AUTOMOTIVE ENGINE CFD - PAST, PRESENT &

THE FUTURE

ERC - 2017 SymposiumJune 14 th & 15 th, 2017

Tang-Wei KuoGeneral Motors Global Research and Development

ABSTRACT

Role of computational tools in propulsion system

development has increased dramatically over the past

several decades and continues to evolve. At General

Motors, use of advanced predictive tools is at the core

of its corporate Road to Lab to Math strategy. This

talk provides an overview of the state of automotive

engine CFD from an OEM perspective with supporting

examples. Finally, the desired roadmap for improved

accuracy and increased use of these tools is presented.

OUTLINE

• How far has engine CFD evolved over the years?

• Examples of GM’s application of Engine CFD

Institutionalize standard work procedures

Improve predictive capability

• Desired future roadmap

INITIAL WAVE OF ENGINE CFD (PRIOR TO 1990)

Sample Sources of CFD Codes & Validation Data:

• Fluid Dynamics Group T-3 at the Los Alamos

National Laboratory (USA)

• Prof. D. B. Spalding at Imperial College (London)

• Prof. F. V. Bracco at Princeton University (USA)

• CRF at the Sandia National Lab (USA)

CFD Codes:

• CONCHAS, CONCHAS-Spray, KIVA

(LANL)

• PHOENICS (CHAM Ltd.)

• FIDAP (Fluid Dynamics International)

• Fluent (Creare Inc.)

• Star-CD (CD-ADAPCO)

National Labs

• Base Architecture

• Code Integration

• Validation Data

Universities

• Training of New User Base

• Birth of commercial CFD companies

Common

Sharing

Source - The Three Waves of Commercial CFD - Mentor Graphics

CFD CAPABILITIES DURING THE INITIAL WAVE

• Limited Computer Hardware –limited model sizes and

resolution

• Structured Finite Volume Methods – difficult to handle

complex geometry and optimize for HPC

• Numerical Methods not available to handle moving

boundaries like valves

• Emphasis is on physical models development and application

Industry remained speculative on usage of CFD toolset

SECOND WAVE OF ENGINE CFD (1990-2000)

National Labs

• Base Architecture

• Code Integration

• Validation Data

Industry

• Research & Development

• Critical Feedback

• Proprietary Sub-models & Best Practices

Universities

• Sub-model Development

• Code Proliferation

• Training of New User Base

Common

Platform

Commercial

CFD

Vendors

Working relationship

1. Centered around a common code

platform

2. Each entity had particular strengths

3. Strong user community and open

communication

Driven By …..

1. Growth in Computer Hardware –

realistic model sizes became

possible

2. Advance in Numerical Methods –

handled complex geometry with

moving interfaces and optimized for

HPC

3. Reliable and Robust Physical

Models became Commercially

Available

4. Growth of Commercial Codes - User

support, code maintenance & usage

(GUI)

THIRD WAVE OF ENGINE CFD (SINCE 2000)

Industry

Universities

National Labs

COTS1

COTS2

OpenSrc1

UDF1

COTS3

UDF2

COTS4

UDF3

OpenSrc2

COTS5

Current Landscape

1. Institutions centered

around a growing

‘web’ of commercial

(COTS) and open

source options

2. Implementing UDFs

into commercial

codes with limited/no

validation

3. Growth of open

source codes in

industry hampered

by liability and user

support UDF4

OUTLINE

• How far has engine CFD evolved over the years?

• Examples of GM’s application of Engine CFD

Institutionalize standard work procedures

Improve predictive capability

• Desired future roadmap

GMs ROAD TO LAB TO MATH (RLM) STRATEGY

Vision

RLM is the fundamental strategy leading to higher quality design, reduced structural cost, reduced reliance on physical test and improved product development time

Strategic Elements

• First Time Capable Designs

Explore a range of what-if scenarios

• Move Processes that Require Vehicles on the Road to Lab or Math

Minimize testing

• Establish Procedures during Production

Fine-tune for optimal system performance

Lab

Math

Road

SPRAY PATTERN OPTIMIZATION

Spray Pattern Definition:Target at 30 mm down stream

Automatically generated by optimizer

Goal:

• Find optimum injector for a

given combustion system to

achieve– Better few economy

– Lower emission

Baseline Optimized

EXPERIMENTAL CONFIRMATION

Measured Particulate Count

during FTP Test

Measured Combustion efficiency

at 2000 RPM 6 bar

Virtual Diesel Engine Design and Calibration Develop base combustion system & key boundary conditions - Piston bowl & Injector, boost target

1D GT-

Power

3D CFD

Response surface

AFR Fueling SOI SprayAngleTipProt HoleDia

Design of Experiments

TrbEff

Virtual Diesel Engine Design and Calibration Develop base combustion system & key boundary conditions - Piston bowl & Injector, boost target

Source - The first of a new generation of diesel engines from General Motors – the efficient and powerful 1.6

liter Euro6 Midsize Diesel Engine, 34th Vienna Motor Symposium, 2013

Overall Integration of Activity

Nodes employed by genetic Algorithm Evolution of Merit Function

OUTLINE

• How far has engine CFD evolved over the years?

• Examples of GM’s application of Engine CFD

Institutionalize standard work procedures

Improve predictive capability

• Desired future roadmap

CFD MODELS STILL REQUIRE FURTHER IMPROVEMENT

1. Large range in spatial scales drives need for accurate sub-models

PFIDiesel, SIDI drops

Human hair10000

Engine

BoreMesh

Size

2. Must account for millions of spray droplets15 mg gasoline:

# of Drops Radius A/V

1: 1.68mm 2

11,300,000: 7.5mm 400

3. Extremely challenging in-cylinder environment (for both models and measurements)

Turbulent Flow Ignition, Combustion & EmissionsDense Spray Effects

A COMMON ENGINE PLATFORM FOR MOTORED AND

FIRED ENGINE LES DEVELOPMENT AND VALIDATION

Bore 92 mm

Stroke 86 mm

Geometric compression ratio 10:1

Crankshaft rotational speed Varied

Intake pressure Varied

Exhaust pressure 101.5 kPa

Transparent Combustion Chamber

(TCC) Optical Engine

Working Group Members

GM Global R&D

U Michigan

UW-Madison

Penn State U

Source – 2010, 2012, 2014 and 2016 LES4ICE Conference, IFPen, Paris, France

TCC engine is simulated by an increasing number

of research groups

Data access managed by GM R&D and UM

Multiplying effect: research output for GM &

visibility

A COMMON ENGINE PLATFORM FOR MOTORED AND

FIRED ENGINE LES DEVELOPMENT AND VALIDATION

Maps.google.com

GM sponsored

Not GM sponsored

IN-CYLINDER FLOWS USING LARGE EDDY SIMULATION (LES ) -

PROPOSED STRATEGY FOR FASTER LES RESULTS

• Source - “Parallel Methodology to Capture Cyclic Variability in Motored Engines,” International Journal of

Engine Research, Vol. 18, No. 4, April, 2017.

Each parallel simulation starts from the

top dead center of compression. At this

crank angle, both valves are closed

and the turbulence is almost

homogenous.

The mapped initial condition is

obtained from the velocity field at the

end of cycle 2.

The initial conditions for each of the

parallel simulations is obtained by

adding synthetic turbulent flow fields to

the mapped initial condition

The length scales and velocity scales

of this synthetic field is determined

based on the PIV dataset at TDC

PARALLEL VS CONSECUTIVE LES

Parallel LES Consecutive LES

19

MEAN

RMS

NEAR-TIP INTERNAL-TO-EXTERNAL NOZZLE FLOW COUPLING

6.6

mm

1.0

mm

Velocity profileNear Field Imaging

Internal Flow simulations

Spray Plumes

Phase 1

OLCF DD

Project

Phase 2

ALCC

Project

6-hole Injector

External Spray Simulations

3mm

TOTAL CELL COUNT: 7,564,828

0.375 mm

70

mm

Injector flow coupling

CONVERGE™ External Spray Simulations

Customized UDF to read-in nozzle internal

flow velocity profile from HRMFoam

simulations

Fixed mesh embedding to resolve the

propagation of the spray

Semi-empirical injection routine

Droplets injected post primary

atomization

Collision & Coalescence model turned

OFF

HRMFoam

104 mm

Source – ALCC CMB-107

EXTERNAL SPRAY SIMULATION RESULTS

Spray collapse driven by increase in plume-to-plume

interaction

Pa=40kPa Tfuel=85C

Pa=40kPa Tfuel=25COn-axis View

0.5 msSource - DOE Annual Merit Review (ACE017)

STEADY STATE CALIBRATION FOR DIESEL ENGINES USING

GENERAL PURPOSE GPU ENABLED CFD TOOLS

GT-Power

CFD

Virtual Engine Model 1-D GT-Power model to predict multi-cylinder

engine performance

3-D CFD to predict heat release and emissions• Reliable prediction of in-cylinder spray, mixture

formation and combustion

Engine testing & Calibration

using Statistical Methods Methodology and constraints similar

to real engine hardware

Virtual Map for Vehicle SimulationSource – ALCC CMB-119

Controlling Parameters

Engin

e P

erf

orm

ance

STEADY STATE CALIBRATION FOR DIESEL ENGINES USING

GENERAL PURPOSE GPU ENABLED CFD TOOLS

• CFD model predictions trend similarly to experimental validation measurements− Excellent in combustion metrics

− Reasonably good for NOx and Soot

− CO and unburn HC predictions are much worse− Highly detailed kinetic mechanism (>1,000 species) + multi-component surrogate to improve

prediction accuracy (investigation in progress)

Excellent

Good

Poor

OUTLINE

• How far has engine CFD evolved over the years?

• Examples of current applications

• Desired future roadmap

WHAT DOES THE FUTURE HOLD?

Fundamental Numerics

• Higher order robust schemes

• Exascale computing

• Fast meshing

• ALE parallelization

• Mesh independence

• Assessment of GPUs

Advanced Sub-models

Product

Development

• Flows (LES, Cyclic variability)

• Sprays (Nozzle flows, atomization, etc.)

• Chemistry (emission, TCI, kinetics, etc.)

Pre

-Co

mp

etitiv

eP

rop

rieta

ry

• Workflows

• In-house validation

• User Interface

Software vendor

implementation or UDF

• Publication

• Source Code

Source - The Role of VTO on Development of Advanced CFD Codes in support of DOE Advanced Combustion

Engine Efficiency Goals, DOE VTO Workshop, August 18, 2014

Desired Roadmap

1-2 day

turnaround

(future roadmap)

• Multi-cycle LES, cyclic variability

• Resolution of dense spray region

• Large kinetic mechanisms

• Fast flow & chemistry solvers

• Mesh independence

• Optimization

Desired pathway for enhanced solver

scalability to enable high-fidelity simulations

with acceptable turnaround time

ENHANCED SCALABILITY TOWARDS PREDICTIVE

SIMULATIONSS

ub

-mo

de

l d

eve

lop

me

nt

1cpu ~10’s cpus ~100’s cpus LCF

• Single Cycle RANS

• Empirical spray and

combustion

• Reduced chemistry

• Extensive trial and error

Restricted to large number of

simultaneous simulations on

LCF Platforms

Present status

Rules of Engagement

Pre-competitive

National Labs Universities

Software Vendors

Industrial R&D

Product Teams(Engine Design)

Industry R&D groups will use the tools to generate design guidelines and calculation metrics for design

optimization by Product Teams

Limited interactions• User training• User defined functions• Software support incl.

—GUI—Debugging—Post processing—Application support

Limited interactions between Universities and software vendors preferred• Help implement

physical submodels

Source - Leveraging National Laboratory Computing Resources to Improve IC Engine Simulation, ANL VERIFI

Workshop, June 22-23, 2016

THANK YOU FOR YOUR ATTENTION

Global Research & Development

Tang-wei.kuo@gm.com