computational tribology

37
Andreas Almqvist, Luleå University of Technology Tribology Days Ö-vik 6-8 Nov 2012 [email protected]

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Page 1: Computational tribology

Andreas Almqvist, Luleå University of Technology

Tribology Days

Ö-vik

6-8 Nov 2012

[email protected]

Page 2: Computational tribology

• Why is modelling & simulation important?

• The Luleå mixed lubrication model

– Surface Topography

– Computing Flow factors

– Component simulation

• Current activities

• Future work

2

Page 3: Computational tribology

3

Understanding

Ranking Optimisation

Testing new ideas

𝛻 ⋅𝜌ℎ3

12𝜂𝛻𝑝

= 𝛻 ⋅1

2𝐔𝑠𝜌ℎ +

𝜕

𝜕𝑡𝜌ℎ

𝜂?

𝜂1, 𝜂2, 𝜂3…

Page 4: Computational tribology

Model

Component

Full-

scale

A complement to:

4

Page 5: Computational tribology

Few commercially available packages

AVL

Ricardo Suite

MIT Consortium

COMSOL Multiphysics

Lubrication Shell

Excite

Piston&Rings

5

Page 6: Computational tribology

Delveloped by: The division of

Award2010

– Flow between asperities

– Asperity deformation

– Utilizing the Flow Factors

– All regimes considered

– Coarse grid = fast

– Pressure

– Load

– Friction

– Leakage

Topography representation

Flow Factor computations Component Simulation

u+

1

u

u

u y1

y2

6 PhDs 5 Lics

60+ publications 40+ Tribology 20+ Mathematics

I II III

at LTU

2004-

Page 7: Computational tribology

Local scale; describing the Surface Topography

Global scale; describing Geometry - Incorporating the effects of roughness

Local

Global

7

through homogenization

Page 8: Computational tribology

8

1. Representative roughness – # of realizations / measurements

– Resampling, interpolation

– Filtering, mirroring

2. Flow factors – Contact mechanics

• Material model - Linear Elastic Perfectly Plastic

• # Loads (=> separations)

– Local problems

• Core of the homogenization

• # Separations

3. Component simulation – Interpolate ff’s onto global domain and

solve for homogenized pressure

p u

*

0minV

p

u+

1

u

u

u y

1

y2

Page 9: Computational tribology

AFM VSI • Artificial surfaces measured with AFM and VSI • Anomalies created from measurement technique observed • Effect on tribological parameters investigated…

Sa (μm) Sk (μm) Spk (μm) Svk (μm)

AFM 39.0 10.9 45.1 101.0

VSI 91.6 64.3 220.0 521.0

• Large differences in roughness parameters for artificial surfaces • Far smaller differences for ’real’ surfaces • However, care must be taken with ’extreme’ surfaces, i.e. laser textured

Paper by Andrew Spencer et al. - Trib. Int. Vol. 57

I

9

Page 10: Computational tribology

• Mirroring => Perfect periodicity

10

Page 11: Computational tribology

11

Page 12: Computational tribology

12 Matlab GUI by Dr. Fredrik Sahlin

• Resampling

Page 13: Computational tribology

• Revamped!

• Was – Matlab 2007 + COMSOL Multiphysics 3.5a

• Is – MATLAB code only!

• Mini-courses / tutorial sessions on the usage of the code will be given.

II

(http://www.python.org/ code under construction)

13

Page 14: Computational tribology

How to achieve a physically acceptable result?

– Representative contact mechanics model

– Clearance measure defining direct contact

– Full film computation over the non-contact area only

Rigid point

II

Contact mechanics BEM model

Local problems Homogenization

14 Contact spot

Topography rep.

In contact

Page 15: Computational tribology

Patir and Cheng Homogenization

Paper by Almqvist et al. – ASME Journal of Tribology, Vol. 133

---> Incorrect modelling of the so called micro-bearing problems

15

Page 16: Computational tribology

[mm]

[ m ]

[mm]

- Include inter-asperity coupling - Have a deterministic representation of the surface

p

u

16

We want to:

Page 17: Computational tribology

Average interfacial separation (Å)

Con

tact

pre

ssu

re (

-)

Almqvist and Persson et al. – Journal of the Mechanics and Physics of Solids, Vol 59. 17

Page 18: Computational tribology

• Cavitation

• Asperity collision

• Wear modelling

• Piston ring - Cylinder liner lubrication

VR

SSF

VR, SSF, Sauer-Danfoss

II

SSF, Scania

18

Page 19: Computational tribology

• Universal equation (Non-linear)

– Elrod – “Incompressible HL”

– Vijayaraghavan Compressible

• Linear Complementarity Problem

– Dini - Incompressible

– Almqvist & Wall - Compressible

𝐴 𝜃 𝜃 = 𝑏,

𝐴𝑝 + 𝐵 1 − 𝜃 = 𝑏,

𝑝 1 − 𝜃 = 0

𝑔 𝜃 = 1, 𝜃 ≥ 10, 𝜃 < 1

II

19

Page 20: Computational tribology

• Friction contribution to deformation included

• FFT accelerated solution procedure

Frictionless Friction included N N

F f=N

II

20 PhD Student - Joel Furustig

Page 21: Computational tribology

• After the first component scale simulation, new flow

factors can be computed and then incorporated to

compensate for possible plastic deformations

21

II

PhD Students - Joel Furustig & Andrew Spencer

Page 22: Computational tribology

Mesh convergence studies &

Optimisation of geometries

have been conducted to verify the accuracy of the model.

PhD Student - Shaojie Kang

3

4

2

4

2

1

80 μm

60μm

Y

X

3

4

2

4

2

1

80 μm

60μm

Y

X

Interference:

Thermoelasto-plastic, time dependent simulations in LS DYNA

22

II

Page 23: Computational tribology

Stress Plastic strain Temperature

PhD Student - Shaojie Kang

Interference Friction Velocity

23

II

Page 24: Computational tribology

• Stress grows from zero to the maximum value, then decreases to the residual stress after collision.

• The distorted stress profile is due to the existence of friction. • Maximum stress is observed near the surface and large plastic

deformation are observed.

A B

C D

24 PhD Student - Shaojie Kang

II

Page 25: Computational tribology

• Maximum plastic strain is found close to the surface • Wear model could be established -> plastic strain is larger

than a critical value means seizure.

A B

C D

25 PhD Student - Shaojie Kang

II

Page 26: Computational tribology

• Maximum temperature is found at the smaller(top) asperity • Max temp rise ≈40°

A B

C D

26 PhD Student - Shaojie Kang

II

Page 27: Computational tribology

III

PhD Student - Andrew Spencer 27

II

Page 28: Computational tribology

28 PhD Student - Andrew Spencer

III II

Page 29: Computational tribology

• Piston ring - Cylinder liner lubrication

• Power losses in hydraulic motors

• Lubrication of cam – roller systems

Energimyndigheten, Scania, Volvo

VR, Hägglunds Drives

Scania, Bosch, HD

III

• Cavitation

• Asperity collision

• Wear modelling

• Piston ring - Cylinder liner lubrication

VR

SSF

VR, SSF, Sauer-Danfoss

SSF, Scania

II

29

Page 30: Computational tribology

30

Page 31: Computational tribology

PhD Student - Markus Söderfjäll

Energy agency Volvo Powertrain AB

Scania

III

31

- Out of roundness - Ring twist & dynamics

- Floating liner test rig

- Effect of laser texture

Page 32: Computational tribology

More than 50% reduction of power losses

Bosch Rexroth (Hägglunds drives AB)

III

32 PhD Student - Patrik Isaksson

Page 33: Computational tribology

PhD Student - Mohammad Shirzadegan

Hägglunds drives AB (Bosch Rexroth), Sweden

Robert Bosch GmbH, Germany

Scania AB, Sweden

Aim

A fast and reliable tool for prediction of slippage in cam - roller contacts

III

33

Page 34: Computational tribology

III

34 PhD Student - Mohammad Shirzadegan

Conformal contact

Non-conformal contact

Are the scales separable? Reaction forces?

Global

Local

Page 35: Computational tribology

The dynamic behaviour can be predicted if the physics can represented by a mathematical model:

Cam

Roller

Spring

Follower

𝑥, 𝑥 , 𝑥

𝑘 𝑐

𝑓𝑐(𝑡) 𝑓𝑐(𝑡)

𝑓𝑠 𝑓𝑑

𝑚 𝑚

System Lumped model Free body

diagram

𝑭𝒙 = 𝒎𝒙

35

Page 36: Computational tribology

36

• Inter-asperity cavitation

• Micro-EHL

• Non-Newtonian Rheology

Page 37: Computational tribology

Questions?

Tribology Days

Ö-vik

6-8 Nov 2012

Andreas Almqvist, Luleå University of Technology [email protected]