CAE-AutoWorkflow: A Framework Based on optiSLang for the Automated Build-up of Simulation Workflows Using the Example of Passenger Car ...
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CAE-AutoWorkflow: A Framework Based on optiSLang for the Automated Build-up of Simulation Workflows Using the Example of Passenger Car Aerodynamics Anke Jäger, Erich Jehle-Graf, Alexander Wäschle, Daimler AG
Content
The Aerodynamics Car Development Process
DOE-based Optimization
Influence on Fuel Consumption (WLTP)
Control of Variants and Load Cases (WLTP)
Identification of Aerodynamic Potentials in the Early Development Phase of the A-Class
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
Tools in aero development
Close Link Between CFD and Wind Tunnel Ensures High
Speed and Quality in Aero Development
Proportion model Look through model
“Aerohartmodell”
Wind tunnel
Development effort
DOE-optimization Evaluation of 1:4 and
of first proportion 1:1 design models,
models sensitivity analysis Efficient wind tunnel measurements based
Simulation on CFD-studies.
Project start Concept prove “Go with One” Model Confirmation Design freeze Prototypes start Job#1
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
Tools in aero development
DOE-based Optimization in the Early Design Phases Deliver
Precise Sensitivity Analyses
• Fully automated process with parametrized car geometry (ANSA-Morphing) = high impact on aero
• over 200 fully resolved simulations per DOE = low impact on aero
high degree of
• Very helpful in discussions with styling und car projects
freedom for styling
Key technology for a digital driven early aerodynamic development!
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
WLTP Increases the Weight of Aerodynamics Dramatically
The Drag of Each Individual Car is Base of CO2-Certification
NEDC 4 x City EUDC
Measure:
TMH (Worst-Case-Weight)
urban Extra-urban
Calculation: Worst Case Worst-Case Aerodynamics
(Interpolation)
v [km/h]
37% 63% 2 Worst-Case Rolling resistance
Weight
Aerodynamics
Rolling resistance
3 Customer
Time [s] configuration
WLTC Low Middle High Extra-High Customer
urban
Extra-urban specific
87% Best Case 1
CO2-Value
v [km/h]
13%
Measure:
TML (Best-Case-Weight)
Best-Case Aerodynamics
Best-Case Rolling resistance
Time [s]
Driving Profile facts
Double distance, +10min, +13km/h higher Ø-Speed, less Stopps
A more „real driving profile“, increased dynamic driving (= no constant driving at constant speed)
Higher weighting of extra urban driving
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
The Spread in Drag is Huge –
the WLTP Takes This Into Account
15“ steel wheels
CLA 180 BlueEFFICIENCY Edition sports trim (-15 mm)
underbody parts
cD = 0.22
117 g/km
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
The Spread in Drag is Huge –
the WLTP Takes This Into Account
16“ alloy wheels
CLA 180 cooling demand
cD = 0.23
+ 1,5 g/km
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
The Spread in Drag is Huge –
the WLTP Takes This Into Account
16“ light alloy wheel
CLA 200 cooling demand
cD = 0.25
+ 4,5 g/km
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
The Spread in Drag is Huge –
the WLTP Takes This Into Account
18“ alloy wheels
CLA 250 Sport exterior design
cD = 0.29
+ 9 g/km
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019
The Spread in Drag is Huge –
the WLTP Takes This Into Account
Expressive exterior design
CLA 45 AMG 4MATIC high cooling demand
19“ alloy wheels
4MATIC
cD = 0.33
+15 g/km
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019Without a Highly Automated CFD-Process WLTP Could not
be Handled
Customer
Requested accuracy (by law):
CO2 ± 2 % oder cD 0,005
Value
~ 40.000
cD-relevant
combinations
Best Case Engine
WLTP-L Engine
Worst-Case
WLTP-C
Step 2: optional
WLTP-LHybrid Step 3: Step 4:
WLTP-H
Evaluation of WLTP-CWLTP-L Optimization of Reporting of cD-deltas of all
Step1: Optimization of the relevant WLTP-H
WLTP-C
the relevant available car configurations
Aero-best-type configurations
WLTP-H
configurations (measurements only)
(digital + tests)
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019Variants- und Load-Case-Steering of the Fully Automated
Aero-CFD-Prozess
Base Configuration
Variant
CAE_AutoWorkflow
Meshing Template
Aero Tool/CCM+ CCM+
by Siemens Post
Enabler autoSim autoSim autoSim autoSim
• Semi automated geo data process (CAE-Cockpit)
• Standardized geo pool
• Configuration of variants in the Excel configurator
• Fully automated process (from pre- to post-processing)
• Automated result documentation in the Excel configurator
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019Aerodynamic Potentials in Early Development Stage A-Class
Radabdeckung (y)
Buglippe mitte (z) Radabdeckung (z)
13 CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019Aerodynamic of the new A-Class: cD-Record defended: cD = 0,22
Record of Aerodynamic Drag for Series Cars: cD*A = 0,49m²
• The A-Class Sedan reaches an excellent
cD-value of cD=0,22.
New A-Class
• Compared to the A-Class the cD-value
could be reduced by up to -0,030. This
corresponds to a fuel efficiency
improvement of:
- 0,15 l/100 km in WLTP New A-Class
- 0,24 l/100 km on BAB Sedan
• Based on a reduced frontal area of A =
2,19m² compared to the CLA the new
A-Class sedan reaches a new
aerodynamic drag record for series
cars of 0,49m².
CAE-AutoWorkflow: A Framework based on optiSLang, RD/ANA 2019Actual Highlight: New Drag World Champion A-Class Sedan
cD*A= 0.49 (comparable to a typical race cyclist!)
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