In order to produce millimeter-scale plasmas for the research of laser-plasma interactions (LPIs), gasbag target is designed and tested on Shenguang-III prototype laser facility. The x-ray pinhole images show that millimeter-scale plasmas are produced with the gasbag. The electron temperature inferred from the stimulated Raman scattering (SRS) spectrum is about 1.6 keV. The SRS spectrum also indicates that the electron density has a flat region within the duration of 200 ps. The obvious differences between the results of the gasbag and that of the void half hohlraum show the feasibility of the gasbag target in creating millimeter-scale plasmas. The LPIs in these millimeter-scale plasmas may partially mimic those in the ignition condition because the duration of the existence of a flat plasma density is much larger than the growth time of the two main instabilities, i.e., SRS and stimulated Brillouin scattering (SBS). So we make the conclusion that the gasbag target can be used to research the large-scale LPIs.
In data-driven modeling of spatiotemporal phenomena careful consideration is needed in capturing the dynamics of the high wavenumbers. This problem becomes especially challenging when the system of interest exhibits shocks or chaotic dynamics. We present a data-driven modeling method that accurately captures shocks and chaotic dynamics by proposing a new architecture, stabilized neural ordinary differential equation (ODE). In our proposed architecture, we learn the right-hand-side (RHS) of an ODE by adding the outputs of two NN together where one learns a linear term and the other a nonlinear term. Specifically, we implement this by training a sparse linear convolutional NN to learn the linear term and a dense fully-connected nonlinear NN to learn the nonlinear term. This contrasts with the standard neural ODE which involves training a single NN for the RHS. We apply this setup to the viscous Burgers equation, which exhibits shocked behavior, and show stabilized neural ODEs provide better short-time tracking, prediction of the energy spectrum, and robustness to noisy initial conditions than standard neural ODEs. We also apply this method to chaotic trajectories of the Kuramoto-Sivashinsky equation. In this case, stabilized neural ODEs keep long-time trajectories on the attractor, and are highly robust to noisy initial conditions, while standard neural ODEs fail at achieving either of these results. We conclude by demonstrating how stabilizing neural ODEs provide a natural extension for use in reduced-order modeling by projecting the dynamics onto the eigenvectors of the learned linear term.
Two neutron time-of-flight (nToF) detectors have been employed to measure the neutron time-of-flight spectrum in different lines-of-sight, i.e., at the equator plane and the south pole, on Shenguang-III (SG-III) laser facility. The contribution of scattered neutrons has been calculated with the Monte Carlo code JMCT for each nToF detector. The results show that the scattered neutron spectrum is dominated by neutrons scattered on materials in the experiment hall, including the vacuum chamber. The shape of the scattered neutron spectrum depends on the view line, which has been observed with nToF detectors located in the experiment hall of the SG-III laser facility. A method based on the convolution of the calculated neutron time-of-flight spectrum and the instrument response function has been developed for the ion temperature determination. The calculated neutron spectra with the contribution of scattered neutrons can fit the measured results. No obvious ion temperature anisotropy has been observed on the SG-III laser facility at present.
Abstract In this work, we propose a data-driven method to discover the latent space and learn the
corresponding latent dynamics for a collisional-radiative (CR) model in radiative plasma
simulations. The CR model, consisting of high-dimensional stiff ordinary differential
equations (ODEs), must be solved at each grid point in the configuration space, leading
to significant computational costs in plasma simulations. Our method employs a physicsassisted
autoencoder to extract a low-dimensional latent representation of the original CR
system. A flow map neural network is then used to learn the latent dynamics. Once trained,
the reduced surrogate model predicts the entire latent dynamics given only the initial condition
by iteratively applying the flow map. The radiative power loss is then reconstructed
using a decoder. Numerical experiments demonstrate that the proposed architecture can
accurately predict both the full-order CR dynamics and the radiative power loss rate.
In this work we construct a high-order, single-stage, single-step positivity-preserving method for the compressible Euler equations. Space is discretized with the finite difference weighted essentially non-oscillatory (WENO) method. Time is discretized through a Lax-Wendroff procedure that is constructed from the Picard integral formulation (PIF) of the partial differential equation. The method can be viewed as a modified flux approach, where a linear combination of a low- and high-order flux defines the numerical flux used for a single-step update. The coefficients of the linear combination are constructed by solving a simple optimization problem at each time step. The high-order flux itself is constructed through the use of Taylor series and the Cauchy-Kowalewski procedure that incorporates higher-order terms. Numerical results in one- and two-dimensions are presented.
Nonlinear dynamics of runaway electron induced wave instabilities can significantly modify the runaway distribution critical to tokamak operations. Here we present the first-ever fully kinetic simulations of runaway-driven instabilities towards nonlinear saturation in a warm plasma as in tokamak start up. It is found that the slow-X modes grow an order of magnitude faster than the whistler modes, and they parametrically decay to produce whistlers much faster than those directly driven by runaways. These parent-daughter waves, as well as secondary and tertiary wave instabilities, initiate a chain of wave-particle resonances that strongly diffuse runaways to the backward direction. This reduces almost half of the current carried by high-energy runaways, over a time scale orders of magnitude faster than experimental shot duration. These results beyond quasilinear analysis may impact anisotropic energetic electrons broadly in laboratory, space and astrophysics.
A high-order finite difference numerical scheme is developed for the ideal magnetohydrodynamic equations based on an alternative flux formulation of the weighted essentially non-oscillatory (WENO) scheme. It computes a high-order numerical flux by a Taylor expansion in space, with the lowest-order term solved from a Riemann solver and the higher-order terms constructed from physical fluxes by limited central differences. The scheme coupled with several Riemann solvers, including a Lax-Friedrichs solver and HLL-type solvers, is developed on general curvilinear meshes in two dimensions and verified on a number of benchmark problems. In particular, a HLLD solver on Cartesian meshes is extended to curvilinear meshes with proper modifications. A numerical boundary condition for the perfect electrical conductor (PEC) boundary is derived for general geometry and verified through a bow shock flow. Numerical results also confirm the advantages of using low dissipative Riemann solvers in the current framework.
Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom or ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate CR modeling is essential in radiative plasma modeling for magnetic fusion, especially when significant amount of impurities are introduced into the plasmas. In radiative plasma simulations, a CR model, which is a set of high-dimensional stiff ordinary differential equations (ODE), needs to be solved on each grid point in the configuration space, which can overwhelm the plasma simulation cost. In this work, we propose a deep learning method that discovers the latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. To facilitate coupling of the latent space CR dynamics with the plasma simulation model in physical variables, our latent space in the autoencoder must be a grey box, consisting of a physical latent space and a data-driven or blackbox latent space. It has been demonstrated that the proposed architecture can accurately predict both the full-order CR dynamics and the critical physical quantity of interest, the so-called radiative power loss rate.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)