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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Dra

g C

o-e

ffic

ien

tOptimisation 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 A.Prem

Lightweight Vehicle Body structures 2015, Birmingham, United Kingdom


Lightweight Vehicle Body structures 2015, Birmingham, United Kingdom Crashworthiness Optimisation of TARF – LCV Structure Using Multidisciplinary Design Optimisation A.Prem

4

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.



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

Mas

s In

cre

ase

%

Drivelines

Topology Mass Comparison

42.84 47.41 54.88

0

20

40

60

80

100

Steel Aluminium Magnesium DCFPMas

s R

ed

uct

ion

%

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


6

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


7

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

Mas

s R

edu

ctio

n %

Materials

Mass Comparison between all materials studied

8.471%


8

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


• 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

Acc

ele

rati

on

(m

m/s

ec²

)

9

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

Intr

usi

on

(m

m)

Position

Footwell intrusion Constraints Baseline Model

Baseline model


10

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

Intr

usi

on

(m

m)

Position

Exterior intrusion Constraints Baseline Model


11

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

Intr

usi

on

(m

m)

Position

Exterior intrusion Constraints Baseline Model


12

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

To

rsio

nal S

tiff

nes

s

Constraint-Lower Bound

Baseline


13

• 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


14

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


15

Design Of Experiments (DOE)

• 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


• 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.

16

Design Of Experiments (DOE)

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


17

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


18

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


19

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.


• 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.

20

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


Structural Optimisation: Improved Accuracy

21

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


22

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


23

Acknowledgements


TARF Coventry Team: • Christophe Bastien

• Jesper Christensen

• Oliver Grimes

• Charles Kingdom

• Gary Wood

• Mike Dickison

TARF Consortium

EPSRC

24

Thank you


tarf-lcv_amit_prem_lightweight vehicle body structures 2015

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