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Progress on Engine LES Using STAR-CD

A D Gosman

CD-adapco

Japan STAR Conference 2012, Yokohama

1. Nature and motivation for LES of engines

2. LES modelling in STAR-CD

3. Collaborations

4. Validation studies

5. Applications

INTRODUCTION

MOTIVATION FOR ENGINE LES

ADVANTAGES OF LES OF ENGINES

• because only small scales modelled, is inherently more accurate.

• calculate individual cycles, so obtain information on cycle-to-cycle variations

➝ principal motivation

• small-scale modelling easier than RANS modelling in some, but not all areas.

COMPARISON LES AND RANS

• RANS models all scales of fluctuation

around mean

• LES models only small scales and

directly computes larger ones RAN

S

LE

S

ACTUAL

DRAWBACKS OF LES

• moderately more expensive/cycle, but need many cycles to get statistics

• more effort to set up, e.g. more detailed boundary conditions required

• puts more demands on numerical solver – both accuracy and speed.

CD-adapco ACTIVITIES IN ENGINE LES

UNIVERSITY COLLABORATION AND SPONSORSHIP

• Penn State Univ.: Professor D Haworth

• Cornel Univ: Prof S Pope

• Darmstadt Univ: Prof J Janicka

• Modena Univ: Prof S Fontanesi

• Imperial College London: Prof W P Jones

IN-HOUSE DEVELOPMENT OF LES CAPABILITIES

• several experts in LES modelling and numerics

• current focus on combustion

ENGINEERING SERVICES PROJECTS

COLLABORATIVE PROJECTS WITH INDUSTRIAL PARTNERS

ENGINE LES/DES MODELLING IN STAR-CD: OVERVIEW

FULL FRAMEWORK FOR MODELLING SI AND DIESEL ENGINES

• LES and DES modelling of flow and mixing

- optional LES subgrid turbulence models

- hybrid DES model combining URANS for near-wall and LES for bulk

• LES-adapted Lagrangian modelling of sprays (V4.20)

- special subgrid modelling of gas-droplet interactions

- full capabilities as in URANS version: atomisation, wall impingement, wall

film

• General ECFM3Z-LES modelling of SI and Diesel combustion and emissions

- LES version of ECFM-3Z model, with full capabilities

- applicable to all combustion modes: full/partially premixed, diffusion

- optional SI ignition models

- under test, scheduled for release in STAR-CD V4.20

LES SUBGRID MODELLING OPTIONS

Filtered LES equations, subgrid viscosity modelling

Options

1. Smagorinsky

 

Sij =1

2

¶ui

¶x j

+¶u j

¶x i

æ

è ç ç

ö

ø ÷ ÷

2. One-equation subgrid k model

• transport equation solved for ksgs

DES MODELLING – I INTRODUCTION

• Hybrid non-zonal model:

- tends to LES in resolved flow

- tends to URANS in unresolved

• Automatic selection of length scale

according to grid:turbulence length scale

ratio

• Preferable to limit URANS to near-wall

region

• Several variants based on different URANS models

- Spalart-Almaras

- k-ω SST

- k-ε

DES MODELLING II - EXAMPLE

SPALART-ALMARAS DES MODEL

• one-equation model: both high-Re and low-Re versions

• tends to Smagorinsky-type LES model when CDESΔ/d > 1

d = wall normal distance, Ψ ≈ 1 at high Re

n =n t

fn1

®n t at high Re

• dissipation rate depends on controlling turbulent length scale

 

˜ d

ECFM3Z-LES COMBUSTION MODELLING – I OUTLINE

Full ECFM-3Z adapted for LES

• coherent flame model for premixed burning

• eddy dissipation model for diffusion burning

• 3-zone modelling for sub-grid mixing

• equilibrium modelling for burnt gas

• Zeldovich NOx chemistry

• range of soot models

• range of knock models

Two optional spark ignition models

1. Eulerian AKTIM

2. AKTIM adapted for LES

ECFM3Z-LES COMBUSTION MODELLING – II LES FLAME SURFACE DENSITY EQUATION

Transport equation for flame surface density

• Tres = resolved transport

• Tsgs = subgrid transport

• P = laminar propagation

• Sres = resolved strain

• Ssgs = subgrid strain

• Cres = resolved curvature

• Csgs = subgrid curvature

KEY NUMERICAL ALGORITHM FEATURES

• Implicit PISO with quasi 2nd order time differencing

• Second-order centered or MARS spatial differencing

for momentum

• MARS for species and other scalars

• Synthetic turbulence inlet conditions

• Non-reflecting inlet/outlet conditions

QUALITY ASSESSMENT CRITERIA FOR LES

1. A-priori ratio integral scale/mesh size = lint /Δ

 

lint = Cm

0.75k3 / 2 /e• use RANS estimate

• want ratio < 0.5

• accuracy depends on RANS solution

2. Fraction resolved kinetic energy kres/ktot

•

• want ratio > 0.8

kres 1

2u'1

2u'22u'3

2 ; ui

' ˜ u i u i; ktot kres ksgs

3. Ratio LES predicted turbulence scale/mesh size

• obtain length scale from energy spectrum

4. Ratio turbulent: laminar viscosity

• ideally close to unity, minimizes modelling error contribution

5. Other, e.g. Index of Resolution Quality

VALIDATION: MOTORED MODEL ENGINE (PENN STATE U):

Model Engine Configuration Flow during Intake

Axisymmetric, central open valve, flat piston

LES simulations, without/with swirl

• Smagorinsky, wall functions

• 1.3M cell mesh, size ~ 1 mm

• effects of mesh (170K,2.6M), time step (0.1-1.0 CAD), subgrid model (1 eqn)

MODEL ENGINE: LES AND RANS COMPARED WITH MEASUREMENT

144°

36°

• LES better than RANS for same mesh and time step

MODEL ENGINE: RESOLVED AND SUBGRID KINETIC ENERGY

• Subgrid-scale energy generally is small relative to resolved-scale energy and

decreases with mesh refinement

170K cells

1.3M

cells

2.6M

cells

MODEL ENGINE: CYCLIC VARIATIONS AND POD ANALYSIS

Cyclic variations investigated using Proper Orthogonal Decomposition (POD)

Similar study underway for the Transparent Combustion Chamber (TCC)

engine

VALIDATION: MOTORED 4-VALVE PENT-ROOF ENGINE (UNIV DARMSTADT)

exhaust intake

exhaust

intake

ENGINE

• 4-valve, pent roof, flat piston

• optical access

• measurements with laser diagnostics

COMPUTATIONAL SET-UP

• Smagorinsky, MARS, Piso

• time-dependent inlet and outlet

pressure boundary conditions

• fixed-temperature inlet

• variable time step, linked to valve motion

• mesh sizes 2.1M (base), 3.2M(refined)

• corresponding mean cell sizes1.2, 0.8mm

• up to 25 successive cycles

phase averaged mean

rms

Intake (270bTDC) Compression (90bTDC)

4-VALVE PENT-ROOF ENGINE: EFFECT OF NUMBER OF CYCLES

Total cycles = 25; phase averages over increasing periods

y (

mm

)

x ( mm

)

y (

mm

)

x ( mm )

base

y (

mm

)

x ( mm )

refined

90°

bTDC

|V| [m/s]

measured

4-VALVE PENT-ROOF ENGINE: FLOW FIELD AT 270O BTDC (INDUCTION)

4-VALVE PENT-ROOF ENGINE: VALIDATION AT 270O BTDC (INDUCTION)

RMS u velocity

y = 0mm

y = -10mm

Phase averaged u velocity

90°

bTDC

|V| [m/s]

4-VALVE PENT-ROOF ENGINE: VELOCITY FIELD AT 90O BTDC (COMPRESSION)

y (

mm

)

x ( mm

)

y (

mm

)

x ( mm )

base

y (

mm

)

x ( mm )

refined

measured

4-VALVE PENT-ROOF ENGINE: VALIDATION AT 90O BTDC (COMPRESSION)

y = 0mm

y = -10mm

Phase averaged u velocity RMS u velocity

90°

bTDC

|V| [m/s]

4-VALVE PENT-ROOF ENGINE: RESOLVED KINETIC ENERGY FRACTION

base refined

4-VALVE PENT-ROOF ENGINE: CONCLUSIONS OF STUDY

1. LES is more accurate than RANS and gives good agreement with

experiment.

2. For reasonable accuracy require:

– Grid size not more than Δ ≈ 1.0 mm to capture 80% - 95% of the TKE (except jets)

– 25 to 50 engine cycles to get accurate statistical means and fluctuations.

3. Cyclic variations most likely to be caused by random nature of turbulence.

4. Accuracy of RANS is not bad, especially for mean velocity.

COMBUSTION IN 4-VALVE HIGH-SPEED GDI TURBOCHARGED ENGINE (U.MODENA)

ENGINE

• 4-valve GDI

• pent-roof, shaped piston

• turbocharged, 10000 rpm

• test-bed measurements

COMPUTATIONAL SET-UP

• STAR-CD development version

• time-varying inlet/outlet pressures

from tuned 1D analysis

• Smagorinsky, wall functions, ECFM-LES,

‘simple’ ignition model

• 1.3M mesh, refined around spark plug

• initialisation from RANS solution

• up to 25 cycles

• research in progress

4-VALVE HIGH-SPEED GDI: CYLINDER PRESSURE PREDICTIONS AND COMPARISON

4-VALVE HIGH-SPEED GDI: CYCLIC VARIATIONS IN FLAME PROPAGATION

27

Interaction between velocity field & flame development

4-VALVE HIGH-SPEED GDI: CYCLIC VARIATIONS IN FLOW AND EQ RATIO

28

Equivalence Ratio

4-VALVE HIGH-SPEED GDI: KNOCK ONSET

PREDICTION FOR HIGH PRESSURE CYCLE

Experimental Spark

Advance

Highly Increased Spark

Advance

Spark Advance - Moderate

Increase

Yellow Isosurface - RVB=0.5

Red Isosurface - Autoigniting

Condition

Experimental Spark

Advance

Spark Advance – High

Increase

Spark Advance - Moderate

Increase

Yellow Isosurface - RVB=0.5

Red Isosurface - Autoigniting

Condition

4-VALVE HIGH-SPEED GDI: KNOCK ONSET

PREDICTION FOR LOW PRESSURE CYCLE

SUMMARY

1. Adaptation of STAR-CD for LES and DES of engines is well advanced,

with involvement of internal and external experts and collaborations

with university and industrial partners.

2. Extensive validations have been performed for motored operation, with

satisfactory results.

3. Current development and validation activity for engine combustion is also

producing promising results.

4. The results confirm the clear benefits of LES in improved accuracy and

capability to predict cyclic variability.

5. The experience gained is also helping to identify best practices for LES/DES.

ECFM3Z-LES COMBUSTION MODELLING – III BASIC IGNITION MODEL

Initialise reactedness profile

• evolution of time mean surface area

• wrinkled surface area

• wrinkle factor evolution

• convert Sm into flame surface density???

•ECFM-3Z LES

Ignition (two options in STAR-CD). Basic Model [1,2]

AKTIM [4] with extensions for LES

Basic Model:

Initialise profile for burnt gases volume fraction c as:

with constraint with rk the given initial kernel radius from the RANS model

Model evolution in time of the mean flame surface area Sm(t) with:

•ECFM-3Z LES

Basic Model (continued):

The mean surface Sm will be wrinkled by the turbulence:

with Ξ the wrinkling factor given by the equation:

with S_l, a_t the laminar flame speed and the sgs strain.

Sm is converted into the Flame Surface Density FSD by:

Extensive parametric studies have been performed

Formulation LES or RAS

Computational

mesh 170K, 1.3M, or 2.6M cells

Pressure-correction

algorithm SIMPLE or PISO

LES subfilter-scale

turbulence model

Constant-coefficient Smagorinsky (CS=0.02-1.0) or 1-eqn.

subfilter-scale k model (Ck=0.05-1.5), with wall damping

RAS turbulence

model Standard high-Re k-e w/wall functions

Computational

timestep Dq= 0.1 – 1.0 CAD

Piston motion Fixed number of cells w/deformation or mesh

addition/deletion

Baseline:

LES, 1.3M cells, PISO, 1-eqn. w/Ck=0.3, Dq=0.1 CAD, fixed # of cells

In all cases:

Discard first 2 cycles (min.), average over 3 cycles (min.) + azimuthal

direction