Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
- URL: http://arxiv.org/abs/2507.09432v1
- Date: Sun, 13 Jul 2025 00:00:47 GMT
- Title: Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
- Authors: Zefang Liu, Weston M. Stacey,
- Abstract summary: We extend NeuralPlasmaODE to perform sensitivity analysis of transport and radiation mechanisms in ITER plasmas.<n>Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential equations, to perform a sensitivity analysis of transport and radiation mechanisms in ITER plasmas. Normalized sensitivities of core and edge temperatures and densities are computed with respect to transport diffusivities, electron cyclotron radiation (ECR) parameters, impurity fractions, and ion orbit loss (IOL) timescales. The analysis focuses on perturbations around a trained nominal model for the ITER inductive scenario. Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement, while also revealing how temperature-dependent transport contributes to self-regulating behavior. These findings demonstrate the utility of NeuralPlasmaODE for predictive modeling and scenario optimization in burning plasma environments.
Related papers
- Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations [0.0]
This work presents an inverse modeling approach using a multinodal plasma dynamics model based on neural ordinary differential equations (Neural ODEs)<n>We compute the external source profiles, such as neutral beam injection (NBI) power, that drive the plasma toward the specified behavior.<n>This framework transforms the forward simulation tool into a control-oriented model and provides a practical method for computing external source profiles in both current and future fusion devices.
arXiv Detail & Related papers (2025-07-12T23:56:47Z) - Quantum Transport in Reduced Graphene Oxide Measured by Scanning Probe Microscopy [0.0]
We report combined scanning probe microscopy and transport measurements to investigate the local electronic transport properties of reduced graphene oxide (rGO) devices.<n>We demonstrate that the quantum transport properties in these materials can be significantly tuned by the electrostatic potential applied by an atomic force microscope (AFM) conducting tip.<n>Our findings emphasize the crucial role of scattering mechanisms, particularly resonant scattering caused by impurities or structural defects, in determining low-dimensional transport behavior in rGO.
arXiv Detail & Related papers (2025-02-25T02:27:15Z) - A New Bite Into Dark Matter with the SNSPD-Based QROCODILE Experiment [55.46105000075592]
We present the first results from the Quantum Resolution-d Cryogenic Observatory for Dark matter Incident at Low Energy (QROCODILE)<n>The QROCODILE experiment uses a microwire-based superconducting nanowire single-photon detector (SNSPD) as a target and sensor for dark matter scattering and absorption.<n>We report new world-leading constraints on the interactions of sub-MeV dark matter particles with masses as low as 30 keV.
arXiv Detail & Related papers (2024-12-20T19:00:00Z) - Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation.<n>This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
arXiv Detail & Related papers (2024-12-17T15:05:33Z) - Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics [0.0]
The dynamics of burning plasmas in tokamaks are crucial for advancing controlled thermonuclear fusion.<n>This study applies the NeuralPlasmaODE to simulate the complex energy transfer processes in ITER deuterium-tritium (D-T) plasmas.
arXiv Detail & Related papers (2024-08-26T16:47:20Z) - Application of Neural Ordinary Differential Equations for Tokamak Plasma
Dynamics Analysis [0.0]
This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential Equations (Neural ODEs)
Our methodology leverages Neural ODEs for the numerical derivation of diffusivity parameters from DIII-D tokamak experimental data.
These regions are conceptualized as distinct nodes, capturing the critical timescales of radiation and transport processes essential for efficient tokamak operation.
arXiv Detail & Related papers (2024-03-03T22:55:39Z) - Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
We propose a method of spatial resolution enhancement in metrology (thermometry, magnetometry, pH estimation, and similar methods) with blinking fluorescent nanosensors.<n>We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
arXiv Detail & Related papers (2024-02-27T10:38:05Z) - Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers [0.0]
This work presents the PORTALS framework, which enables the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy.
The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO.
This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.
arXiv Detail & Related papers (2023-12-19T21:33:00Z) - Plasma Surrogate Modelling using Fourier Neural Operators [57.52074029826172]
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion.
We demonstrate accurate predictions of evolution plasma using deep learning-based surrogate modelling tools, viz., Neural Operators (FNO)
We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models.
FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak.
arXiv Detail & Related papers (2023-11-10T10:05:00Z) - Unsupervised Discovery of Inertial-Fusion Plasma Physics using
Differentiable Kinetic Simulations and a Maximum Entropy Loss Function [77.34726150561087]
We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function.
We apply this framework to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect.
arXiv Detail & Related papers (2022-06-03T15:27:33Z) - Engineering the Radiative Dynamics of Thermalized Excitons with Metal
Interfaces [58.720142291102135]
We analyze the emission properties of excitons in TMDCs near planar metal interfaces.
We find suppression or enhancement of emission relative to the point dipole case by several orders of magnitude.
nanoscale optical cavities are a viable pathway to generating long-lifetime exciton states in TMDCs.
arXiv Detail & Related papers (2021-10-11T19:40:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.