Computer aided engineering applied to engine design · GT-Power UGM - Saclay RESEARCH &...

RECHERCHE & DEVELOPPEMENT

Computer aided engineering applied to engine design

GT-Power UGM - Saclay

RESEARCH & DEVELOPMENT

Clément Dumand - clement.dumand@mpsa.com

Team Manager in Scientific & Future Technologies Department

May 17th 2017

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2016 2025 2020

Present

Digital Validation Boost

Full Digital Ambition

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Nowadays status in Groupe PSA

3

Level 4 : No Physical Test needed

Level 3 : Good maturity of Simulation

/ No Physical adjustment needed

Level 2 : Physical adjustments are

needed to reach performance target

Physical Testing validation cost

for which CAE is used (%)

Physical Testing validation cost

for which CAE is NOT used (%)

2025 target is to have CEA everywhere with

a confidence index higher or equal to 3

Confidence / fidelity index

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11 MAIN CAE FIELDS

4

Functional Simulation

Multi Body

Finite / Volume Element

Legend :

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Part design challenges – Nowadays methods

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Most of designs are iterative and “manual” design using a single simulation tool

Challenges :

• To improve models accuracy

• To improve simulation cost / complexity

• To define new criterions for numerical design

High fidelity

Tool

User

Constraints /

Objectives

Best

tradeoffs

First steps of the full

digital design project

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Part design challenges – Nowadays methods

6

Simulation domain tends to grow : multi-physics simulation or co-simulation

Challenges :

• To avoid to many interface between codes all in one code

• To manage complexity (high number of parameters, objectives, constraints, etc.)

High fidelity

Tool A

High fidelity

Tool B

Users

Constraints /

Objectives

Best

tradeoffs

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Engine

acoustics

Combustion

process

Supervisor

Combustion systems design strategies

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Speed

Torq

ue

1D / 3D coupling for

each operating points

Combustion system optimization for

several operating conditions

Simulation complexity tends to grow : tools managed by automatic optimization methods

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Automatic optimization of combustion system

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For a single operating condition

Parametrized piston shape

Supervisor

Constraints /

Objectives

Best

tradeoffs

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Automatic optimization of combustion system

9

For a single operating condition

Optimization result

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Automatically design combustion systems for gasoline

and Diesel engines

Objectives :

• To maximize the whole efficiency and max torque curve

• To maximize the exhaust temperature for aftertreatment systems

lightoff (Diesel part loads, gasoline starting)

• To fulfill constraints (noise, emissions targets, stability criterion,

mechanical limits)

Final outputs : Overall gains / maps

IFSC Torque

2020 objectives – Combustion system design

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2020 objectives – Remaining challenges to achieve full digital design (gasoline engines)

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To use CFD to calibrate 1D combustion model

• To determine intake and exhaust pressure drops (steady

state simulations)

• To find a way to calibrate combustion speed and knock

limits in 1D from 3D results

To improve models accuracy

• Knock

• Soot (PN / PM)

To enrich optimization with new criterions

• Combustion stability

• Low speed pre-ignition

Supervisor

Constraints /

Objectives

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Complex system approach, with a global overview of the whole system : Engine example

• Engine BSFC – cycle fuel consumption

• Raw emission and after treatment behavior (transient)

• Engine cooling and part thermo-mechanical stress (transient)

• Engine friction

• Engine control

We need global simulation tools in order to simulate the whole complex system

Challenges :

• Simulation accuracy

• Interface with specific codes

• Simulation time (up to real time)

• Global optimization

2020 objectives – Complex system validation

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2020 objectives – Complex system validation

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A global and collaborative simulation for complex systems

High fidelity

Tool A

High fidelity

Tool B

Supervisor

All in one or

co-simulation

tool

High fidelity

Tool B High fidelity

Tool B

High fidelity

Tool A High fidelity

Tool A

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2025+ objectives – Continue to improve the conception efficiency

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More and more complex systems

Artificial Intelligence to manage the design process

AI Data base

Low fidelity

tools

High fidelity

tools

Constraints /

Objectives

Best tradeoffs

Management

strategies

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Full digital design

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2025 target

Global optimization

Supervisor AI for design

Simulations accuracy & speed-up

New criterions for

robust design

Complex systems

simulations

4 topics have to be improved

Labs

Industries

& Labs

Codes suppliers

& Labs

Industries &

codes suppliers

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Thanks for your attention