Calculation machines have some of the magicians in the fairy tale. - - PowerPoint PPT Presentation

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Calculation machines have some of the magicians in the fairy tale. - - PowerPoint PPT Presentation

May 23, 2023 91 likes •362 views

Die Rechenautomaten haben etwas von den Zauberern im Mrchen. Sie geben einem wohl, was man sich wnscht, doch sagen sie einem nicht, was man sich wnschen soll . Calculation machines have some of the magicians in the fairy tale. They might

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Die Rechenautomaten haben etwas von den Zauberern im Märchen. Sie geben einem wohl, was man sich wünscht, doch sagen sie einem nicht, was man sich wünschen soll. Calculation machines have some of the magicians in the fairy

  • tale. They might give you, what you wish, but they don’t tell you,

what you should want. (Norbert Wiener (1984-1964), Professor at MIT)

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How DoE Makes Vehicle Dynamics Simulation Intelligible And Efficient A bit of magic help in tuning vehicle dynamics

Benjamin Kanya, AVL

  • Dr. Hans – Michael Koegeler, AVL
  • Dr. Felix Pfister, AVL

Richard Hurdwell RHE

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  • Calibrating vehicle chassis parameters is always a compromise and

simulation can provide a sensible starting point for real vehicle tuning

  • This is a glimpse at how optimisation techniques can help reach this starting

point and make effects of compromise more understandable to the engineer

  • It will be demonstrated by simple examples using limited variables
  • The scenarios are based on settings for a hypothetical rally car where the

focus is on optimising vertical ride and tyre grip performance

Background

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Scenario 1 “ Flat” Primary Ride (minim pitch disturbance)

  • An existing rally car has been fitted with an electric drivetrain.
  • Front /rear weight distribution has been fixed but location of the 2 batteries

( 1 front 1 rear) can be adjusted to adjust the pitch inertia.

  • To save money only the rear spring rate is allowed to be changed , Front

spring and all dampers are fixed

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The Optimisation Challenge

  • The Vehicle’s Dynamics” must make it safe as possible by keeping the wheels

well connected to the ground and its responses consistent and predictable

  • Any “Vehicle’s Dynamics” must match the “Driver’s Dynamics” requirements

for:

  • Confidence
  • Controllability
  • Agility
  • Speed
  • Comfort

depending on the driving experience required

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“ Flat” Primary Ride

  • To test this the vehicle is driven over a long wavelength bump .
  • First Objective
  • At first recovery from the bump, front and rear axle heights should be in

phase and of equal amplitude , giving a pitch free attitude.

  • Additional Objective
  • minimise pitch velocity over the whole manoeuvre (less driver distraction)
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Characteristics observed to judge: Reference Condition (not flat ride)

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Δ Amplitude Δ time

max_rear max_rear_time max_front max_front_time

Scenario 1 “ Flat” Primary Ride ( i.e. minimising pitch)

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CAMEO – on a steady state TEST BED SYSTEM Calibration needs reproducible measurements

Asap 3

CAMEO ACI

Asap 3

Control unit (EMCON) Fuel Consumption (753) Indicating (Indimaster) Emission bench (CEB) Smokemeter, Opacimeter PUMA ECU ENGINE DYNO Application System

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CAMEO ACI

Asap 3

CarMaker as Simulation System and CAMEO:

CarMaker System

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Calibration Workflow using CAMEO

(Office Environment) Task definition Test planning Run Test Modelling Optimisation

Map Generation & Verification

  • Definition of the manoeuvre to be optimized:
  • the vehicle is driven over a long wave bump
  • Definition of the optimisation target
  • the vehicle has subjectively “flat” primary motion:

 Minimize Δ Amplitude at Δ time=0

  • Definition of the factors (variation parameters to be changed)
  • pitch inertia and rear spring rate
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  • Create the right manoeuvre in IPG-Carmaker and apply

“named values” for the parameters to be varied: e.g.:

  • Define the formulas for the calculations of the

characteristic result values

  • Create test plan in respect to Design of Experiment (DoE)

Task definition Test planning Run test Modelling Optimisation

Map Generation & Verification

Calibration Workflow using CAMEO

(Office Environment)

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Task definition Test planning Run Simulations Modelling Optimisation

Map Generation & Verification

Calibration Workflow using CAMEO

(Office Environment)

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Test Maneuver

IPG CarMaker

DoE / Optimizer

AVL CAMEO

Run the Simulations in direct link between: Inputs

  • Rear spring

rate

  • Pitch Inertia

Outputs

  • Δ Time
  • Δ Amplitude
  • tyre load variation
  • vertical load

variation

  • max vertical

defelction in CG

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Test Maneuver

IPG CarMaker

DoE / Optimizer

AVL CAMEO

Run the Simulations in direct link between CAMEO and CarMaker: Inputs

  • Rear spring

rate

  • Pitch Inertia

Outputs

  • Δ Time
  • Δ Amplitude
  • tyre load variation
  • vertical load

variation

  • max vertical

defelction in CG

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Task definition Test planning Run test Modelling Optimisation

Map Generation & Verification

  • Fit the Characteristic result values

e.g.: Δ time = as f(pitch inertia and rear spring rate)

Calibration Workflow using CAMEO

(Office Environment)

pitch inertia rear spring rate Δ time

3D-View of the model as f(2 Parameters)

rear spring rate pitch inertia Δ time

IntersectionView

  • f the model as

f(2 Parameters)

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Δ time Δ Amplitude rear spring rate pitch inertia Task definition Test planning Modelling Optimisation

Map Generation & Verification

Calibration Workflow using CAMEO

(Office Environment)

Run test

  • at Δ time=0

Δ Amplitude Δ time Δ Amplitude

  • Minimize Δ Amplitude

pitch inertia rear spring rate rear spring rate pitch inertia Factor: 1.338 1548 kg*m^2

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Scenario 2 Maximising tyre contact & minimising pitch velocity

  • Now what is about the load variations on the tires as well as

the overall pitch velocity?

  • The team have now been allowed a budget to tune the dampers as well, to
  • minimise pitch velocity over the whole manoeuvre
  • but keep also the flat ride condition!
  • These items are now allowed to be optimised
  • rear damper settings (Compression and Rebound)
  • rear springs
  • pitch inertia
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So, minimize pitch velocity and keep “flat ride condition” (the picture is just a place holder for the live Demonstration)

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Task definition Test planning Modelling Optimisation Map Generation & Verification

Calibration Workflow using CAMEO

(Office Environment)

Run test

12,0 12,5 13,0 13,5 14,0 14,5 15,0 15,5 16,0 16,5 17,0

Time [s] Bodymovement above axle [m]

  • 0,15
  • 0,10
  • 0,05

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 rear base condition front base condition 12,0 12,5 13,0 13,5 14,0 14,5 15,0 15,5 16,0 16,5 17,0

Time [s] Bodymovement above axle [m]

  • 0,15
  • 0,10
  • 0,05

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 rear good condition front good condition rear base condition front base condition

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Flat ride animation:

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Scenario 3

Maximum tyre contact & minimum body acceleration

  • n a bumpy road
  • Using the rear spring rate and the pitch inertia from the flat ride

both tyre grip and comfort on rough surfaces needs to be minimised.

  • The team have now been allowed a budget to tune both front and

rear dampers.

  • Engineers need a graphic way to understand the compromise
  • Using the data presented as a “Pareto Front” enables them to

make an informed assessment of the best compromise

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Trade off: “Tire load variation”  “body acceleration”

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Feedback of the trade off decision into the intersection plot:

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Summary

  • By combining IPG-CarMaker and AVL-CAMEO we have shown:
  • In scenario1) a “Flat ride condition” was optimized with just adapting

pitch inertia and rear spring rate when driving over a long wavelength bump

  • this “Flat ride condition” was further improved in scenario 2) by also

adapting the rear damper settings ( Compression and Rebound) in order to minimize the overall pitch velocity

  • and finally in scenario 3) –

keeping the pitch inertia and the rear spring rate fixed - this settings was further refined to optimize “tire load variation” and “body acceleration”

  • by Visualizing the trade off behavior, a decision regarding front and rear

damper settings ( Compression and Rebound) could be made

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Conclusion

  • „Computers have some of the magicians in a fairy tale. They give you

perhaps, what You whish, but they dont tell You, what You should want“ Norbert Wiener (1984-1964), Professor at MIT  Task planning

  • Getting a good starting point for development of a real vehicle and making

choices about meeting conflicting targets in an engineering challenge

  • identify workable combinations of tunables

and

  • User friendly visualisation of conflicting solutions helps the engineer make

more informed judgements for both the virtual and the real vehicle