwards. Another trend is mass customization: vehicle manufacturers are delivering a strongly increasing number of vehicle variants on an ever lower number of vehicle platforms. The lower number of platforms gives a scale advantage in the design process, with platforms shared between vehicle types in a given brand, and even between different vehicle brands. The increased number of vehicle variants is driven by the growing consumer demands to personalize the car, turning it into the car of his/her choice by selecting from a wide realm of options and feature variants. A major challenge for vehicle manufacturers is that these variants must all meet the high product quality standards and performance targets. This sets the requirement for a highly flexible design engineering process, in which all variants can be easily modeled and virtually analyzed, complemented with experimental validation. The above trends drive the manufacturers to implement new process solutions that streamline the virtual design engineering process and allow a tighter collaboration between internal departments and with the supply chain, such that the complexity of the vehicle design process can be managed and the vehicle design can be optimized for all relevant performance attributes in a balanced manner, tightly linking all design aspects and departments involved. Firstly, a co-simulation approach towards optimized design of a bus chassis is presented, based on an integrated bus simulation model (involving flexible bodies), which is a step up in terms of model size and design complexity as compared to previously reported applications of co-simulation for passenger cars. Secondly, a new process for streamlining the chassis and suspension design process of multiple variants is outlined, which supports the creation of customized applications. The process enables template-driven simulation and efficient driving dynamics scenario evaluation, resulting in large efficiency gains in industrial design variant engineering. Finally, a new virtual durability test rig methodology is presented, which alllows dramatically reducing the computational time for early durability loads analysis, yielding better results in the identification, especially at low frequencies.

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Co-simulation approach towards optimized design of a bus chassis

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Co-simulation: an introduction

By definition, in a mechatronic system, many different types of subsystems (e.g. mechanical, hydraulic or electric subsystems) interact with each other [2], creating closed loop actions between the mechanical components, hydraulic actuators, electrical elements and control systems. Given the complexity of these products their design and virtual validation leads to a multi-domain problem. The simulation of such complex multidisciplinary systems is a new challenge in modern computer aided engineering.To be able to solve such a multi-domain problem, the state of the art solution is through “co-simulation”. The core idea behind co-simulation is to split the overall system into different subsystems, which are treated by different optimized simulation software tools, coupled by exchanging input-output variables, thus creating a coupling loop [3][4]. Co-simulation is a powerful method to virtually analyze the complex interactions between the different subsystems in a complex chassis and suspension design, so that an optimal product can be delivered

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 to market [5][6]. The rationale of co-simulation is to exploit the power of existing solvers in their core domains, and establish a communication process between the different solvers, so that the complex behavior of the integrated system is realistically simulated in the time domain. Especially for industrialsized applications in automotive and aerospace industry, it is crucial to on the one hand capitalize on the availability of subsystem models in the different simulation domains, and to on the other hand manage the complexity of the multi-domain simulation model across domains. 2.2

Co-simulation approach towards optimized design of a bus chassis

The CHASING project [1] has an international “EUREKA” collaboration with a consortium of R&D partners in Poland, involving AGH, EC Engineering and bus manufacturer SOLARIS. Within the Polish R&D context, the major use case is the design of an innovative chassis and suspension system for a city bus, leveraging the power of innovative new simulation methods and processes [12][13]. A cosimulation approach has been implemented to create an integrated virtual simulation model of the bus and its subsystems/controllers. The co-simulation allows to model the different parts of the mechatronics system model of the bus in different software, and to establish a single simulation setup that can be used to predict the properties of the suspension and chassis of the vehicle from the conceptual design process onwards [7][14]. For the bus chassis, the co-simulation integrates all chassis components in an integrated simulation process: •

LMS Virtual.Lab multi-body simulation components for the chassis structural components and the bus body (modeled as a flexible body component) [8][9];

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LMS Imagine.Lab components (for pneumatic air spring system and braking system) [10][11];

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Simulink controllers (for the ECAS and ABS/ASR systems).

The integrated bus simulation model has been used for a wide range of analyses in particular for virtual updating and tuning of the spring and damper elements, and to analyze and optimize the driving dynamics performance of the bus. Based on virtual simulations, the realistic bus suspension behavior can be predicted, and one can optimize the suspension parameters such as spring-damper elements characteristics for different roads, so that for each target road network, buses can be delivered with an optimal ride comfort, handling and safety performance.

Figure 1: Integrated bus simulation model, using co-simulation to link the chassis structural components and the bus body (as 3D multi-body simulation models) with the air spring and braking systems (as 1D functional simulation models) and the controllers (ECAS and ABS/ASR). The possibility of including the body flexibility allows also assessing the effect of changes in the suspension settings or different design on the handling metrics and on the comfort. For instance, following the ISO 2631 [15], the ride analysis on several roads allows computing the Vibration Dose Value (VDV) [16] experienced by the driver. This offers the prospect to perform virtual design optimization to significantly reduce the VDV, for instance by introducing structural modifications or acting actively on the suspension elements (dampers, air springs).

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013

3 Streamlining the Chassis and Suspension Design process of multiple variants The driving dynamics performance of a vehicle results from the motion mechanics of the vehicle under the application of tire forces, generated by driver input through the “actuator” systems: steering, braking and powertrain. The application of the tire forces to the vehicle is achieved by transferring these forces from the wheels (unsprung mass) to the vehicle body (sprung mass) through the suspension linkage, and the resulting dynamic body motion is controlled by spring and damper elements in that suspension [17]. The driving dynamics are thus primarily determined by the chassis subsystems, through their design and interactions. Moreover, many of these systems are “active”, with controllers that adapt the system’s characteristics or assist the driver by interfering with the “actuator” systems. Nowadays, vehicle dynamics simulations are used by a range of design engineers to understand the effects of setup changes on the vehicle performance, which could be made before, during or after an event (test-rig, static corner, dynamic maneuver...). Design questions can be highly specific and complex (e.g. the effect of chassis setup changes on dynamic wedge and understeer gradient), open ended (“see what you can do to address the issue”) or almost impossible to solve because of the amounts of inputs needed (e.g. determine the optimal lap time simulation). Broadly, two classes of simulation studies are distinguished: •

Component changes (e.g. springs, shocks, anti-roll bars)

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Topology changes (e.g. upper control arm lengths, static camber or toe, suspension arm window locations).

In terms of simulation practice, one can distinguish configuration lists (lists of potential setup configuration changes, but also a range of discrete options for power curve, tire selection, tire pressure or other design values), Design of Experiments (to systematically assess the effect of e.g. gear ratio, mechanical setup, alignment, ... on the vehicle dynamics performance) and Optimizations. Simulations may be performed sequentially (e.g. quasi-static up to dynamics ...) or in parallel (suspension test rig simulations, virtual shaker testing ...). This involves generating a large number of model definition files, each of which may have to be transferred to another location, executed and calculated. This generates a large number of possibly large result files (up to gigabytes of data for detailed (sub)system models), that must be retrieved and postprocessed [18]. The design engineer’s key interest is then to efficiently retrieve the data, obtain a summary and to be able to interpret the results in order to support design decisions in an automated process that can be customized easily. From the above, it becomes clear that a streamlined approach for chassis and suspension design variants analysis is vital ensure the final product performance. Two current trends underline this: •

On the one hand, the ongoing paradigm shift to a systems engineering approach for active and intelligent vehicles increases the need for modularity in the results interpretation methodology (allowing to select subsystems of interest, and to flexibly select results and visualization options per subsystem).

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On the other hand, the ever increasing size of detailed 3D (sub)system analysis models and results for vehicle dynamics forms a burden for efficient file handling and results synthesis.

LMS has developed a new process for streamlining the chassis and suspension design process, in order to address this challenge. The Composer [8] supports the creation and easy customization of design scenarios focused on chassis and suspension, streamlining the simulation process from reading input data, through solving different design variants, to results post-processing; see Figure 2 (left). The design engineer can construct the design models and scenarios using libraries of template models, which are typically built by a multi-body simulation expert. The Composer supports drag-anddrop GUI creation, thus enabling design engineers to rapidly design customized applications. An example of this powerful tool is the Driving Dynamics GUI (see Figure 2, right), a dedicated interface for all vehicle dynamics analysis of models that have been built using the Composer. It allows vehicle selection, population, solution and post-processing for platform design. The application also helps users to optimize vehicle subsystems (chassis and suspension, steering, driveline) for driving dynamics performance. Furthermore, it can easily be customized and tailored to in-house processes and simulation requirements. The Variant Manager feature in the Composer allows the easy creation of variants. In the context of the Driving Dynamics GUI, design engineers can derive variants of a vehicle program from a base

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 vehicle without having to duplicate data and do extensive file management. In a single interface and single data file, the design engineer selects a base vehicle model and starts deriving multiple variants that have a unique subset of data, different from the base model. A few real world examples of this scenario would be to have sedan and wagon variants, AWD and FWD variants, different trim and wheel packages, etc. With the Simulation Manager feature in the Composer, a matrix of load cases and CAE models can be solved simultaneously without having to go through a manual setup of a series of load cases. The Simulation Manager thus helps solving the entire suite of standard simulations in a single setup in a highly efficient manner.

Figure 2: The chassis and suspension design process is supported by the Composer (left) to create and easily customize chassis and suspension design scenarios, and by the Driving Dynamics GUI (right), a dedicated interface for all vehicle dynamics analysis built using the Composer. Together, the Composer and Driving Dynamics functionalities enable the manufacturers to streamline their complex chassis design process, thus ensuring the optimal driving dynamics of all variants. Additional application know-how is captured in dedicated interfaces to facilitate the design of complex chassis systems. For instance, a leaf spring modeling tool has been developed to facilitate the creation and analysis of this suspension type, often used as suspension in trucks. A leaf spring is easy to manufacture and has a robust performance, but the model creation can be cumbersome due to the complex geometry, the high number of leaf elements and the specification of the contacts. With the leaf spring interface, design engineers can easily create the leaf spring geometry of their choice and model the leaves and their properties in a user-friendly GUI, and thereafter automatically create a multi-body simulation model for driving dynamics analysis.

4 Virtual durability test rig to bridge from handling/comfort models to durability analysis 4.1

Introduction

It is essential to tighten the collaboration between different departments, in view of ensuring a balanced design of the complex chassis and suspension and the vehicle as a whole. Typically, handling and ride comfort departments make large investments to design detailed multi-body simulation models and virtually optimize the chassis and suspension design. This paper presents a new virtual durability test rig approach that allows the durability departments to tap into these models for their analysis purposes in a streamlined manner. This offers potential to improve the durability design process, which often still relies substantially on test-based validation on experimental test tracks. The determination of durability loads on full vehicles in an early design change is necessary to virtually analyze and optimize the vehicle durability performance during the product development cycle. The computation of the life of vehicle components is challenging from different points of view and frontloading the calculation during the design process allows to save time and

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 money later on. In fact in a early design stage, building a prototype and performing track tests is costly and any change involves high costs and the need to repeat experimental tests. On the other hand it’s quite common to have test data from predecessors and similar vehicles together with correlated multibody models. Several techniques exist to transfer measured loads on numerical models [19][20][21][22]. The presented methodology follows the rationale of the Hybrid Road approach [19], which involves performing a simulation on an unconstrained vehicle, but instead of applying directly the measured loads (typically forces and torques at the wheel hubs) that will result in an unbalanced set of forces (and thus in a incorrect computation of durability loads), the equivalent road profile is computed. The aim of the ‘Virtual Test Rig’ [23] is then to allow the manufacturer to perform a durability analysis and a reverse-load-identification in the virtual world before testing the vehicle prototype on the experimental track. The application takes advantage of the modeling capabilities of LMS Virtual.Lab Motion and uses its model linearization functionality to provide a linear model of the vehicle to the core solver of the Time Waveform Replication (TWR) process. In a traditional TWR process, the linearized model is derived through the classical system identification, which is a very time consuming part of the workflow. With the new TWR methodology, it is possible to dramatically reduce the computational time and achieve better results in the identification, especially at low frequencies. 4.2

Time Waveform Replication (TWR): Methodology description

As stated in the previous paragraph, the Time Waveform Replication (TWR) technique is the core technology that enables the reverse load identification and its integration with the Multibody simulation (MBS) software creates a unique solution, taking advantage of the powerful modeling tools available in MBS. TWR in fact is an inverse engineering tool that allows computing a set of inputs that, if used to drive the numerical model, will result in a set of desired outputs at some location on the model. In particular, the TWR solution relies on a linearized model of the system to back-calculate the inputs from a given (desired) set of output. In practice, most of the models, especially in the field of vehicle dynamics, are non-linear because of geometrical nonlinear elements or non-linear bushings and so on. Thus the linear model that is used to back-compute the inputs is only an approximation of the real one and the inversion process does not lead to an exact estimation. In order to cope with this issue, an iterative process is implemented. The computed input is used to drive the multibody model and to compute the actual output. The error with respect to the desired output is evaluated and used in the TWR linearized model to calculate the correction factor. The process is repeated until a criterion on the error is met (for instance the RMS error stays below a certain threshold). The full process is shown in the block scheme in Figure 3.

Figure 3: Overview of Time Waveform Replication (TWR) process.

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 One of the key elements in the process is the system identification and the inversion of the system model that is used throughout the process for the drive calculation and update. Therefore it is important to obtain a good linearized model of the system to ensure the convergence of the process. The adopted TWR approach in LMS Virtual.Lab Motion [8], denoted as Motion TWF from here on, is capable to drive the system with a pink noise applied at the actuator location. The dynamic simulation in the multibody solver computes the response of the model in correspondence of the sensors. Then a time signal processing algorithm is used for the estimation of the frequency response function of the linearized system. The identification signal plays a fundamental role in the process. Its characteristics are designed in the frequency domain in order to excite the model dynamic only the frequency range of interest. The “system inversion” consists in practice in a pseudo-inversion of the frequency response matrix by means of singular value decomposition. In general the frequency response matrix is not square, which means that the usual inverse operator cannot be used. The inverse FRF will then be multiplied by the target signal to obtain the estimation of the drive to apply to the actuators. The target is a signal that has been measured in the road tests. Normally this is available as a function of time while the FRF is computed in the frequency domain. Accordingly, Motion TWR transforms the signal in the frequency domain by means of the Fourier transform and after the matrix multiplication applies the inverse Fourier transform to obtain the drive signal in the time domain. In general, the systems that are analyzed are non-linear and then the application of the full driver will result in a response that differs from the target. In order to mitigate this effect, one can apply a gain to the target and the drive to reduce their amplitude.

Here, F is the Fourier transform, d is the drive gain and g is the target gain, t(f) is the target signal transformed in the frequency domain and u(t) is the estimation of the first drive. After the dynamic

simulation, Motion TWR allows adjusting the first solution by using the error that results from the application of the drive and the target. In this case the indices i and i+1 indicate the iteration number. The factor g is the Error Feedback Gain and e(f) is the error in the frequency domain, which is defined as:

Here, y(t) denotes the system response. The process can continue until a certain criterion is met. At the end of each iteration, the TWR solver computes not only the error in the time domain but also in the frequency domain together with other indicators such as the Root Means Square error.

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Time Waveform Replication (TWR): Process Integration

Toso et al. [23] firstly proposed the integration of the core TWR solver described in the previous section, with an existing Multibody software package [8]. The resulting Motion TWR process comprises the following steps: 1. Definition of model inputs and outputs. 2. Computation of the linearized model of the system and its inverse (FRF and Inverse FRF). 3. Definition of the desired outputs (Target). The experimental data is retrieved from a file and assigned to the corresponding model sensors. 4. Computation of the “First Drive”. 5. Computation of the MBS solution. 6. Evaluation of the error and calculation of the updated drive. 7. Iterate from step 5.

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 Within the Motion TWR approach, at each iteration, a number of indices are computed to help the engineer to evaluate the quality of the iteration and of the overall process. Moreover a set of postprocessing tools are available. In particular the time histories of the current outputs and the targets and their PSDs can be overlapped, the error is computed in time and frequency domain, and the RMS of the error for each channel is evaluated. An example is reported in the next section. 4.4

Time Waveform Replication (TWR): Case Description & Results

As an illustrative example, the hybrid road methodology has been applied on an industrial case starting from the test campaign to the calculation of the road loads on the flexible multibody model of the vehicle prototype and performing a variant analysis for the development of a new vehicle model. The prototype under exam has been equipped with accelerometers and wheel force transducers and driven on a test track. The data collected during the drive has partially been used for the flexible multibody model correlation and update and finally for the road load prediction. In Figure 4, the multibody model is shown together with a typical result, the comparison of the cumulative damage computed on test data and simulation.

Figure 4: MBS model and Cumulative Damage plot of test data and simulation. It’s important to note that the model is in unconstrained condition in order to reproduce, in a realistic way, the driving conditions. Moreover the Motion TWR methodology allows avoiding the modeling of tires. In an engineering practice, tire models are indeed the most challenging (and expensive) model elements to correlate. By working on the wheel center nodes (instead of the tire patch) and by enabling the unconstrained condition, the Motion TWR approach indeed enables to efficiently and accurately predict realistic road loads for durability analysis, as can be seen from the good agreement between the experimental and numerical results shown in Figure 4.

5 Conclusions Key trends in industrial chassis engineering are the drastic increase of mechatronics content, the increased need for cross-department collaboration and the trend toward mass customization, in which a high number of variants needs to be fine-tuned and validated. To meet the overall vehicle cost and quality targets, the product design process should be largely based on virtual prototypes. Within this context, this paper focuses on the R&D results achieved in the EUREKA R&D project “CHASING”, a consortium of R&D partners LMS (in Flanders) and AGH, EC Engineering and bus manufacturer SOLARIS (in Poland). Within CHASING, the partners have pursued a dual goal: LMS has extended its CAE solution portfolio to the chassis and suspension segment, whereas the Polish partners have focused on their major use case, namely the design of an innovative chassis and suspension system for a city bus, by leveraging the power of innovative new simulation methodologies and processes. The presented advanced simulation methodologies for chassis and suspension design engineering of cars, trucks and buses enable automotive manufacturers to optimize the handling and ride comfort of

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Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 their vehicles and bridge from chassis engineering to durability engineering, all based on virtual prototypes. More specifically, the LMS Virtual.Lab Composer and Driving Dynamics GUI enable the creation of customized applications for template-driven simulation and efficient driving dynamics scenario evaluation. Furthermore, complex mechatronics systems can be simulated by exploiting a cosimulation approach, and the embedded know-how in multi-body simulation models can be exploited in a virtual test rig for early durability analysis.

Acknowledgements IWT Vlaanderen (http://www.iwt.be/) supported the CHASING R&D project (Advanced Simulation Methodologies for Chassis & Suspension Engineering) in Flanders. It is part of the EUREKA CHASING project E!4907 with an R&D consortium from Poland, comprising the AGH University of Science And Technology, EC Engineering in Krakow, and bus manufacturer SOLARIS in Owinska. The Polish National Centre for Research and Development (http://www.ncbir.pl/en/) co-financed the Polish project. The authors gratefully acknowledge IWT, NCBIR and EUREKA for their support. Furthermore, the authors kindly acknowledge the other researchers at LMS, AGH, EC Engineering and SOLARIS who have contributed to the CHASING R&D activities and results reported in this paper. LMS furthermore acknowledges the contributions of its subcontractors in the Flemish IWT R&D project: Manfred Bäcker and Axel Gallrein (Fraunhofer ITWM) in the R&D area of comfort and durability tire (CDTire) modeling and simulation; Domenico Mundo and Gianluca Gatti (University of Calabria) in the R&D areas of vehicle dynamics concept modeling and modeling and parameter identification for bushings. Furthermore, we kindly acknowledge Francesco Cosco (G&G Design and Engineering), who participated in the R&D activity as researcher in the EC Marie Curie IAPP project “INTERACTIVE” (“Innovative Concept Modelling Techniques for Multi-Attribute Optimization of Active Vehicles”, see http://www.fp7interactive.eu). For the support of INTERACTIVE as well as the EC FP7 Marie Curie ITN network No. 290050 “GRESIMO” (“Best Training for Green and Silent Mobility”, see http://www.gresimo.at/), the European Commission is gratefully acknowledged.

References [1]

IWT Vlaanderen: IWT O&O project 090408 (EUREKA project E!4907) “CHASING - Advanced Simulation Methodologies for Chassis & Suspension Engineering”, 01/10/2009 – 30/09/2012.

[2]

Anthonis J., Gubitosa M., Donders S., Gallo M., Mas P. and Van der Auweraer, H.: “MultiDisciplinary Optimization of an Active Suspension System in the Vehicle Concept Design th Belgian-French-German Conference on Optimization, Leuven, Belgium, Stage”, 14 September 14-18, 2009.

[3]

Busch M. and Schweizer B.: Numerical Stability and Accuracy of Different Co-Simulation Techniques: Analytical Investigations Based on a 2-DOF Test Model, 1st Joint International Conference on Multibody System Dynamics, Lappeenranta, Finland, May 25-27, 2010.

[4]

Schierz, T. and Arnold, M.: “MODELISAR: Innovative numerische Methoden bei der Kopplung von multidisziplinären Simulationsprogrammen”, in A. Brenke (ed.): Tagungsband ASIMKonferenz STS/GMMS 2011, Krefeld, 24.02.-25.02.11, Shaker Aachen, 2011.

[5]

Bastian J., Clauß C., Wolf S. and Schneider P.: “Master for Co-Simulation Using FMI, Proceedings 8th Modelica Conference, Dresden, Germany, March 20-22, 2011.

[6]

Gigler, M.: “Virtual Verification of Onbord Electrical System by Co-Simulation”, Proc. 5 Grazer Symposium Virtuelles Fahrzeug (GSVF), Graz, Austria, April 17-18, 2012.

[7]

Wyckaert, K.; Anthonis, J.; Donders, S.; Gopalswamy, S. and Van der Auweraer, H.: “Tackling the mechatronics engineering challenge in vehicle development from the concept phase onwards”, Proc. 4. Grazer Symposium Virtuelles Fahrzeug (GSVF), Graz, Austria, May 12-13, 2011.

[8]

LMS International, LMS Virtual.Lab Motion, http://www.lmsintl.com/simulation/virtuallab/motion, Rev. 11, 2012. See http://www.lmsintl.com/chassis-suspension-engineering for dedicated Chassis & Suspension solutions.

th

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[9]

Abstract 6 Grazer Symposium Virtuelles Fahrzeug / May 14-15, 2013 Gubitosa, M.; Tamarozzi, T.; Donders, S.; Mundo, D. and Desmet, W.: “Including flexible representation of suspension components in the co-simulation environment for active systems sensitivity assessment”, Proc. Eccomas thematic conference “Multibody Dynamics 2011”, Brussels, Belgium, July 4-7, 2011.

[10] LMS International, LMS Imagine.Lab AMESim, Rev. 11, 2012, http://www.lmsintl.com/LMSImagine-Lab-AMESim. [11] Alneri, S., di Carlo, P., Toso, A. and Donders, S. “Handling And Primary Ride Comfort Development In Early Design Stage By Means Of 1D Modeling Approach And Multi Attribute Optimization Process”. Proceedings of IMECE11 2011 ASME International Mechanical Engineering Congress and Exposition November 11-17, 2011, Denver, Colorado, USA. [12] Kowarska I.; Skarbek J.; Kuczek K.; Uhl T.: “Model-based engineering – simulations based design of the suspension of city bus”, Journal of KONES Powertrain and Transport, vol. 18 no. 4 p. 205–212, Krakow, Poland, 2011. [13] Kowarska, I.; Kuczek, K.; Uhl, T.: “Model-based engineering - the fully equipped city bus model- validation and sensitivity analysis”, Systems: Journal of Transdisciplinary Systems Science, vol. 16 iss. no. 1 s. 9-18, Krakow, Poland, 2012 [14] Kowarska, I.; Kuczek, K.; Uhl, T.: “Concept Modeling with the Use of Multi-Body Simulations of Suspension System for City Bus”, Proceedings of ESMC 2012, Graz, Austria, July 9-13, 2012. [15] Intl. Organisation for Standardization, ISO 2631-1, “Mechanical vibration and Shock-Evaluation of Human Exposure to Whole-Body Vibration – Part 1: General requirements”, 1997. [16] British Standards Institution, BS 6841, Measurement and Evaluation of Human Exposure to Whole-Body Mechanical Vibration, 1987. [17] Gillespie, T.D.: “Fundamentals of vehicle dynamics”, R-114, SAE International, March 1992. [18] Rosenplänter, S. and Warnecke, U.: “A Review of Experiences in Interdiscplinary Data th Management in Vehicle Engineering”, Proc. 5 Grazer Symposium Virtuelles Fahrzeug (GSVF), Graz, Austria, April 17-18, 2012. [19] Bäcker, M.; Langthaler, T.; Olbrich, M.; Oppermann, H.: “The Hybrid Road Approach for Durability Loads Prediction at BMW”, SAE World Congress, April, 2005. [20] Bäcker, M.; Möller, R.; Kienert, M.; Bayram, B.; Özkaynak, M.U.; Hakan, Y.: “Component Load Determination For A CAE Based Fatigue Life Prediction Of A New Bus Type”, Fraunhofer Publica, MP materials testing, Vol.51 (2009), No.5, pp.309-316 [21] De Cuyper, J.; Coppens, D.; Liefooghe, C.; Swevers, J.; Verhaegen, M.: “Advanced Drive File Development methods for improved Service Load Simulation on Multi Axial Durability Test rigs”, Proc. of the Int. Acoustics and Vibration Asia Conference, November 1998. [22] Van der Auweraer, H.; Donders, S.; Mas, P.; and Janssens, K., “Breakthrough Technologies for Virtual Prototyping of Automotive and Aerospace Structures”, Product Engineering: Tools and Methods Based on Virtual Reality, 2008 [23] Toso, A., Cofelice, N., Carr, S. and De Cuyper, J. “A Virtual Multibody Test Rig For Durability Analysis And Time Waveform Replication”. Presented at the JSAE Annual Congress on May 20, 2011. Pacifico, Yokohama. Japan. [24] Sinokrot, T., Nembrini, M., Prescott, W.C., Toso, A. “A Comparison Of Different Multibody System Approaches In The Modeling Of Flexible Twist Beam Axles”. Proceedings of IMECE11 2011 ASME International Mechanical Engineering Congress and Exposition November 11-17, 2011, Denver, Colorado, USA.

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