design optimization of suspension coil springs & stabilizer bars using hyperstudy in batch mode

Copyright © 2011 Allevard-Rejna Autosuspensions /All Rights reserved Confidential / Internal ARA use only

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Automotive Passion

Automotive Passion

Design Optimization of

Suspension Coil Springs &

Stabilizer Bars using

HyperStudy in Batch Mode


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Summary

• Company profile

• Why using a collaborative platform dedicated to FEA ?

• The solution : Virtual Prototyping Network (VPN)

• How it works ?

• Optimization problem formulation for coil springs - for stabilizer bars

• Why implementing HyperStudy in batch mode within VPN ?

• How was HyperStudy implemented within VPN ?

• Examples

• Conclusions

• Perspectives


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Company profile

A few customers :


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Why using a collaborative platform dedicated to FEA ?

1. Development engineers are not FE analysts

Parametric and easy-to-use tool (pre & post-processing)

Support from experts (training, hot line, user’s guide…)

2. Development engineers are present worldwide

Getting worldwide licenses would be too expensive

Data base sharing (co-development)

Standard models

In-house softwares articulated around Abaqus


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The solution : Virtual Prototyping Network (VPN)


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DOUAI PLANT

How it works ?

DISTANT

PLANTS

C-Shell & Fortran programs

Load

Balancer

Data base &

process management

FEA runner 1

FEA runner 2


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Optimization problem formulation for Coil Springs

• Constraints : Fz, rate, center of pressure (lower & upper), inter coil distance…

• Design space : ximin xi xi

max

• Objectives : min (max) , min (PSWT), max (inter coil distance)…

• Design variables : number of coils, (R, Z) & wire

Long and painful iterations…


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Optimization problem formulation for Stabilizer Bars

• Constraints : rate, maximum stress (outside & inside) & feasibility (process)

• Design space : ximin xi xi

max

• Objective : min (mass)

Long and painful iterations…

• Design variables : (ext , thickness) at different locations


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Why implementing HyperStudy in batch mode within VPN ?

• Specific pre-processing & post-processing

• Cost

3800 GWU for all the users – only used to solve the analysis

(HST GUI is no more needed)

• No need to be familiar with HyperStudy to run an optimization analysis

Development engineers can contribute the analysis more efficiently

(by choosing adapted design variables)

Pre-processing time (5 minutes VS minimum 2-3 hours in the past) Post-processing time (5 minutes VS 20 minutes in the past)


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How was HyperStudy implemented within VPN ? (1/3)

• Explore the impact of different design variables

Collaboration with our product experts was essential Select only useful design variables for optimization analysis

• Create a prototype that runs HST in batch mode and satisfies our needs

Collaboration with Altair’s support engineers

VPN was initially adapted (mesh based on parameters – no CAD…)

• Create a sketch to realize the specific GUI to implement in VPN

Collaboration with our partner

• Invest in new calculation servers

Decrease CPU time (optimization runs numerous FE analysis !)

• Organize specific trainings for this new feature

1 hour only is necessary to train users


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How was HyperStudy implemented in VPN ? (2/3)

Optimization defined in VPN by a user (pre-processing)

HyperStudy input files (TPL, XML, MVW) + C-shell scripts

HyperStudy output files (HGRES)

DXY file (VPN format)

Extract optimized run (post-processing)


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How was HyperStudy implemented in VPN ? (3/3)

Keep only runs

that satisfy

constraints

Extract

optimized run


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Examples : coil spring shape optimization

Initial shape to improve

• Fz = 3803 N

• Rate = 26.4 N/mm

• COP Xlow = 2.3 mm

• COP Ylow -34.5 mm

• COP Xup = -0.1 mm

• COP Yup = -5.2 mm

• Fz = 3764.3 N

• Rate = 25 N/mm

• COP Xlow = 2.6 mm

• COP Ylow -36 mm

• COP Xup = 1.3 mm

• COP Yup = -1 mm

Improved shape after 466 FE runs

(Wall clock time < 3 hours)

TARGETS

• Fz = 3745 N +/- 20

• Rate = 25.55 N/mm +/- 3%

• COP Xlow = 1 mm +/- 2

• COP Ylow -34 mm +/- 2

• COP Xup = 3 mm +/- 2

• COP Yup = -2 mm +/- 2

Minimize torsion stress


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Examples : stabilizer bar shape optimization

• Rate = 84.9 N/mm

• Out. VM stress = 864 MPa

• Inn. VM stress = 533 MPa

Mass = 4.985 kg (-28% / solid bar)

Initial shape to improve

TARGETS

• Rate = 85 N/mm +/- 5%

• Out. VM stress < 820 MPa

• Inn. VM stress < 550 MPa

• Feasibility (process) OK

Minimize mass

• Rate = 81.6 N/mm

• Out. VM stress = 782 MPa

• Inn. VM stress = 547 MPa

Mass = 4.020 kg (-42% / solid bar)

Improved shape after 63 FE runs

(Wall clock time < 1 hour)


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Conclusions

• HyperStudy was implemented in batch mode within a collaborative platform

• It helps development engineers to improve their designs while earning time

• Pre-processing time was divided by 30 !

• Post-processing time was divided by 4

• No need to be familiar with HyperStudy to use it

• Better solutions can now be proposed to our customers


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Perspectives

• Reliability-based optimization method to find an optimized robust design (consider the distribution of the design variables)

Distribution of max values due to dispersion at one point (coiling radius)

Stochastic analysis synthesis

Probability to get max > 1180 MPa


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design optimization of suspension coil springs & stabilizer bars using hyperstudy in batch mode

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