structures in mhd turbulencesirius.unist.ac.kr/kaw6/pdf/18. david porter kaw6.pdf · 2011-12-04 ·...
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Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved. © 2009 Regents of the University of Minnesota. All rights reserved.
Supercomputing Institute for Advanced Computational Research
6th Korean Astrophysics Workshop
Structures in MHD Turbulence
David H. Porter University of Minnesota
Collaborators: Tom Jones Dongsu Ryu Jungyeon Cho
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Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Outline • Differences between Neutral fluid and MHD Turbulence
– Use simulation with initial beta = 106, solenoidal driving, 20483 mesh – T=20: high beta & developed velocity spectrum – T=130: B-field near saturation
• Structures in strong field MHD turbulence – Motivation: visualizations – Measures
• Characterize • Automatically identify • Evaluate statistical significance
• Development structures – rate of strain field – time scale for development
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Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Compare structures at T=20 & 130 T=20: Fluid
Turbulence is fully developed & B-fields is weak
T=130: B-field strong enough to dominate flow
T=20 T=130
KE
BE
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Supercomputing Institute for Advanced Computational Research
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Compensated V & B Spectra: T=20 V-field is fully
developed.
B-field still very weak.
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Supercomputing Institute for Advanced Computational Research
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Compensated V & B spectra: T=130 B-field now
dominant for k>10.
V-field strongly influenced.
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Supercomputing Institute for Advanced Computational Research
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Vorticity T=20 (in slab) • B-field is weak and
does not back-react on flow.
• Flow behaves essentially like a neutral fluid.
• Flow on smallest (dissipation) scale dominated by vortex tubes.
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Supercomputing Institute for Advanced Computational Research
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Magnetic Energy: T=20 (in slab) • B-field grows fastest
in where rate of strain is largest,
• Rate of stain is larger and persistent in vortex tubes.
• Weak B-field tents to trace vortex tubes.
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Supercomputing Institute for Advanced Computational Research
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Vorticity T=130 (in slab) • Flow now strongly
influence by B-field.
• Vortex tubes are gone
• Velocity fluctuations on small scales now greatly inhibited by B
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Supercomputing Institute for Advanced Computational Research
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B Field Energy T=130 (in slab) • B-field has strong
filamentary structures.
• B-field appears to have many thin, parallel, and closely packed structures.
• Adjectives: – Fibers – Layers
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Supercomputing Institute for Advanced Computational Research
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The Point • Weak B-field: very much like neutral fluid turbulence
– Small scales dominated by vortex tubes – Velocity spectra Kolmogorov like at intermediate scales – B-field follows Velocity structures
• Fastest development in vortex tubes
– NO SUPRIZES
• Strong (nearly saturated) B field: very different – B-field back reacting on velocity – Vortex tubes gone – Strongest B organized in fibers or layers
CHARACTERIZE/QUANTIFY SATURATED STATE
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Supercomputing Institute for Advanced Computational Research
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Vorcity & B-field at Saturation • Vorticity and B-field
appear to be co-local on large scales
• Details – T=130 – Tubes show B-field – Volume rendered vorticity – Structures span range of
driving (1/2 of box) – B more coherent than
Vorticity.
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Supercomputing Institute for Advanced Computational Research
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Correlation Coefficient • Correlation coefficient between magnitude of vorticity and
magnetic field strength.
• Evaluate on raw data to measure if fields are correlated on smallest scale
• Evaluate on blended data (2x2x2, 4x4x4, …) so measure correlation as a function of scale
€
r(P,Q ) =< PQ > − < P ><Q >
[(< P2 > − < P >2 )(<Q 2 > − <Q >2 )]1/ 2
P = log(| ω |)Q = log(| B |)
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Supercomputing Institute for Advanced Computational Research
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Log Vorticity Vs. Log B Blend Factor = 1 T=130 Run: dmt36
Correlation Coef r=0.219
€
Log(|ω |)
€
Log(|B |)
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Supercomputing Institute for Advanced Computational Research
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Log Voricity Vs. Log B (blended) Blend Factor = 2
Correlation Coef r=0.233
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Supercomputing Institute for Advanced Computational Research
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Log Voricity Vs. Log B (blended) Blend Factor = 4
Correlation Coef r=0.265
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Supercomputing Institute for Advanced Computational Research
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Log Voricity Vs. Log B (blended) Blend Factor = 8
Correlation Coef r=0.326
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Supercomputing Institute for Advanced Computational Research
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Log Voricity Vs. Log B (blended) Blend Factor = 16
Correlation Coef r=0.419
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Supercomputing Institute for Advanced Computational Research
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Correlation of B with Voricty at Saturtion Correlation fn. vs.
blending factor
BF=1: no blending Correlation~0.2
BF=512: ¼ of box Correlation~0.8
B field & Vorticity structures are in near each other, but not at the same points.
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Supercomputing Institute for Advanced Computational Research
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Motivation & Goals • Both visualizations and cross-correlation indicate
– B-field and vorticity are not co-local on smallest scales – B-field and vorticity are co-local on larger scales
• Visually: B & vorticity – Aligned – Interleaved – Part of larger structures
• Goals: – Characterize structures – Measure statistical significance – Identify generation mechanism
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Supercomputing Institute for Advanced Computational Research
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4 fiels in cross-section of a strip • Fields shown:
– Density – Magnetic energy – Vorticity – Normal B (out of plane)
• Multiple, closely spaced nearly parallel layers
• All 4 fields influence by (participate in) the structure
€
ρ
€
|ω |€
B2 /2
€
Bn
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Supercomputing Institute for Advanced Computational Research
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Premis: Fields Organized in Layers • Layers are thin • All fields show multiple stacked layers • Visually well defined direction of most rapid variation
• How well can field variation be characterized as functions of just one variable?
• Field gradients locally indicate direction of variation • However, volume averages of gradients will tend to 0
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Supercomputing Institute for Advanced Computational Research
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Principle Directions of Variation Gradient matrix: tensor product of gradient
vector with itself Q in {rho,Vx,Vy,Vz,Bx,By,Bz
Real & symmetric, which means: Eigenvectors are orthogonal Eigen values are real
Order eigenvalues by value
Label eigenvectors correspondingly
In eigenvector coordinates, gradient matrix is diagonal .
Eigenvalues are non-negative, and generically positive
€
aij =∂Q∂xi
∂Q∂xj V
€
aii =λi = ( ˆ e i • ∇ Q )2
V≥ 0€
λmax ≥ λ,mid ≥ λmid
€
{ˆ e max, ˆ e mid , ˆ e min}
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Supercomputing Institute for Advanced Computational Research
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Measure of Allignment Ratio of max to mid to min eigenvalues Eige values will vary with field
Strong alignment
Weak alignment €
λmax /λmin >>1
€
λmax /λmin ~ 1
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Supercomputing Institute for Advanced Computational Research
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Samples Taken in Spherical Volumes
Gradient matrix
Evaluated in spherical volumes
1000 spherical volumes 10x10x10 array
Radius of each sphere R=0.4 Full domain is 10 on a side
€
aij =∂Q∂xi
∂Q∂xj V
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Supercomputing Institute for Advanced Computational Research
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Shapes of structures: RHO
€
λmid /λmin
€
λmax /λmid
Spaghetti
Lasagna
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Supercomputing Institute for Advanced Computational Research
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Lambda Ratios for Bx
€
λmax /λmid
€
λmid /λmin
Lasagna
Spaghetti
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Supercomputing Institute for Advanced Computational Research
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Lambda Ratios for Vx
€
λmax /λmid
€
λmid /λmin
Lasagna
Spaghetti
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Supercomputing Institute for Advanced Computational Research
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Measure of how strongly all variations are in just one direction
• Take minimum of ratio over all field variables as a conservative measure of alignment.
• Strongest variation in just one direction (lasagna)
• If R is large, then ALL fields have just one direction in which the primarily vary (i.e., all variations orthogonal directions are small by comparison in the mean-square).
€
R ≡min(λmax (Q) /λmid (Q), Q∈{ρ,ui,bi})
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Supercomputing Institute for Advanced Computational Research
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Alignment Strength: Distributions
R
T=130
T=20
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Supercomputing Institute for Advanced Computational Research
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Alignment Between Fields
Are the directions of variation of different fields aligned?
C is cosine of maximum angle between strongest direction of variation between any two fields
If C is large, then directions of variation of all fields are nearly aligned
€
C ≡ min(| ˆ e max(Q )• ˆ e max(P) |, P & Q ∈{ρ ,ui ,bi})
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Supercomputing Institute for Advanced Computational Research
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Alignment Between Fields
C
T=20
T=130
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Supercomputing Institute for Advanced Computational Research
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Alignment of Variation Vs. B-Field strength
|B|
C
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Supercomputing Institute for Advanced Computational Research
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Min_L2/L1 vs. Min_dot
C
R
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Supercomputing Institute for Advanced Computational Research
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Alignment Vs. Anisotropy of B
C
€
| B |ˆ e max ⋅
B
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Supercomputing Institute for Advanced Computational Research
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Alignment of B within a Layer
In regions containing well defined layers (C>>1), B is the plane of the layer.
How is B lined up with the direction of most rapid variation?
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Supercomputing Institute for Advanced Computational Research
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Angular B In A Layer
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Supercomputing Institute for Advanced Computational Research
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B-Field Sampled Across Slip Surface • T=130 • View in plane of
slip surface (along curl(V))
• B trace foot points sampled on line normal to slip surface
• B field: To left above To view inside To right below
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B-field in Slip Surface • T=130 • View normal to
slip sufrace • Grean to white
ramps show direction of B
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Rotation of
structure
Color of tubes shows relative B field strength
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Supercomputing Institute for Advanced Computational Research
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Development of Laminated Sturcutres
• Can measure rate of strain in principle directions of variation.
• Is rate of strain sufficient to produce thin layers?
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Rate of Strain in Direction of Max Variation
• 7 variables lead to 7 directions
• Principle direction of average tensor product
• V_L,L is rate of strain in common direction of Max variation across all 7 variables
€
ˆ e max(Q ), Q ∈{ρ ,VX ,VY ,VZ ,BX ,BY ,BZ}
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ˆ e max (Q) ˆ e max (Q)Q⇒ ˆ e L
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VL,L = ˆ e L ⋅∂
V ∂ x ⋅ ˆ e L
ball
Can measure rate of strain in MID and MIN directions also
![Page 42: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/42.jpg)
Supercomputing Institute for Advanced Computational Research
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Rate of Strain along MAX Variation
![Page 43: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/43.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Rate of Strain Along MID Variation
![Page 44: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/44.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Rate of Strain Along MIN Variation
![Page 45: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/45.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Rate of Strain Layers • Rate of Strain in direction of most rapid variation is
predominantly negative Median is always negative most elements are compressing
(squeezing) in the direction of most rapid variation
• Amplitude of strain rate enough to explain filaments: In simulation units: Median e-folding times < 3 over last 100 time units 30 e-folding times
![Page 46: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/46.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Movie of squeeze
From d6a52 50% compressive
Move starts with a strong filament and follows back in time
![Page 47: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/47.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Weak B-field turbulence – Very much like neutral fluid turbulence – B-field follows the flow – B develops fastest on small scales in vortex tubes
Distinctive features of saturated B-field turbulence – Layers – Interleaved fields – Multiple criss-crossed laminates – Rate of strain field produces and maintains laminated structures
Conclusions
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![Page 48: Structures in MHD Turbulencesirius.unist.ac.kr/kaw6/pdf/18. David Porter KAW6.pdf · 2011-12-04 · • Structures in strong field MHD turbulence – Motivation: visualizations –](https://reader034.vdocuments.us/reader034/viewer/2022042913/5f4c3f0d8bfc473ac5508c02/html5/thumbnails/48.jpg)
Supercomputing Institute for Advanced Computational Research
© 2009 Regents of the University of Minnesota. All rights reserved.
Thank You