TARF-LCV_Amit_Prem_Lightweight Vehicle Body structures 2015

Towards the Affordable
Recyclable Future – Low Carbon Vehicle 1
Crashworthiness Optimisation of TARF - Low Carbon Vehicle Structure Using
Multidisciplinary Design Optimisation
Amit Prem
Lightweight Vehicle Body structures 2015, Birmingham, United Kingdom
TARF-LCV

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DragCo-efficient Optimisation Stages
Stage 4
Stage 1
Stage 2
Stage 3
Stage 5
Stage 6
Integration of the Initial
spoiler
Final
Design
2
TARF Background
a. Design envelope & Aerodynamics
Target
CD-0.25
Governing Factors for the Design Envelope
• Aerodynamics
• Packaging requirements
Stage 1: Open rear
wheel arches
Stage 2: Flat Underbody
& rear diffuser
Stage 3: Rear Spoiler
Stage 4: Front Curtains
Stage 5: Rear Diffuser
Optimisation
Stage 6: Front and Rear
Wheel Arch Slots
Final
CD-0.23
Raised spoiler height
Crashworthiness Optimisation of TARF – LCV Structure Using Multidisciplinary Design Optimisation
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TARF Background
b. Topology & Sizing Optimisation Engine
Vehicle Front
Fuel Tank• Topology was conducted on 5 drivetrain
possibilities to define the load paths.
• The Loads used were calculated for a vehicle
kerb weight of 1000kg.
• Inertia relief was utilised, balances external
loading with inertial loads and accelerations
within the structure.
• Masses for the components were added through
mounting location.
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TARF Background
1.04
10.41
3.84 4.5
16.75
0
5
10
15
20
ICE-1 ICE-2 HEV-Volt HEV-Prius FEV REHEV
MassIncrease%
Drivelines
Topology Mass Comparison
42.84 47.41
54.88
0
20
40
60
80
100
Steel Aluminium Magnesium DCFP
MassReduction%
Materials
Material Models Mass Comparison
• Topology was greatly affected by
component masses and mounting
location.
• 1 D beam model sizing optimisation
produces an optimised beam for a
defined load path.
• Euler Buckling was utilised to
calculate the critical buckling force
for the A-pillar members
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Front end structure works in buckling
i. Topology results do not take into account large
deformation or plasticity of the structure.
ii. Requires explicit solver
• Federal motor Vehicle Safety Standard test no.208,
full frontal vehicle collision against a rigid wall
• Test speed of 56 km/h (35 mph)
• Octagonal profile selected for the Longits based on
data from LCVTP
• Design iterations were carried out to find the
optimum response
TARF Background
c. Front Crash Structure Development Displacement
Acceleration
Front Crash Structure- Aluminium
Constraints Achieved target
Section force (kN) 300 172.3
Acceleration Magnitude (g) 40 34.3
Displacement (mm) 630 623.5
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LS-OPT gauge optimisation was carried out on
every component in the front crash structure
• Sequential with domain reduction was
selected as the optimisation strategy.
• Main objective of the optimisation was
reduction of mass.
• 55 D-optimal points were used for the initial
simulation runs to find the optimum solution.
• Material replacement studies were also
conducted to achieve further mass savings.
TARF Background Rigid
Bulkhead
Inner
Longit RHS
Bracket Crush Cans
Bumper
Beam
Outer Longit
LHS
Shotgun
LHS
Turret
RHS
Tower
LHS
49.44% 49.88%
0
20
40
60
80
100
Opt:Steel Opt:Aluminium Opt: Aluminium-Magnesium
MassReduction%
Materials
Mass Comparison between all materials studied
8.471%
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Instrumentation
Side and Pole Impact
• Springs along 4 locations measuring Intrusion
- Intrusion1_PI&SI (Window Top)
- Intrusion2_PI&SI (Window Sill )
- Intrusion3_PI&SI (Occupant H-point )
- Intrusion4_PI&SI (Sill)
Frontal Offset Deformable Barrier(ODB) Impact
• Springs along Footwell measuring Intrusion
-Intrusion1_FC
-Intrusion2_FC
-Intrusion3_FC
• Accelerometers placed on the Sill under each
B-pillar to measure vehicle acceleration
Note: Seats are for representation and adds no structural support in this model
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• Simulates vehicle collisions
• Offset deformable barrier (ODB)
• Vehicle moves towards barrier at 64 km/h (40mph)
• Impacts 40% frontal overlap to ODB
• Acceleration measured through accelerometers
(SAE60Hz filter)
• Passenger compartment Intrusion and Vehicle
acceleration being monitored
480000
445377
420000
430000
440000
450000
460000
470000
480000
490000
FC_acceleration
Acceleration(mm/sec²)
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a. Frontal ODB Impact
20
30
20
13.47
26.37
5.39
0
5
10
15
20
25
30
35
FC1_Intrusion FC2_Intrusion FC3_Intrusion
Intrusion(mm)
Position
Footwell intrusion Constraints Baseline Model
Baseline model
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Baseline model
b. Vehicle to Vehicle Impact (Side Impact)
• Simulates vehicle collisions
• Mobile deformable barrier (MDB) mass 950 kg
• Moves towards vehicle at 50 km/h (31mph)
• Impacts perpendicularly to vehicle side
• Passenger compartment Intrusion being
monitored
20
145
232
150
1.36
52.26
164.99
76.41
0
50
100
150
200
250
SI1_Intrusion SI2_Intrusion SI3_Intrusion SI4_Intrusion
Intrusion(mm)
Position
Exterior intrusion
Constraints Baseline Model
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Baseline model
• Simulates collisions with narrow fixed objects
(i.e. lampposts, trees)
• 254 mm diameter pole
• Vehicle propelled sideways at 29 km/h (18 mph)
into a narrow rigid pole
• Impacts perpendicularly to vehicle side
• Passenger compartment Intrusion being monitored
c. Pole Impact
151
320
349
318
47.76
245.51
275.52
214.04
0
50
100
150
200
250
300
350
400
PI1_Intrusion PI2_Intrusion PI3_Intrusion PI4_Intrusion
Intrusion(mm)
Position
Exterior intrusion Constraints Baseline Model
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Baseline model
d. Torsion
• Displacement applied at the end of a load limiting spring
on each wheel centre.
• Load limiting spring transfers a maximum force of 1250N
onto each wheel centres.
• Rear Turrets of the vehicle is fixed
• Torsional stiffness is calculated using the following
equation:
Torsional Stiffness =
𝑀𝑜𝑚𝑒𝑛𝑡 𝑖𝑛 𝑋
𝑇𝑜𝑟𝑠𝑖𝑜𝑛 𝑎𝑛𝑔𝑙𝑒
• Torsional Stiffness (baseline) = 9.483 kNm/deg
8.8
9.483
8.7
8.8
8.9
9
9.1
9.2
9.3
9.4
9.5
9.6
TorsionalStiffness
Constraint-Lower Bound
Baseline
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• Multidisciplinary design optimisation
is a process where multiple disciplines
such as Crash, NVH or Torsional Rigidity
are included within a single optimisation.
• Metamodel based optimisation can be
employed in order to minimize the
computational time needed for design
exploration where design surfaces are
fitted through points in the design space
to construct an approximation to the
design response, the metamodel can
then be used instead of actual
simulations to find the optimum variables
Multidisciplinary Design Optimisation
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Design Of Experiments (DOE)
Variable
Number
Variable
Name
Part Description
1 B_Pillar Door: B-Pillar L/R
2 Bonnet Bonnet
3 Door_B1 Door: Beam1 L/R
5 Door_In Door: Inner L/R
6 Floor_Sa BIW: Floor-reinforcement L/R
7 Floor_Tu BIW: Floor-Tunnel
8 Lower_A BIW: Lower-A-Pillar-Reinforcement L/R
9 Roof_Pa BIW: Roof-Panel
10 Seat_Pa BIW: Rear-Seat-Panel
11 Side_Pa BIW: Side-Panel L/R
12 Sill_In BIW: Sill-Inner L/R
13 Sill_Out BIW: Sill-Outer L/R
14 Wheel_Pa BIW: Wheel-Arch-Panel L/R
15 mat_B1 Door: Beam1 L/R (800, 1019, 1143)
16 mat_B2 Door: Beam2 L/R (800, 1019, 1143)
17 mat_BP Door: B-Pillar L/R (800, 1019, 1143)
• The TARF model consists of a number of
assumptions: Panel Thickness/ Material Grade
• Global Response of the TARF vehicle for different
loadcases has not been studied extensively.
• Sensitivity analysis would provide a better
understanding of the structure and help in
eliminating redundant variables.
• 17 Variables were chosen for the DOE study.
• 14 panel thickness variables and 3 discrete
material variables.
• All variables would be fully shared.
• Polynomial Metamodel with Space filling sampling
method was considered for the DOE.
• Passenger compartment Intrusion, Vehicle
acceleration and mass difference attributed to
change in variables were monitored.
Constraints Upper Bound Constraints Upper Bound
FC1_Intrusion 20 mm PI3_Intrusion 349 mm
FC2_Intrusion 30 mm PI4_Intrusion 318 mm
FC3_Intrusion 20 mm SI1_Intrusion 20 mm
FC_acceleration 480000 mm/s2 or 48.9g SI2_Intrusion 145 mm
PI1_Intrusion 151 mm SI3_Intrusion 232 mm
PI2_Intrusion 320 mm SI4_Intrusion 150 mm
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• From The GSA/Sobal’s Indices
(a Variance based sensitivity analysis)
the influence of the variables on the
responses can be obtained.
• 7 feasible design solutions were
obtained.
• Two constraints dominated the
feasibility of the solutions:
FC2_Intrusion and Torsional Stiffness
lower bound.
• The residuals of pole, side and torsion
loadcases indicates a good fit for the
size of the design space and sample
set.
• Noise captured in the residuals for
front crash maybe attributed to the
highly non linear buckling behaviour
and also due to the nature of the
problem.
• Redundant variables were eliminated
for the optimisation phase based on
the GSA.
Global Sensitivity Analysis: Pole Impact
Residuals plot:Intrusion4_SI
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• The potential mass savings were
highlighted from the DOE study.
• The most influential variables were
considered for the optimisation phase.
• Variable values from run 1.31 would
be taken into account for further
optimisation studies for the remaining
variables and kept constant.
• The two constraints FC2_Intrusion
and torsional stiffness lower bound
would be revised for the optimisation
phase.
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Loadcase Important Variables
Frontal ODB Door_In; Sill_In; Lower_A;
mat_B2
Pole Impact Sill_In; Door_B1; Side_Pa;
Door_B2
Side Impact Door_In; Side_Pa; Sill_In; B_Pillar
Torsion Floor_Sa; Side_Pa; Floor_Tu;
Sill_In
Global Sensitivity Analysis: Mass
Mass Reduction potential from DOE
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Structural Optimisation: Single Stage optimisation
• Ideal for a limited simulation budget requires a large
sample set for good metamodel accuracy based on
the problem at hand.
• This method is good for design exploration.
• 8 variables were considered for the optimisation
phase
• 7 panel thickness variables and 1 discrete material
variables
• All variables were fully shared.
• Metamodel which can capture complex response
and predict accurately with flexible sample sets are
needed for automotive application.
• RBF Metamodel
-Transfer function: Hardy’s Multi Quadrics
-Topology selection criteria: Noise variance
• Space filling sampling method
• Hybrid Algorithm: ASA with LFOPC
Variable
Number
Variable
Name
Part Description
1 B_Pillar Door: B-Pillar L/R
2 Bonnet Bonnet
5 Door_In Door: Inner L/R
6 Floor_Sa BIW: Floor-reinforcement L/R
7 Floor_Tu BIW: Floor-Tunnel
8 Lower_A BIW: Lower-A-Pillar-Reinforcement L/R
9 Roof_Pa BIW: Roof-Panel
10 Seat_Pa BIW: Rear-Seat-Panel
11 Side_Pa BIW: Side-Panel L/R
12 Sill_In BIW: Sill-Inner L/R
13 Sill_Out BIW: Sill-Outer L/R
14 Wheel_Pa BIW: Wheel-Arch-Panel L/R
15 mat_B1 Door: Beam1 L/R (800, 1019, 1143)
16 mat_B2 Door: Beam2 L/R (800, 1019, 1143)
17 mat_BP Door: B-Pillar L/R (800, 1019, 1143)
Constraints Upper Bound Constraints Lower Bound
FC2_Intrusion 40 mm Torsional_Stiffness 8500000 Nmm/deg
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Structural Optimisation: Single Stage optimisation
Scenarios Response Optimisation Baseline Optimum Upper Bound Units
Frontal Crash
FC1_Intrusion 19.93 17.65 20 mm
FC_acceleration 400485 431352 480000 mm/s2
Pole Impact
PI1_Intrusion 49.29 87.42 151 mm
PI2_Intrusion 260.58 294.29 320 mm
PI3_Intrusion 296.32 329.86 349 mm
PI4_Intrusion 233.73 254.19 318 mm
Side Impact
SI1_Intrusion 1.79 1.85 20 mm
SI3_Intrusion 171.75 203.77 232 mm
SI4_Intrusion 102.73 132.37 150 mm
Torsion Torsional_stiffness
8992200 8646970 8500000
(Lower Bound)
Nmm/deg
• 15.58% or 14.08kg mass reduction was
achieved
• All constraints were satisfied
• RBF has good predictive capability and is a
viable candidate for automotive application
• A total of 29.26kg mass saving was achieved
through the DOE+Optimisation stages
• Improved accuracy can be achieved by using
sequential or increasing the size of the sample
set
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Structural Optimisation: Improved Accuracy
• Sampling Points were increased to 163 per
simulation.
• Improved accuracy resulted in a better
prediction of the optimum solution.
• Complex responses due to buckling such as
the front crash requires even more simulation
points.
• Similar percentage of mass reduction.
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• It is important to identify outliers, their influence
reduces with larger sample sizes.
• Best practises for response monitoring is
critical.
• Identifying the cause of the outliers can result
in an improved model.

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Scenarios Response Computed Predicted Upper Bound Units
Frontal Crash
FC_acceleration 41.36 44.62 48.94 g
Pole Impact
PI1_Intrusion 92.35 89.72 151 mm
PI2_Intrusion 297.97 292.30 320 mm
PI3_Intrusion 336.55 332.26 349 mm
PI4_Intrusion 261.16 262.41 318 mm
Side Impact
SI3_Intrusion 219.82 214.89 232 mm
SI4_Intrusion 149.45 149.99 150 mm
Torsion Torsional_stiffness
8709250 8821360 8500000
(Lower Bound) Nmm/deg
• Comparison between predicted and computed optimum results
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Structural Optimisation: Improved Accuracy

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Conclusion
a. Frontal ODB Impact • The optimum solution highlighted an issue
with the upper A-pillar T junction
• Causes:
-Reduction in lower A-pillar reinforcement
gauge (Lower_A)
-Lower grade Material
-Lack of additional reinforcement
A-Pillar T Junction
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b. Vehicle to Vehicle Impact (Side Impact) c. Pole Impact
• Multidisciplinary design optimisation has been a very
useful tool in identifying the global behavior of the
TARF vehicle structure
• The sensitivity analysis conducted through the DOE
study was helpful in identifying the importance of the
variables
• The LS-Opt study facilitated for a significant reduction
in the mass of the vehicle
• Improved accuracy was obtained by increasing the size
of the sample set
• Complex buckling response such as the front crash
would require more number of simulation points.
Conclusion
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Acknowledgements
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TARF Coventry Team:
• Christophe Bastien
• Jesper Christensen
• Oliver Grimes
• Charles Kingdom
• Gary Wood
• Mike Dickison
TARF Consortium
EPSRC

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Thank you
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