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Transition in ICF Capsule Implosions
Longstanding design and reproducibility challenges in inertial confinement fusion (ICF) capsule implosion experiments involve recognizing the need for appropriately characterized and modeled three-dimensional initial conditions and high-fidelity simulation capabilities to predict transitional flow approaching turbulence, material mixing characteristics, and late-time quantities of interest—e.g., fusion yield. We build on previous coarse graining simulations of the indirect-drive national ignition facility (NIF) cryogenic capsule N170601 experiment-a precursor of N221205 which resulted in net energy gain. We apply effectively combined initialization aspects and multiphysics coupling in conjunction with newly available hydrodynamics simulation methods, including directional unsplit algorithms and low Mach-number correction-key advances enabling high fidelity coarse grained simulations of radiation-hydrodynamics driven transition. Our presentation includes discussion of the capsule initialization and implosion dynamics, analysis of the vorticity production budget, transition signatures, quantities of interest—late-time ion temperature and fusion-neutron yield, numerical uncertainty quantification, and comparisons with NIF data.
First study of Hohlraum x-ray preheat asymmetry inside an ICF capsule
In indirect drive inertial confinement fusion (ICF), laser induced Hohlraum preheat radiation (so-called M-band, >1.8 keV) asymmetry will lead to asymmetric ablation front and ablator–fuel interface hydrodynamic instability growth in an imploding capsule. First experiments to infer the M-band asymmetries at the capsule were performed on the National Ignition Facility for high density carbon (HDC) ICF capsules in low density fill (0.3 mg/cc 4 He) Au Hohlraums by time resolved imaging of 2.3 keV fluorescence emission of a smaller Mo sphere placed inside the capsule. In this study, the measured Mo emission is pole hot (P 2 > 0) since M-band is generated mainly by the outer laser beams as their irradiance at the Hohlraum wall is 5× higher than for the inner beams. P 2 has a greater negative than positive swing vs time [Δ(P 2 /P 0 )/Δt ~ 0.2/ns], giving insight into laser heated Hohlraum dynamics. P 4 asymmetry is small at the sphere due to efficient geometric smoothing of Hohlraum asymmetries at large Hohlraum -to-capsule ratios. The M-band P 2 history is qualitatively reproduced by radiation hydrodynamic HYDRA simulations. The smaller P 2 than that calculated earlier suggests either less outer beam spot motion and/or preheat emission. At late times, the observed P 2 swing is larger and P 4 is more negative than simulated, which could be due to inner beams being stopped more in the outer beams wall plasma bubble than simulated. Asymmetry at the HDC capsule inner surface (“ice–ablator interface”) is also inferred from the Mo emission asymmetry by an analytic viewfactor model, accounting for the Mo/HDC radius difference and HDC capsule opacity.
Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.
Simulating and comparing cryogenic ICF capsule implosions in iFP and xRAGE
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Hohlraum x-ray preheat asymmetry measurement at the ICF capsule via Mo ball fluorescence imaging
In inertial confinement fusion, penetrating asymmetric hohlraum preheat radiation (>1.8 keV, which includes high temperature coronal M-band emission from laser spots) can lead to asymmetric ablation front and ablator–fuel interface hydrodynamic instability growth in the imploding capsule. First experiments to infer the preheat asymmetries at the capsule were performed on the National Ignition Facility for high density carbon (HDC) capsules in low density fill (0.3 mg/cc 4He) Au hohlraums by time resolved imaging of 2.3 keV fluorescence emission of a smaller Mo sphere placed inside the capsule. Measured Mo emission is pole hot (P2 > 0) since M-band is generated mainly by the outer laser beams as their irradiance at the hohlraum wall is 5× higher than for the inner beams. P2 has a large swing vs time, giving insight into the laser heated hohlraum dynamics. P4 asymmetry is small at the sphere due to efficient geometric smoothing of hohlraum P4 asymmetries at large hohlraum-to-capsule radii ratios. The asymmetry at the HDC capsule is inferred from the Mo emission asymmetry accounting for the Mo/HDC radius difference and HDC capsule opacity.
Biermann battery magnetic fields in ICF capsules: Total magnetic flux generation
This paper focuses on the process of magnetic flux generation in inertial confinement fusion implosions. Hot-spots are shown to be dominated by fields generated during stagnation when the temperature and density gradients are largest. A scaling of hot-spot magnetic flux is derived and compared with simulations, revealing that perturbations with both larger amplitudes and higher mode numbers generate more magnetic flux. Furthermore, the model allows for greater understanding of which target designs will be susceptible to magnetohydrodynamic effects. For example, the model can be used to ascertain the time when most magnetic flux is generated. If generation is weighted more toward early times, then more high-mode magnetic field loops will be present. A hot-spot with no high-mode perturbations at time of peak neutron production can still contain significant magnetic flux on those scales. By assuming that magnetic flux is deposited at the hot-spot edge by Nernst advection, the model can be used to post-process radiation-hydrodynamics data to estimate magnetic field strengths and magnetizations.
Magnetized directly-driven ICF capsules: increased instability growth from non-uniform laser drive
Simulations anticipate increased perturbation growth from non-uniform laser heating for magnetized direct-drive implosions. At the capsule pole, where the magnetic field is normal to the ablator surface, the field remains in the conduction zone and suppresses non-radial thermal conduction; in unmagnetized implosions this non-radial heat-flow is crucial in mitigating laser heating imbalances. Single-mode simulations show the magnetic field particularly amplifying short wavelength perturbations, whose behavior is dominated by thermal conduction. The most unstable wavelength can also become shorter. 3D multi-mode simulations of the capsule pole reinforce these findings, with increased perturbation growth anticipated across a wide range of scales. Finally, the results indicate that high-gain spherical direct-drive implosions require greater constraints on the laser heating uniformity when magnetized.
Assessing hohlraum radiation preheat asymmetry at the ICF capsule via Mo ball fluorescence imaging*
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Magneto-Hydrodynamic Studies of Burn in Pre-Magnetized ICF Capsules
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An Eulerian Vlasov-Fokker–Planck algorithm for spherical implosion simulations of inertial confinement fusion capsules
Here, we present a numerical algorithm that enables a phase-space adaptive Eulerian Vlasov–Fokker–Planck (VFP) simulation of inertial confinement fusion (ICF) capsule implosions. The approach relies on extending a recent mass, momentum, and energy conserving phase-space moving-mesh adaptivity strategy to spherical geometry. In configuration space, we employ a mesh motion partial differential equation (MMPDE) strategy while, in velocity space, the mesh is expanded/contracted and shifted with the plasma’s evolving temperature and drift velocity. The mesh motion is dealt with by transforming the underlying VFP equations into a computational (logical) coordinate, with the resulting inertial terms carefully discretized to ensure conservation. To deal with the spatial and temporally varying dynamics in a spherically imploding system, we have developed a novel nonlinear stabilization strategy for MMPDE in the configuration space. The strategy relies on a nonlinear optimization procedure that optimizes between mesh quality and the volumetric rate change of the mesh to ensure both accuracy and stability of the solution. Implosions of ICF capsules are driven by several boundary conditions: (1) an elastic moving wall boundary; (2) a time-dependent Maxwellian Dirichlet boundary; and (3) a pressure-driven Lagrangian boundary. Of these, the pressure-driven Lagrangian boundary driver is new to our knowledge. The implementation of our strategy is verified through a set of test problems, including the Guderley and Van-Dyke implosion problems — the first-ever reported using a Vlasov–Fokker–Planck model.
Evolution of highly multimodal Rayleigh–Taylor instabilities
Rayleigh–Taylor (RT) instabilities are important fluid instabilities that arise in inertial confinement fusion (ICF) capsule implosions, and many other contexts. Multi-mode coupling is observed in experiments and plays a substantial role in material mix from RT instabilities. In this work, we study the evolution of highly multimodal perturbations (power law distribution) that approximate those found at manufactured material interfaces. We use simulations of over 2000 different perturbations in the LANL code xRAGE to identify distinct phases in the processes of bubble growth and bubble merger which can be visualized in a 2D phase portrait with clear regimes of mode growth and decay. Our results show that the dynamic evolution of the instability strongly depends on the mode of the perturbations and mode interactions. The merger process accelerates bubble growth. A non-Markovian region and a transition of the instability from: (1) initial exponential growth to (2) linear growth and to (3) quadratic growth and asymptotic behavior, are clearly captured in the phase space. We have developed a quantitative model of bubble growth that reproduces the dynamic behavior of ensembles of perturbations. Implications for ICF capsules designed for robustness against instabilities are discussed.
Using distributions to understand neutron and x-ray production in ICF ignition capsules and other high energy density plasmas
Here this paper describes how x-ray and neutron distribution functions can be useful tools to visualize the conditions measured in many types of plasma physics experiments. In particular, we model a standard inertial confinement fusion ignition capsule that consists of a Si doped plastic ablator surrounding a layer of deuterium–tritium (DT) ice as the yield varies from 18 kJ to 16.7 MJ and use the distribution functions to show that neutrons and high energy x rays (15 keV) are produced under similar conditions when the yield is low. However, as the capsule starts to support a propagating burn due to alpha heating, the x rays and neutrons are produced under somewhat different conditions in different parts of the plasma. In particular, the x-ray production takes place mainly in the hot plastic ablator for the full yield ignition capsule under quite different plasma conditions from the DT region producing the 14 MeV neutrons, which results in x-ray images with larger radii than the corresponding neutron images. These same distribution functions can be applied to many other plasma physics experiments.
First M-band preheat asymmetry measurement at the capsule in ICF hohlraums via Mo ball fluorescence imaging
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High-rate magnetron sputter deposition of low-stress boron carbide films on tilted substrates
Boron carbide is attractive for several applications, including nuclear fuel capsules for inertial confinement fusion (ICF). The fabrication of ICF capsules involves the deposition of ultrathick coatings with density and thickness uniformity on submicron length scales on non-planar (spherical) substrates. Such a deposition requires control of the deposition rate, residual stress, and film microstructure. Here, we systematically study the direct-current magnetron sputter deposition of B 4 C coatings with a full-face-erosion magnetron source as a function of substrate tilt and Ar working gas pressure. Film properties are correlated with results of plasma diagnostics and predictions of Monte Carlo simulations of ballistic sputtering and gas-phase transport. The fabrication of low-stress amorphous B 4 C films with deposition rates of 7μm/h is demonstrated.
Optimizing Simulation Fidelity in Direct-Drive Inertial Confinement Fusion with Cassio
Recently, the National Ignition Facility (NIF) demonstrated that inertial confinement fusion (ICF) is capable to achieve thermonuclear (TN) ignition in the laboratory, making it a crucial method on the path to replicate the Sun’s power production mechanism on Earth. However, the physics governing the high-energy density environments is very complex and remains a challenge to fully understand and model. For example, dopants in the TN fuel are important diagnostic tools to extract the thermodynamic conditions of the plasma. However, if their concentration is chosen too high, they can significantly degrade the performance of an ICF capsule. In this study, we use the Los Alamos National Laboratory radiation-hydrodynamics code Cassio to model ICF implosions of capsules which contain deuterium fuel with high-Z dopants like Krypton and Argon from the high-Z campaign conducted 15 years ago. We focus on how chosen computational and physics parameters influence the implosion outcomes. By systematically changing the resolution of the computational mesh and the photon energies as well as modifying settings for the laser drive and TN fuel pre-heat effects, we assess the impact on experimentally measured performance metrics like neutron production from TN burn and x-ray emission during the implosion. Our results will help to improve the fidelity of simulations and guide future numerical studies and experimental designs.
Inference of three-dimensional hot-spot and shell morphology in inertial confinement fusion experiments using a convolutional neural network
The performance of inertial confinement fusion (ICF) implosions is sensitive to the three-dimensional (3D) morphology of the hot-spot and shell configurations. The ability to infer shell-mass uniformity and reconstruct 3D hot spots is crucial for quantifying the degradation of ignition criteria and improving symmetry in ICF implosion experiments. In this work, we present a deep-learning convolutional neural network (CNN) for reconstructing 3D hot-spot and shell structures for ICF capsules. The 3D geometry of the hot spot is reconstructed from x-ray images measured from multiple lines of sight on OMEGA. The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. This work demonstrates a CNN framework that successfully reconstructs 3D capsule structures from two-dimensional images in ICF implosions.
Temperature and density dependent pair potential for deuterium under shock
Large-scale classical molecular dynamics (CMD) simulations naturally include the microscopic physics necessary for atomistic modeling of shock release at the ablator-fuel interface in an inertial confinement fusion (ICF) capsule. Here, the multi-megabar shocks utilized in ICF experiments can drive the deuterium fuel from ambient to electron volt temperatures (T) and multi-fold compression. Modeling interatomic interactions over such an extreme range of conditions is challenging for empirical bond order potentials. We generate a pair potential for deuterium with explicit temperature and mass density dependence from ab initio density functional theory molecular dynamics using the iterative Boltzmann inversion method. This potential accurately reproduces the radial distribution functions and pressures from DFT in CMD equilibrium simulations across a wide range of thermodynamic conditions, yet fails to return the expected Hugoniot relations when used in direct CMD shock simulations.