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Chair of Numerical Thermo-Fluid Dynamics | TU Bergakademie Freiberg | 09599 Freiberg
S. Buhl | +49 3731 394214 | [email protected] | www.ntfd.tu-freiberg.de
Separation of Large-Scale Structures and
Turbulent Fluctuations in IC Engines using
POD-Based Conditional Averaging
Stefan Buhl, Frank Hartmann, Christian Hasse
LES for Internal Combustion Engine Flows [LES4ICE]
IFPEN / Rueil-Malmaison - 4-5 December 2014
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Motivation
Workflow for scale separation (based on a simplified engine setup)
Test on real engine setup
Summary
Overview
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Cycle-to-cycle flow variations can be divided into large-scale (coherent)
and small-scale fluctuations
Large-scale fluctuations e.g. precessing vortex core (PVC)
Small-scale fluctuations usually considered as “turbulence”
Development of a method to separate large- and small-scale
fluctuations for these strongly inhomogeneous flow fields, which can be
used for post-processing of:
Experimental data
Numerical data
Method to improve comparability between
URANS
LES
Experiment
Motivation
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• Vortex shedding as simple test case
• Classical RANS approach fails
• Averaging depends on time or frequency
Clear separation of average flow field and turbulence in this simple test case
u = const.
Cylinder All turbulence???
Ensemble Averaged
Motivation
u = const.
Cylinder
Instantaneous
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Simplified engine setup similar to experiments of
Morse et. al. [1]
Engine speed: 200 rpm
Bore: 75 mm
Stroke: 60 mm (sinus-shaped)
Compression Ratio: 3
Non-moving valve (4 mm gap) Simulation of:
Intake stroke
Exhaust stroke
Ambient pressure & temperature
Simplified Engine Setup
[1]: Morse, Whitelaw and Yianneskis; Turbulent Flow Measurements by Laser-Doppler Anemometry in Motored Piston-Cylinder
Assemblies; 1979; J. Fluids Eng.
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Numerical model:
ANSYS CFX (Release 15.0)
Node centered code
Finite volume approach
2nd order discretisation (space and time)
Incompressible and isothermal fluid
SAS-SST turbulence model
Resolved turbulence at inlet
Time step width of 0.025° CA (2.08e-5 s)
Hexahedral mesh
8.3 million grid points
Mesh refinement at walls
About 24000 CPUh each cycle
Simplified Engine Setup
Isosurface: Q-Criterion
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Simplified Engine SetupA
naly
zed
are
a
More than an integral length scale between
each sample plane (average)
26 sample planes
8 considered cycles (1 + 8 consecutive cycles)
Total number of 208 samples (each crank
angle)
>Lt
26 samples
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Results (at 90°CA)
Instantaneous Average (left) & RMS (right)
-10 mm
-30 mm
z
r
Instantaneous (grey), averaged (black) and rms (bars) velocities
-10 mm -30 mm
Isosurface: p = -1855 Pa
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Workflow for Scale Separation
Proper orthogonal
decompostion (POD)
Flow field classification
Averaging
Calculate small- and
large-scale fluctuations
Instantaneous flow fields
(snapshots)
POD coefficients
Subsets
Subset averages
CCV &
turbulenceInput Output
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Workflow for Scale Separation
Proper orthogonal
decompostion (POD)
Flow field classification
Averaging
Calculate small- and
large-scale fluctuations
POD for discrete data set (SVD):
𝐴 = 𝑈 Σ 𝑉𝑇
A data set to investigateRow: single variable over considered samples
Col.: considered variables at a single time step
U proper orthogonal modes
Σ singular values
V sample-dependent coefficients
≈
samplessp
ace
sp
ace
modes coefficients
singular
values
Source (image):
www.ece.umn.edu/~niha../mihailo/softwar/dmdsp/index.html
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Workflow for Scale Separation
Mode 1 Mode 2 Mode 3
POD at 50 deg crank angle
≈
samplessp
ace
sp
ace
modes coefficients
singular
values
Source (image):
www.ece.umn.edu/~niha../mihailo/softwar/dmdsp/index.html
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Workflow for Scale Separation
Proper orthogonal
decompostion (POD)
Flow field classification
Averaging
Calculate small- and
large-scale fluctuations
Classification based on POD coefficients
Straightforward approach for this case
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Workflow for Scale Separation
Sub 4 Sub 1Sub 2 Sub 3
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Workflow for Scale Separation
Proper orthogonal
decompostion (POD)
Flow field classification
Averaging
Calculate small- and
large-scale fluctuations
Ensemble average:
Conditional average:
Φ Conditioning vector (becomes a
variable s(n) in the following)
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Averages at 50° CA
4 Subsets for this case
Sub 1: 36
Sub 2: 65
Sub 3: 64
Sub 4: 43
Differences between
subsets in terms of
Vortex core position
Jet position and
penetration depth
Workflow for Scale Separation
Sub 1 ave Sub 2 ave
Sub 3 ave Sub 4 ave
Global ave
Ensemble average
Subset 1 average
Subset 2 average
Subset 3 average
Subset 4 average
50° CA
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RMS from global average:
RMS from subset average:
Quantification of large-scale structures:
Workflow for Scale Separation
Proper orthogonal
decompostion (POD)
Flow field classification
Averaging
Calculate small- and
large-scale fluctuations
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Workflow for Scale Separation
Sub 1 rms Sub 2 rms
Sub 3 rms Sub 4 rms
Global rms
CCV rms
Ensemble average
Subset 1 average
Subset 2 average
Subset 3 average
Subset 4 average
Fluctuations at 50° CA
Large bulk of fluctuations
in case of „Global rms“
Jet structure can be
clearly identified in
„Sub 1,2,3,4 rms“
Increased level of
fluctuations due to jet
breakup in subset
averages
CCV rms clearly
identifies jet flapping
50° CA
Small-scaleLarge-scale
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Method tested on real engine setup(transparent combustion chamber engine – University of Michigan)
Engine speed: 800 rpm
Bore: 92 mm
Stroke: 86 mm
Compression Ratio: 8
Motored operation condition
ANSYS CFX
SAS-SST turbulence model
Max. 2.5 million grid points
120 consecutive cycles (3 threads) (300 000 CPUh overall very moderate)
Important and demanding test case due to missingtumble motion
Real Engine Setup
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Real Engine Setup
Considering 90° CA
3d POD based on all 120 flow fields
Flow field classification identical to
Morse engine 4 Subsets
Investigation of velocity along line for
each subset
Classification of velocity fields based
on most energetic structure (intake
jet)
Subset averages
Instantaneous velocities (Sub4)
Works also for complex 3d engine
flow fields
Details will be published
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Developement of new method to separate large- and small-scale
fluctuations in IC engines
Succesfully tested on:
Simplified engine setup (208 ensembles)
Real engine setup (120 cycles)
Further tests and development focusing on:
Conditioning variable
Large-scale fluctuations
Comparison of URANS, LES, Exp. in terms of large-scale structures
Summary
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Acknowledgements
Financial support Saxon Ministry of Science and Fine Arts, the
SAB and the European Union in the project
„DynMo“ (project number 100113147)
FVV in the project "BSZII" (project number
6011333).
Discussions and data: Dave Reuss, Volker Sick (University of
Michigan)
Jens Neumann(BMW AG) and
Mark Sastuba (TU Bergakademie Freiberg)
