Computational Fluid Dynamics

(1)

Aerodynamics

Computational Fluid Dynamics

Industrial Use of High Fidelity Numerical Simulation of Flow about Aircraft

g

y

Presented by

(2)

• Aerodynamic Vision – where do we go?

Aerodynamic Vision where do we go?

• Numerical Simulation – what is it?

• CFD – typical pictures and examples

CFD typical pictures and examples

• meshing, modelling, quality of results

• CFD – what is it used for?

• Some challenges

• Route to the future

(3)

Aerodynamics is a Major Contributor to …

Overall Community & Company Vision

The European aviation community leads the world

in sustainable aviation products and services,

**meeting the needs of global citizens and society *)**

**meeting the needs of global citizens and society.*)**

Major 2050 Goals

**)

••

CO

₂

reduced by 75%,

reduced by 75%, NO

y

,

NO

_x

by 90% and perceived noise by

y

p

y

65%, relative to typical new aircraft in 2000

••

Certification cost reduced by 50% through leading new

standards

••

Leading edge design manufacture & integration

••

Leading edge design, manufacture & integration

maintained

••

Jointly defined European research and innovation

strategies, from basic research to demonstrators

••

Strategic European aeronautic test, simulation and

development facilities identified, maintained and

continuously developed

(4)

Aerodynamic Simulation

Experiment

Method

Theoretical / Numerical

Wind tunnel

Flying aircraft

Simulation

machine

Computer

Ph

i

l /

th

ti

l

„Real“ image

Model

Physical / mathematical

_{models of nature}

4 Both types of simulation do have advantages, drawbacks and limits

4 Both techniques complement each other

4 Both methods are further being needed and developed

(5)

Computational Fluid Dynamics – the Principle

z

Geometry,

flight conditions

CAD

CATIA V5

z

Physical model

of flow

conservation laws of nature – 5+2 equations

z

Discretization

10-50 Mio. mesh points

Mesh Generator

ICEM/Centaur/SOLAR

z

Numerical

Solution

0.915476

0.934722

0.995642

0.936521

0.968456

0.973853

50-350 Mio. numbers

Flow Solver

elsA/TAU

z

Evaluation

_Lift:

_0.5

Drag:

0.025 Postprocessing

FFDx/ENSIGHT etc.

Moment: -0.08

z

Flow,

Aero Data

(6)

CFD Models

Refinement of Physical Models

Full Aircraft

Configurations

Flow

Properties

Reduced

Configurational

C

l

it

Complex

Configurations

Wake Vortex

Configurations

Properties

10

8

10

6 Complexity

Viscous Flow

Turbulent Flow

Reduced

Configurational

Complexity

Simple

Vortex Models

InviscidFlow

Complex

Configurations

Vortex Flow

Wake Vortex

Separation

10

4 A319

A330/340

A340-500/600

10

2 A320

A380

Vortex Models

Subsonic Flow

A350

Problem

Size

Go ahead

Configuration

A310

A321

1965

1975

1985

1995

10

2 A318

A320

A300

2005

A380

Size

g

CFD-Model

Navier-Stokes

Equations

Euler

Equations

Potential

Equation

(7)

Expected Improvement Lines for CFD/WTT

CFD

WT

M€

Cost

CFD

WT

Cost

New model

technologies/

High Re testing/

improved strategies

improved WT

technologies/

rapid

prototyping

improved CFD

improvement of

algorithms & hardware

intelligent

meshes

improved CFD

(8)

CFD

(9)

(10)

CFD Status: Flow physical modelling

• Significant improvement in flow modelling

• RSM turbulence model clearly favourable to classical 1- or 2-eqn models

y

q

• Much better prediction of shock-induced separation

(11)

(12)

CFD Purpose: Predict Unsteady Aerodynamic Effects

• Unsteady simulation on installed rotor configuration

• Validation investigation on A400M and IPEKA test models

• Chimera mesh technique with combined structured & unstructured meshes

(13)

CFD Purpose: Support engine integration

Stream Lines and x-velocity

(CT= -0.2, decelerated flow)

Stream Lines and x-velocity

(CT= -0.6, reverse flow)

(14)

CFD Purpose: Support WT test set-up and analysis

• Consistent CFD

application helps to

d

t

d WT

understand WT

results and create

confidence

• Lessons learnt:

Lessons learnt:

best match with WT

only via complete

simulation of

experiment (support,

flexibility, walls, ...)

(15)

CFD Purpose: Predict Aerodynamics on “true” shapes

• High Fidelity CFD/CSM applied to aileron/spoiler case

• Validation of CFD/CSM model on ETW wind tunnel data

(16)

CFD Purpose: Aircraft unsteady aero loads

• Gust encounter simulation for flexible trimmed aircraft

• Full MD simulation of standard gust affecting aircraft, incl. flexibility and trim

• Multiple iterative coupling with automated process control

Trim-iteration convergence

(17)

The Future

?

Game Changer

Laminarity

?

Game Changer

Laminarity

?

Future Aircraft

for

m

a

n

c

e

Laminarity

Flow Control

Multi

-

disciplinary

?

Future Aircraft

for

m

a

n

c

e

Laminarity

Flow Control

Multi

-

disciplinary

A380

A330

logy

/

P

e

rf

Fully 3D optimized

integrated wing design

p

y

p

y

Optimisation

A380

A330

logy

/

P

e

rf

Fully 3D optimized

integrated wing design

p

y

p

y

Optimisation

A320

A330

lut

io

n

c

s T

e

c

h

n

o

l

Advanced Aerodynamics in Wing Design for

All-new advanced technology wing

A320

A330

lut

io

n

c

s T

e

c

h

n

o

l

Advanced Aerodynamics in Wing Design for

All-new advanced technology wing

A300

Ev

ol

ut

ro

d

y

n

a

m

ic

Supercritical airfoil for superior economical performance

Advanced Aerodynamics in Wing Design for

excellent economics

A300

Ev

ol

ut

ro

d

y

n

a

m

ic

Supercritical airfoil for superior economical performance

Advanced Aerodynamics in Wing Design for

excellent economics

Ae

Supercritical airfoil for superior economical performance

1st airliner to use winglets for better cruise performance

Ae

Supercritical airfoil for superior economical performance

1st airliner to use winglets for better cruise performance

(18)

Future Expectations on Numerical Simulation

• By the power of the future simulation capability, multidisciplinary

simulation and optimisation will be at hand of every Flight Physics

engineer

• Quality of the simulation will be appropriate for product development

• Turn-around of simulation will be such that the engineer will not be

Turn around of simulation will be such that the engineer will not be

faced with major interruptions of his work process

• There is a strong tendency and need towards multi-disciplinary

simulation and optimisation across Flight Physics

• MD interaction will be fully implemented in FP simulation capability

• Numerical optimisation will provide baseline design as well as

improvement steps, it will widely assist the design engineer

• Numerical simulation will be the major source of all FP aircraft

data

• Comprehensiveness as well as quality of data is accepted by

related customers/authorities

• Physical experiments will only back up/validate numerical data

• Physical experiments will only back-up/validate numerical data

(19)

Flight Envelope Flow Physics Challenges

• Beyond buffet boundary

flow tends to become

more and more separated

• Maximum lift borders are

determined by onset of

massive separation

i

t t l lift l

3g

Buffet boundary

p

from the aircraft surface

• Unsteady effects start to

become dominant

• High lift configuration

flow is physically very

complex due to high

• Transonic flow

ff

t ( h

k )

causing total lift loss

2g

F

ACT

OR

2g

F

ACT

OR

p

g

loading

effects (shocks)

get stronger with

increasing Mach

number and load

• Interaction of

• Region of highest

d

1g

LOAD

F

1g

VD

LOAD

F

VF

physical effects

(shock flow,

boundary layer

flow) start to

dominate

accuracy and

robustness

• Region of most

experience

0g

VC

EAS

(at a given altitude) • The colour gradient is intended to show

level of confidence in CFD flow solutions

dominate

- 1g

• Low g flow tends to

separate on the lower

side of the aircraft

• Local low Mach number /

low compressibility flow

only weakly coupled to

(20)

HPC is a key to future

Capacity:

# of Overnight

Loads cases run

Capacity:

# of Overnight

Loads cases run

Available

Computational

Capacity [Flop/s]

Available

Computational

Capacity [Flop/s]

LES

Unstead

1 Zeta (10

21

)

10

2 Unsteady

RANS

x10

6 1 Peta (10

15

)

1 Exa (10

18

)

10

3

10

4 RANS Low

Speed

RANS High

1 Tera (10

12

)

10

5 RANS High

Speed

“

Smart” use of HPC power:

• Algorithms

D t

i i

1 Giga (10

9

)

10

6 • Data mining

• Knowledge

1980

1990

2000

2010

2020

2030

CFD based

CFD-based

LOADS

Aero

Optimisation

& CFD CSM

Full MDO

Real time

CFD based

in flight

CFD based

CFD-based

noise

HS

Design

Data

Set

& HQ

& CFD-CSM

simulation

in flight

simulation

Design

Capability

achieved during one night batch

(21)

CFD: Request for “real-time”

The factor

d d i

needed is

10

7 The factor

needed is

10

6

10

6 Vorticity

(22)

Numerical Simulation Capability – 5 Corner Stones

• High fidelity aerodynamic simulation

g

y

• High Fidelity Flow Simulation CFD capability

• Full parametric product definition

• Parametric aircraft - shapes and aero data model

• Multi-disciplinary product optimisation

• Integrated product simulation and optimisation

• Highly efficient numerical simulation & optimisation

• Platform backbone software system

• High performance computing

g p

p

g

• Latest processor and hardware architecture

(23)

Th

k

Th

k

!!

Thank

(24)

© AIRBUS Operations GmbH All rights reserved Confidential and proprietary document This document and all information contained herein is the sole property of AIRBUS Operations GmbH No intellectual property © AIRBUS Operations GmbH. All rights reserved. Confidential and proprietary document. This document and all information contained herein is the sole property of AIRBUS Operations GmbH. No intellectual property

rights are granted by the delivery of this document or the disclosure of its content. This document shall not be reproduced or disclosed to a third party without the express written consent of AIRBUS Operations GmbH. This document and its content shall not be used for any purpose other than that for which it is supplied. The statements made herein do not constitute an offer. They are based on the mentioned assumptions and are expressed in good faith. Where the supporting grounds for these statements are not shown, AIRBUS Operations GmbH will be pleased to explain the basis thereof.

AIRBUS, its logo, A300, A310, A318, A319, A320, A321, A330, A340, A350, A380, A400M are registered trademarks.

Computational Fluid Dynamics

Aerodynamics

Computational Fluid Dynamics

Industrial Use of High Fidelity Numerical Simulation of Flow about Aircraft

g

y

Presented by

Contents

•

Aerodynamic Vision – where do we go?

Aerodynamic Vision where do we go?

•

Numerical Simulation – what is it?

•

CFD – typical pictures and examples

CFD typical pictures and examples

•

meshing, modelling, quality of results

•

CFD – what is it used for?

•

Some challenges

•

Route to the future

Aerodynamics is a Major Contributor to …

Overall Community & Company Vision

Overall Community & Company Vision

The European aviation community leads the world

in sustainable aviation products and services,

meeting the needs of global citizens and society *)

meeting the needs of global citizens and society.*)

Major 2050 Goals

Major 2050 Goals

**)

**)

••

CO

CO

2

2

2

2

reduced by 75%,

reduced by 75%, NO

y

y

,

,

NO

x

x

x

x

by 90% and perceived noise by

by 90% and perceived noise by

y

y

p

p

y

y

65%, relative to typical new aircraft in 2000

65%, relative to typical new aircraft in 2000

••

Certification cost reduced by 50% through leading new

Certification cost reduced by 50% through leading new

standards

standards

••

Leading edge design manufacture & integration

Leading edge design manufacture & integration

••

Leading edge design, manufacture & integration

Leading edge design, manufacture & integration

maintained

maintained

••

Jointly defined European research and innovation

Jointly defined European research and innovation

strategies, from basic research to demonstrators

**meeting the needs of global citizens and society *)**

**meeting the needs of global citizens and society.*)**

₂

₂

₂

₂

_x

_x

_x

_x

_{models of nature}

_Lift:

_0.5