ANU Institutional Update
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�Monday 18 March, 2024
Acknowledgement: Australian Research Council, Simons Foundation
R. L. Dewar [1], A. Tassavoli [1], D. Muir [1], S. Jeyakumar [1,3], D. Pfefferlé [1,3], N. Bohlsen [1],
R. Tang [1], K. Duru [1], V. Robins [1], N. Cross [1], S. R. Hudson [4], Z. Qu [5] , M,. Kraus [6], N. Duignan[7]
Prof. Matthew Hole[1,2] (matthew.hole@anu.edu.au)
Mathematical Sciences Institute & School of Computing
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University of Sydney
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Simons Retreat at the ANU: 4-15 Dec. 2023
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New “MRxMHD” approach to equilibrium �and stability
Captures magnetic islands ….
and chaotic fields
Fusion Plasma Theory and Modelling
Fully 3D plasmas
Burning Plasmas
/Strong Auxiliary � heating
Integrated
modelling
Basic
Science
energetic ion physics
Model-data fusion using Bayesian inference
M. J. Hole, R. L. Dewar, A. Tassavoli,
�Students:
S. Jeyakumar, J. Doak, M. Thompson, D. Muir, R. Tang, N. Bohlsen, �T. Malcolm, L. Huang �
MSI Collaborators:
L. Ferrario, K. Duru
Local: Z. Qu (NTU), �D. Pfefferlé (UWA), �N. Duignan (Sydney)
DG methods and Non-Linear Optimisation for MRxMHD
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[R. Tang, K. Duru, D. Muir, M. J.Hole, R. L. Dewar ]
DG methods and Non-Linear Optimisation for MRxMHD�1D illustration
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| Exact [p + 1/2 B^2] | UnCorrected | Corrected |
Plasma Interface 1 | 0.0 | -0.5 | -0.00038976952476421634 |
Plasma Interface 2 | 0.0 | -0.8 | 4.117509425860533e-05 |
Plasma Interface 3 | 0.0 | -0.4 | 1.0349951263310952e-05 |
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Relaxed magnetohydrodynamics with weak ideal-Ohm’s-law constraint
[A. Tassavoli, S. R. Hudson, Z. Qu, R. L. Dewar, M. J.Hole]
The Hamiltonian minimization
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The global constraints are:
The weak local constraint is:
The hard microscopic constraint is:
Other microscopic constraints are relaxed
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IOL constraint
Euler-Lagrange Equations
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Isothermal ideal gas equation
Hamiltonian minimization in slab geometry
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Stable and accurate method for simulating anisotropic diffusion in toroidally confined magnetic fields
[D. Muir, K. Duru, M. J. Hole, S. R. Hudson]
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Developing structure-preserving particle methods for different collision operators
A structure-preserving particle discretisation for the Lenard-Bernstein collision operator (https://arxiv.org/abs/2309.16894)
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[1] Morrison, P. J. (1986). A paradigm for joined Hamiltonian and dissipative systems. Physica D: Nonlinear Phenomena, 18(1–3), 410–419. https://doi.org/10.1016/0167-2789(86)90209-5
Illustration of a Poisson manifold. Surfaces are of constant entropy, where ideal (Hamiltonian) dynamics take place, while collisions carry you across different surfaces through the changing entropy. Image courtesy Michael Kraus, IPP Garching.
[S. Jeyakumar, M. Kraus, D. Pfefferlé, M. J. Hole].
Automated field line classification with persistent homology
Question: Can we determine the class of a field line (stochastic, KAM torus, island chain, etc.) from only the Poincare section automatically?
Answer: Yes, with persistent Homology
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Form sequence of simplicial complexes by progressively connecting points which are further and further apart.
Add triangles as soon as possible (Vietoris-Rips filtration). Homology classes are born and die (display on Persistence Diagram).
[N. Bohlsen, V. Robins ]
Recent Developments
Problem: Previous method for separating islands from topological noise was unrigorous (just looked at lifetime of the class). Can we do better?
Note: In 2023 Bobrowski and Skraba (A universal null-distribution for topological data analysis) showed empirically that the distribution of multiplicative persistence for any random cloud of points can be transformed to a universal distribution.
This gives us a Null-hypothesis to we can compare the multiplicative persistence of our field line orbits against.
Idea: Use hypothesis testing to only count statistically significant topological features as islands.
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Different CDFs for death/birth for different random processes
Same CDF after transformation on the data (left-skewed gumbel)
Hypothesis testing with a Bonferroni correction does not count the number of islands in a chain correctly but counts the large islands in a chaotic layer well.
FLR Effects on Ballooning Stability Near Field null
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[N.Cross, M. J. Hole]
The Effect of Pressure Anisotropy on ELMs
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[J. Doak, M. J. Hole]
Anisotropic Pressure Models in the Pedestal Region
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MHD Equilibrium with Neural Models
Grad-Shafranov Equation (with a certain assumptions from the Soloviev profiles):
Enforcing the equation into a loss term
Neural Models�(e.g., PINN, Neural Operator)
Equilibrium state
Training with respect to the loss terms
Output
Input (R,Z)
[F. Rizqan, C. Gretton, M. J. Hole]
B. Jang et al “Grad-Shafranov equilibria via data-free physics informed neural networks”, https://arxiv.org/abs/2311.13491
[D. Pfefferlé, D. Perella, N. Duignan]