Simu mulati lating ng Syst stems ems in Ground und Vehicl cle - - PowerPoint PPT Presentation

Simu mulati lating ng Syst stems ems in Ground und Vehicl cle e Design ign

Frederick J. Ross Director, Ground Transportation

Matur urit ity y of Simulat ation ion

– Growing from validation to virtual simulation

Simulat ating ng Syst stem ems

– Driving virtual prototype to look at early design behaviors

Agenda da: : Simu imulatin lating g Syst stems ems

Syst stem em Simulation mulation Maturi rity ty Model l

Validate (software) Troubleshoot Predict Automate Optimize Ultimate Goal: Find best design in shortest time

Increased ROI

Critical inversion point (from reactive to proactive engineering)

As simulat ation

• n matures

es, greater er ret eturn n of investm estment nt is seen. n.

– Each analysis goes through phase. – First, the process needs to be validated. – Once validated, engineers start taking advantage of the tool to troubleshoot existing designs. – Key turning point in simulation is where the use becomes more predictive

• Replace test by virtual simulation

– Next key turning point is automation

• Automation leads directly into
• ptimization. To build the best design

in the shortest period.

• Ease-of-Use to run design

modifications

Power ertr train ain Simulation mulation Roadma dmap

Component Analysis

• Port Flow
• Coolant Flow
• Intake/Exhaust Manifold flow

1

Transient Behavior

• Couple Simulation to 1D Code
• Look at EGR mixing
• Exhaust manifold temperatures

2

System Analysis

• Coolant Filling
• Crank Case Ventilation
• Oil Circuits
• Turbo Charger
• Aftertreatment

3

Environment Evaluation

• Dynamometer Testing
• Engine in Vehicle
• Drive Cycle Simulation

4 Increased ROI Complexity

Validate Troubleshoot Predict Automate Optimize

Component Analysis System Analysis

Autom

• mat

ation ion: : Inta take e Port t Flow Analysis lysis Vi Virtual tual Port t Flow Tool

Challenge

Combustion efficiency depends a lot on the intake air flow, tumble, and swirl to get complete, and fast burn. CFD has proven to be a valuable tool to

• ptimize port flow. Engineer needs quick design

studies to evaluate flow efficiency at different valve lifts.

Solution tion

• Automated tool has been built and designed.
• Port Flow optimization.
• Follows work from established best practices.
• Pass data to other software/databases without

manual interactions.

Impact act

• Reduce errors in simulation.
• Leverage product expertise without needing

software expertise.

• Leverage the expertise of analysis to the

experts.

Return to a focus on Design as opposed to Analysis!

STAR AR-CC CCM+ M+ envi vironme

• nment

nt promo motes es aut utomat mation ion

– Tools from CAD to Results

The e Simulat ation ion As Assist stant ant helps ps gui uide de us user for specif cific c applicat ations ions

– New for 2013 – User can define steps needed to define the workflow

Autom

• mat

ation ion: : The SCR R Simulation mulation Assistant istant

Coolant

• lant Jac

acket ket Sim imulat lation ion Assis sistan ant

Guidi iding ng the e user er thro rough ugh set up and d post proc

• ces

essin ing g

• f a Cylin

inder der Bloc

• ck / Head

ad Coola lant nt Jacket et.

4.38M Cell Polyhedral al Mesh Coolant t Inlet Gasket t Holes

Baseli eline e Design ign

Optimiz imizat ation: ion: Coolant lant Flow Challen llenge ge: :

– Minimize pressure drop across water jacket

• Modifying 24 gasket hole

– Subject to constraints:

• Specified peak head and liner temperatures
• Cylinder to cylinder variation in peak liner & dome temperatures <

10 °C

• Peak coolant temperature specified
• Peak velocity of coolant in head/block water jackets < 10 m/s

Optimat imate+ + Result ults: s:

– 1/3 less design evaluations compared to DOE – 10% reduction in pressure drop relative to DOE-

• ptimized design
• 7% reduction in max head temperature

– 16 feasible designs in highly constrained design space

8

Opt ptimiz imizatio ion n Proc

• cess

Opt ptimal imal Desig ign Opt ptimal imal Desig ign

Improvement t in Cooling ng Jacket t Temperatu ture Variatio ion Baselin ine Opti timized

9

Simulating mulating Syst stems: ems: Power ertr train ain

Challenge

During development process, test are design to look at engine for early design testing. But critical tests need to consider installation of the powertrain in the vehicle.

Solution tion

Use existing geometry of the engine in dynamometer and place engine in vehicle. Includes:

• Cooling Air Flow
• Air Induction System
• Coolant Flow Network
• Oil Flow

Impact act

• Reduce prototype of engine/vehicle

construction.

• Reduce time to find out thermal failures.
• Reduce cost
• Reduce time to production.
• Improve information on failure cause.

Syst stem em Simulations: mulations: Exhaust haust After ertr treatme eatment nt

Simulat ation ion Fea eature tures

• NOx reduction in the catalyst
• Lagrangian multiphase with pulsed spray injection
• Multi-component droplets (water/urea mixture)
• CHT (multi-phase fluid + solid pipe walls and mixers)
• Liquid film + droplet/film wall interaction
• Droplet/film evaporation + gas mixing (air, Urea gas,

NH3, H2O…)

• Chemical reactions (Thermolysis/Hydrolysis)
• Porous Media

DOC (Diesel el Oxidat ation ion Catalys lyst) t) Spray ay Injector tor SCR DPF (Discrete ete Partic icle e Filter er) Mixer ers Flow

• utlet

et Flow inlet et

SCR R Simulation mulation Roadma dmap

Uniformity Test

• Urea Injection
• Wall Modeling
• Urea/Gas Mixing

Optimization demo exists

1

NOx Prediction

• Surface Chemistry
• Detail Chemistry Using DARS

Clients have validate results

2

Crystallization Prediction

• Full Chemistry
• Solidification prediction

3 Increased ROI Complexity

Validate Troubleshoot Predict Automate Optimize

Vehic icle le Therm rmal l Managem gement nt Roadma dmap

1 2 3 4 5 6 7

Front End Air Flow

• Top Tank Temperature

Prediction

• Turn-Around: 1 Day

1

Local Component Temperature

• 30-60 Solids
• Local to a component

2

Total Vehicle Simulation

• Using existing sub-models

4

Underbody Temperature

• ~ 100 Solids
• Includes Exhaust System, hangers,

engine mounts, heat shields

3

Power Train Cooling

• Full Engine CHT model
• Induction System
• Exhaust System
• Oil Flow

5

Full Vehicle Thermal Management

• Conduction/Radiation using

Radtherm

• Includes Drive Cycle Simulation

6

Full Vehicle Thermal Management

• Co-Simulation from STAR-

CCM+ to STAR-CCM+

• 4000 Solid Components
• Includes Drive Cycle

Simulation via Ports

7

8

GUM: Grand Unified Model

• Complete vehicle simulation
• 4000+ Solid Components
• Cabin Thermal Comfort
• Vehicle Aerodynamics
• HVAC Simulation
• Electronics Cooling
• Co-Simulation STAR-CCM+ to STAR-CCM+

8 Complexity Increased ROI

Simulation mulation using ng the Digital ital Prototyp type

Durability (BiW) Crash NVH Ride/Handling

HVAC/ Thermal Comfort Durability Chassis

Aerodynamics Climate Control Heat Protection Manufacturing Powertrain Transmission

Digit ital Prot

• toty

type pe becomes comes enabler for advan ance ce simulat ation

• n

– Simulation for more advance analysis then just component design – Simulation includes multi-physics. – Simulation can involve motion as needed as well. Whatever best helps engineer design their product efficiently. – In the past, these would not have been possible until hardware of the vehicle has been produced.

Generati eration

• n of a Digit

ital al Prototyp type

Data Freeze define nes s digital l prototy

• type

pe

– As with a real prototype, design teams work together to meet a goal for the design freeze. – Review board checks, to make sure all components are fitted together and data pool is complete.

Data Filter: Filters data for simulation

– Data needed for simulation is filtered from the

• verall data pool, and provided for the virtual

simulation.

• Key component for data transfer

– Example of data filters:

• Red Cedars Heeds
• Custom tool designed to pull data together.

– OpenRoad

• CAD plugin can help provide data filter

– PLM (product lifecycle management) tools enable communication between different tools.

Analysis Response

– Feeds back into the data pool for design improvement.

Deflection speed v Damping force F Grade 1 Grade 2

Geometrical Data Functional Data

Aut utom

• mation

ation: : Front nt End nd Cooli ling/ ng/Aerodynamics

• dynamics

Challenge: Data Filtering

Large CAD database needs to be quickly moved from 1000’s of CAD parts to few boundaries needed for CFD.

Solution:

OpenRoad

• Provides part filtering with link to

boundary setup for the simulation.

• Forms template for the full

simulation process including dual stream heat exchangers. Impact:

• Enables users to quickly predict

drag and/or front end air flow.

• Enabler for more complex studies

such as component temperature prediction, soiling, aero-acoustics

• Runs fully in batch: good for
• ptimization with Heeds

1

Deflection speed v Damping force F Grade 1 Grade 2

Geometrical Data Functional Data

Optim imization: ization: Front nt End nd Cooling/

• ling/Aer

erodynamics dynamics

Challenge: Cooling Performance

Engineers have two competing design criteria's

• Need to provide cooling air for

engine.

• Decrease grill/bumper opening to

reduce drag

Solution:

Optimization study can be done looking at grill/bumper openings and fan size and determine best case where both criteria's can be satisfied.

• Involves looking at drag at high speed

while cooling performance is done with a uphill trailer tow study.

Impact:

• Using SHERPA improvements are

seen within 50 design iterations

1

Syst stem em Simulation: mulation: Brake e Cooling ling Modeling ling Brak ake Cooling

• ling
• Thermal temperature

prediction of brake disk.

• Brake drive cycle studies
• Brake cooling duct design
• Optimization: Minimize rotor

temperature while reducing drag.

• Failure protection
• Water splash/spray on

bearings

• Dust shield design

2

Syst stem em Simulation: mulation: “An Innovative Approach to Race Track Simulations for Vehicle Thermal Management”

Challenge:

Extreme drive cycle push strain on thermal environment of the engine.

Solution:

Simulation can help reduce time and costs compared to experimental testing. Allows testing during early concept phases where testing is not possible due to lack of hardware. Allowed simplified thermal components to be modeled quickly in Radtherm, coupled to a detail CFD simulation Impact:

Improve endurance on PowerTrain. Reduce thermal drive failures Reduce cost and time.

18

“Overall the methodology indicated that fast quasi-transient solutions can be achieved for a highly dynamic profile with our current computational resources” Kristian Haehndel, BMW Group

6

Syst stem em Simulation: mulation: Stead eady-Stat State e Full l VTM Simu mulation lation Airflo flow w + S Solids lids using g co-sim simulat ulation ion

• Air model is

~35 million cells.

• Solid Model is

~35 million cells.

• Over 4000 solid

components modeled in the simulation

7

Shell ll Vs Vs Solid lid Modelin ling

Accura uracy cy

– Solids are more accurate

• Air flow imping on edge
• Heat capacity of solid

– Number of parts considered is more critical

• How many parts can be modeled in

4 weeks Turn-around time?

Recomm commen enda dati tion

• n

– Use what provides fastest turn- around time – CD-adapco Goal:

• Using solid elements should provide

fastest modeling and modification time since the true part has thickness

• Working at automating part contact

with solid elements.

– Zero thickness can be a problem

Syst stem em Simulation: mulation: Thermal rmal Ana nalysis ysis & D & Design sign Improvemen ement t of an Inter ernal al Air-Cooled

• oled Electric

ectric Machine ne

Challenge: Use simulation to improve the thermal performance of an internal air- cooled induction machine Solution: Compute EM losses in SPEED and map as heat loads to STAR-CCM+

EM Loss/Heat Loads

Batt tter ery Modelli lling A Multi-Physics and Multi-Length Scales Solution

Characterizes cell electrochemical and physical description Cell performance validated against experimental data.

Skin temperature applied to cell, and thermal cooling prediction is carried out with STAR-CCM+. Battery design studio used to determine cell performance. Cell performance can then be supplied to surface of cell to determine packaging of battery back.

Case e Studies udies

– Oil Slosh: Gearbox, Hydraulic Reservoir – Gears: Planetary, Screw, Pinion ETC – Bearings – Clutch Plate – Torque Converter

Op Operating rating Condit nditions ions

– Flooding

• Leakage into transmission

– Thermal Fatigue Stress

• Operating Load Point
• Heat up or Cool Down
• Drive Cycle

Syst stem em Simulations: mulations: Tran ansm smission ission

Key Enablers:

• Overset Grids
• Robust VOF Simulation

Syst stem em Simulation: mulation: Headlam dlamps ps

Challenge:

Two challenges

• Condensation
• Thermal deformation

Solution:

Simulation of Condensation

• Investigating removal time for

condensation on/in headlamp

• Look at ventilation patterns in headlamp

Thermal Environment

• Investigating thermal stresses that may

cause deformation or melting

Impact:

Improves safety and customer satisfaction.

24

“STAR-CCM+ is capable of handling conjugate heat transfer phenomena between different bodies as well as radiation and solid stress. ” Andrea Menotti, Olsa S.P.A

Pas Passenge enger Therm rmal l Comf mfort

– Thermal comfort manikin

Trans nsien ient t heat at up up/coo

• ol down

wn mode des

– Highlights importance of fast radiation modeling

• Need Solar, diffused solar, and reflective radiation

– Experience with heat transfer through walls and heat capacity

Deice/Def ce/Defog Simula ulati tion

• n

– Important use of wall film models

Syst stem em Simulat lation: ion: Cabin n Comfort

Syst stem em Simulations: mulations: Manufac acturi turing

Drying Paint Dip/E-Coat Spray Paint Casting

System m Simula ulati tion

• n is imp

mpacti cting ng design ign

– Reduce turn-around time in design – Reduce costs from reducing number of prototypes

Simulat ation ion is expanding anding

– Users are looking at replacing more expensive tests with simulation – As capability grows and mature in simulation tools, so does the demand on extending the features.

Design ign Ex Explora

• rati

tion

• n Growi

wing ng

– With increased automation provided by the STAR-CCM+ suite, optimization expanding to provide engineers with best design, in the shortest design cycle.

Summa mmary

Thank k You!