New Automated MultiˆDisciplinary Optimization (MDO) Process … · 2016. 2. 20. · Design1...

Vehicle NVH Europe

Acoustic

Centre

CologneVehicle NVH Europe

Acoustic

Centre

Cologne

24�November�2011 FordProprietary

Automated Multi�Disciplinary Optimization

(MDO) Process Development and Application

on Vehicle Program

Norbert Hampl, Vehicle NVH CAE, Ford Werke GmbH

Giri Nammalwar, Global CAE, Ford Motor Company

Presentation to WOST 8.0

based on a Presentation by

Giri Nammalwar, Behrooz Shahidi, Bruno Barthelemy, Nand Kochhar (all Ford Motor Company), Nitin Sharma, RadhaKrishnan (both DEP Inc., Troy, MI USA) to

2011 SIMULIA Customer Conference

Barcelona, Spain, May 18, 2011

of 22 Automated MDO Process on Vehicle ProgramFordProprietary

�Develop tools and technologies to significantly reduce the

total execution time of full vehicle Multi-Disciplinary

Optimization (MDO).

� Implement MDO process on a vehicle program to achieve

weight reduction while maintaining performance for

Crash, Body NVH & Full vehicle NVH

�Deploy the automated MDO process and associated tools

and techniques on all future Ford vehicle programs

Objectives


�Long time to parameterize detailed full vehicle models

�Lack of robust tool to compare design content

�Extremely long design generation time

�Limited design space exploration

�Lack of tools to automatically post-process large number

of runs

Current Challenges


�Single parametric model for all attributes

�Modular and scalable morphing methodology

�Model synchronization tool

�Designer Environment to submit and post-process

multiple jobs

�CAD morphing to generate CAD data for the optimized

design

Required Technologies and Processes


Automated (MDO) Process Flow

MESHWORKS

(Common Control-

Blocks to parameterize

models from different

disciplines)

Crash

Design1

Design2

Design3

:

:

Design ‘n’

NVH (BODY)

Design1

Design2

Design3

:

Design ‘n’

Durability

Design1

Design2

Design3

:

:

Design ‘n’Isight

(Optimizer)

DOE

Design Variables

& Limits

Input-Output

matrix

Response

Surface

Optimized design

Objectives

Constraints

Ford in-house process

NVH Solver

Crash Solver

Ford & Meshworks scripts

UNIFY

Baseline - Crash

Base – BODY NVH

Base - Durability

Base – VEH. NVH

Ford in-house Vehicle NVH Solver

• First step is to unify Crash, NVH & Durability models

• MDO Process integrates the Parametrized full vehicle CAE

models with Ford’s solvers & output processing scripts

• Tools developed to fully automate the design generation,

submission and results extraction process


�Attributes chosen for MDO were Safety, Body NVH, Vehicle NVH and Durability.

�Model synchronization to commonize design content across different attributes.

�Safety model was parameterized to have shape, weld and gauge variables.

�Critical shapes and sections @ A, B, C, D pillars, roof header and rocker, weld pitch at key locations and 60 BIW component gages were included as parameters

�Parametric models of Body NVH, Vehicle NVH and Durability were generated from safety model.

Process Application on Vehicle Program


MDO –Attributes and Load Cases

Offset Frontal

Body NVH Side Impact

Roof Crush

Vehicle NVH


Attribute Model Synchronization

Crash Model NVH Model

COMP10078

DG93-F02908

Comp-name Image

COMP150007S71-A02509

Image Comp-name

• MESHWORKS ‘Syncing’ geometrically compares every part on

Crash modes with every part on NVH model

• Provides % geometric matching of parts (even though part names

and mesh sizes are different)

• Applies parameters from one model to the other


Comprehensive Parametrization

Motor Compartment Weld

Variable

• Complex sections with

multiple parts can be

parametrized rapidly and

easily

• Automatic conversion of spot

welds to weld lines

• Control of weld pitch or no. of

welds on each weld line

C-Pillar Section Variable

D-Pillar Section Variable

Gauge of

60 BIW

parts


�DOE matrix generated through Isight using Optimal

Latin Hypercube Method

�The DOE designs were generated in batch mode

using DEP Meshworks

�Design generation through parallel processing using

multiple Vista machines to achieve fast turnaround

time

Automated Design Generation


G a u g e P a r a m e t e r S h a p e P a r a m e t e r W e l d P a r a m e t e r Macro

Para

meter

Crash NVHNCAP Roof LINCAP IIHS Trim Model BIW Durability Model

• Isight was used for DOE matrix generation

• All models for 440 designs generated as a batch process

driven through DEP Meshworks

• Completely automated and robust process

DOE-Matrix & Design Generation


Safety

NVH

Durability

Job Completion StatisticsJob Completion Statistics

Safety Body CAE DurabilityVehicle NVH(2)

# of Runs

Time (days)(1)

1,776 2,6642,664 444 444

16 5 1 5

Meshworks Design Environment

HPC

Scripts

Python

Scripts

Automated Job Submission


�Tool developed within Meshworks Designer

Environment to automate multi-attribute response

extraction for crash, body NVH and vehicle NVH

results.

�Ford’s in-house processes of results extraction were

seamlessly integrated with DEP Meshworks for

complete automation.

�Significant time reduction achieved by automatically

extracting (using batch process) over 100 responses

from about 3000 LS-Dyna, Nastran runs.

Multi-Attribute Response Extraction


• NCAP

• IIHS

• LINCAP

• Roof

SafetySafety Body CAEBody CAE

• Global Torsion Mode

• Global Bending Mode

• Noise and Vibration

Transfer Functions

• Equivalent Stiffness

• Dynamic Stiffness

• Bending Stiffness

• Torsion Stiffness

DurabilityDurability

• Cumulative Damage

Vehicle NVHVehicle NVH

• P/T (@idle rpm)

• Rough Road

Output Responses Tracked

WEIGHTWEIGHT


� Isight was used to evolve optimized designs using

Response Surface (RSM) Based Optimization

�Combination of Numerical Sequential Programming

and Genetic Algorithms were used to obtain optimal

design

�Models corresponding to optimal designs were

generated and validated for different attributes

RSM Based Optimization


Response Surface Model (RSM)

MESHWORKS

(Common Control-Blocks to parameterize

models from different disciplines)

Crash

Design1

Design2

Design3

:

:

Design ‘n’

NVH (BODY)

Design1

Design2

Design3

:

Design ‘n’

Durability

Design1

Design2

Design3

:

:

Design ‘n’Isight

(Optimizer)

DOE

Design Variables

& Limits

Input-Output

matrix

Response Surface

Optimized design

Objectives

Constraints

Ford in-house process

NVH Solver

Crash Solver

Ford & Meshworks scripts

UNIFY

Baseline - Crash

Base – BODY NVH

Base - Durability

Base – VEH. NVH

Veh. NVH Solver

RSM FOR VEH. NVH OUTPUTS

• Response Surface Models (RSM) were created for all the responses –

Crash, Body NVH and Vehicle NVH

• Accuracy analysis was carried out by comparing RSM predictions with

actual solver results

• This RSM forms the basis for subsequent optimization & sensitivity

studies


RSM FOR DYN. STF

Optimization Scheme using RSM

• Several optimization runs were carried out using the RSM

• Different choices of constraints and their levels were tried out

• Different optimization algorithms were tried out

• This resulted in several optimal designs with different degrees

of mass saving and associated performance levels

• Constraints were set in general at baseline or target levels


Insight into design space using Pareto plots

• Pareto plots were created to study the ‘contribution’ of

each design variable to a chosen output response


-1.50E-08

-1.00E-08

-5.00E-09

0.00E+00

5.00E-09

1.00E-08

1.50E-08

2.00E-08

0.0 19.8 39.7 59.5 79.4 99.2

Baseline

Design 1

Design 2

Attribute Confirmation Runs - Results

Body NVH: Noise Transfer Function Vehicle NVH: Steering Wheel Response

Baseline

Optimized Design

Safety: Acceleration Response

Baseline

Design 1

Optimized Design

Baseline

Design 1

Optimized Design

Weight Savings

of 5 lbs.


Metric for StartMetric for StartMetric for StartMetric for Start��Up Vibrations:Up Vibrations:Up Vibrations:Up Vibrations:

( )4

0

4

,, ∫=

=

T

t

wzyx dttaVDV 4444

Zyx VDVVDVVDVRSQ ++=

• aw (t) is the frequency�weighted acceleration in time�domain.

• T is the signal duration.

C344_gf_base: 250.32C344_gf_base: 250.32C344_gf_base: 250.32C344_gf_base: 250.32

C344_gf_3421: 263.46C344_gf_3421: 263.46C344_gf_3421: 263.46C344_gf_3421: 263.46

Single-Number Metrics


�High Degree of Automation

�Wide Range of Design Parameters

�Automated Synchronization Across Attributes

�Rapid Throughput during design exploration

�Practical Application at any stage of the Vehicle

Development

Key Benefits of Automated MDO Process


� Developed an automated MDO Process and successfully applied the process on a vehicle program to realize 5 lbs of weight savings.

� Addressed many of the challenges that exists today in a traditional MDO process by leveraging the current technology and by developing new tools to seamlessly integrate Ford Processes.

� Tools and techniques developed enable Ford to execute Full vehicle MDO several times during the product development phases

� Early stages of product development with a focus to optimize and develop vehicle architecture

� During the platform development phase to optimize the underbody components

� Finally during the upperbody development phase to optimize the tophatcomponents.

� New tools developed as part of this study are currently used by Ford engineers to automate MDO tasks

� The process is highly scalable to include additional attributes, load cases or design variables

Conclusions

New Automated MultiˆDisciplinary Optimization (MDO) Process … · 2016. 2. 20. · Design1...

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