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It is shown analytically, in a suitable limit, that a sharp pattern-selection mechanism exists in driven explosive crystallization: after the onset of a morphological instability of the steady-state crystallization front, nonlinear effects cause the front to develop a spatial pattern with a definite wavelength. The amplitude, wavelength, and shape of the selected pattern are calculated analytically.
We propose a model for the jetting activity that is commonly observed in the Sun's corona, especially in the open-field regions of polar coronal holes. Magnetic reconnection is the process driving the jets and a relevant magnetic configuration is the well-known null point and fan separatrix topology. The primary challenge in explaining the observations is that reconnection must occur in a short-duration energetic burst rather than quasi-continuously as is implied by the observations of long-lived structures in coronal holes, such as polar plumes, for example. The key idea underlying our model for jets is that reconnection is forbidden for an axisymmetric null-point topology. Consequently, by imposing a twisting motion that maintains the axisymmetry, magnetic stress can be built up to large levels until an ideal instability breaks the symmetry and leads to an explosive release of energy via reconnection. Using 3D MHD simulations we demonstrate that this mechanism does produce jets with high speed and mass, driven by nonlinear Alfven waves. We discuss the implications of our results for observations of the solar corona.
The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.
Lightning-produced electromagnetic effects may produce significant modifications in the ionospheric plasmas. An outstanding phenomenon investigated in this paper is the so-called explosive spread F, whose close link with lightning has been identified (Woodman and Kudeki, 1984). Parametric instability excited by the lightning-induced whistler waves is proposed as a potential source mechanism causing the explosive spread F.
The first few seconds of the explosion of SN 1987A are modeled here using a 2D cylindrical geometry smooth particle hydrodynamics code. The success of the explosion is determined to be sensitive to the duration of the infall, the timing of the bounce, and the subsequent neutrino heating. A semianalytical model for the initial structure of the collapsed object is used to present two simulations that differ by the mass that has been allowed to collapse into a neutron star prior to the bounce. In the case of a short initial infall, the explosion fails due to excessive cooling. For a longer initial infall, the cooling is less and the explosion is successful although relatively weak. It is shown that in this case, a successful explosion is brought about by the presence of an entropy gradient which, combined with the gravitational pull of the neutron star, leads to extremely strong instabilities. The critical importance of the global circulation for the success of the explosion is demonstrated.
Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.
We present self-consistent 3D core-collapse supernova simulations of a 40 ${M}_{\odot }$ progenitor model using the isotropic diffusion source approximation for neutrino transport and an effective general relativistic potential up to `0.9 s postbounce. We consider three different rotational speeds with initial angular velocities of ${{\rm{\Omega }}}_{0}=0$, 0.5, and 1 rad s -1 and investigate the impact of rotation on shock dynamics, black hole (BH) formation, and gravitational wave (GW) signals. The rapidly rotating model undergoes an early explosion at ~250 ms postbounce and shows signs of the low $T/| W| $ instability. We do not find BH formation in this model within ~460 ms postbounce. In contrast, we find BH formation at 776 ms postbounce and 936 ms postbounce for the nonrotating and slowly rotating models, respectively. The slowly rotating model explodes at ~650 ms postbounce, and the subsequent fallback accretion onto the proto–neutron star (PNS) results in BH formation. In addition, the standing accretion shock instability induces rotation of the PNS in the model that started with a nonrotating progenitor. Assuming conservation of specific angular momentum during BH formation, this corresponds to a BH spin parameter of a = J/M = 0.046. However, if no explosion sets in, all the angular momentum will eventually be accreted by the BH, resulting in a nonspinning BH. The successful explosion of the slowly rotating model drastically slows down the accretion onto the PNS, allowing continued cooling and contraction that results in an extremely high GW frequency (f ~ 3000 Hz) at BH formation, while the nonrotating model generates GW signals similar to our corresponding 2D simulations.
An understanding of material phase transitions in megaampere pulsed-power–driven exploding conductors is important for predicting the growth of hydrodynamic instabilities in magneto-inertial fusion concepts. Here, this study analyzes phase transitions in electrical conductor explosions using 1D Lagrangian and 2D arbitrary Lagrangian–Eulerian resistive magnetohydrodynamic simulations to show that micrometer-scale surface roughness can lead to the electrothermal instability (ETI), a feedback effect that concentrates resistive heating and leads to early melting and ablation. Simulations of the Mykonos electrothermal instability II (METI-II) experiment show melting begins 19% sooner for machined rods with micrometer-scale surface roughness than for rods without these features. The surface magnetic field is 41 T around the initial region of melt, representing a lower magnitude than both the 86 T from 1D simulations and the 85 T threshold reported elsewhere. In 2D simulations with micrometer-scale surface roughness, temperature measurements indicate the critical point temperature of aluminum is reached 17% faster in comparison with 1D simulations. Values from 2D simulations with surface roughness align with predictions from ETI theory, and the observed temperature redistribution further supports the ETI as an underlying mechanism. Simulation results are validated against experimental photonic Doppler velocimetry data. This study shows 1D simulations are adequate to model conductors with sub-micrometer-scale surface roughness in this high-energy-density regime; however, 2D or 3D simulations are required to capture the full range of physics for accurately describing phase transitions in conductors with micrometer-scale or larger surface roughness.
In the solar atmosphere, flux ropes are subject to current-driven instabilities that are crucial in driving plasma eruptions, ejections, and heating. A typical ideal magnetohydrodynamics instability developing in flux ropes is the helical kink, which twists the flux rope axis. The growth of this instability can trigger magnetic reconnection, which can explain the formation of chromospheric jets and spicules, but its development has never been investigated in a partially ionized plasma (PIP). Here, we study the kink instability in PIP to understand how it develops in the solar chromosphere, where it is affected by charge-neutral interactions. Partial ionization speeds up the onset of the nonlinear phase of the instability, as the plasma β of the isolated plasma is smaller than the total plasma β of the bulk. The distribution of the released magnetic energy changes in fully ionized plasma and PIP, with a larger increase in internal energy associated with the PIP cases. The temperature in PIP increases faster also due to heating terms from the two-fluid dynamics. PIP effects trigger kink instability on shorter time scales, which is reflected in more explosive chromospheric flux rope dynamics. These results are crucial to understanding the dynamics of small-scale chromospheric structures—minifilament eruptions—that thus far have been largely neglected but could significantly contribute to chromospheric heating and jet formation.
A speculative model for the nonlinear phase of the collisionless tearing instability is developed for the case of a single long wavelength tearing mode. Using an energy principle formalism, we find that the nonlinear growth rate is linearly proportional to the mode amplitude. Hence in the nonlinear phase, the tearing mode grows explosively in time, and saturates when the width of the magnetic islands become comparable to the thickness of the current sheet. For typical plasma sheet parameters, the explosive phase lasts 5-10 minutes, and develops cross-tail emf's of several 100 KV.
The earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ($M_w$) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an $M_w$ 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. As a result, our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.
Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.
Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. In conclusion, we are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE.
Attention is given to the cumulative effect of many SNs in a galaxy, especially a young galaxy, as well as the effect of the blast wave resulting from the combination of all individual SNs on the intergalactic medium (IGM). It is suggested that the IGM can be shock-heated by this process, so that in the late stages of an individual expanding galaxy's shock a shell may form whose mass is many times greater than that of the seed galaxy. If this shell fragments, due to gravitational instability, it will give rise to a new generation of galaxies. SN explosions may thereby provide a hydrodynamic amplifier of some significance in the process of galaxy formation.
We use the astrophysical hydrocode ZEUS to compute high-resolution models of the disruption and deceleration of cometary fragments striking Jupiter. We find that simple analytic and semianalytic models work well for kilometer-size impactors. We show that previous numerical models that placed the explosion much deeper in the atmosphere failed to fully resolve important gasdynamical instabilities. These instabilities tear the comet apart, greatly increase its effective cross section, and bring it to an abrupt halt. A 1 km diameter fragment loses over 90% of its kinetic energy within a single scale height at an atmospheric pressure of order 10 bars. For all practical purposes, it explodes.
The Richtmyer–Meshkov instability (RMI) is shock driven and affects many phenomena from inertial fusion to supernova explosions. The behavior of single-modes in the RMI has been studied extensively but less is known with the broadband perturbations that occur in applications. Here, we describe extensive numerical simulations and modeling of the RMI with broadband perturbations with an initial power spectrum of the form P ∝ km, where k is the wavenumber. The hydrodynamic condition is the same as in the θ-Group Collaboration [Thornber et al., “Late-time growth rate, mixing, and anisotropy in the multimode narrowband Richtmyer–Meshkov instability: The θ-group collaboration,” Phys. Fluids 29, 105107 (2017)] with a Mach ∼1.86 shock and gamma-law = 5/3 fluids with Atwood number A = −0.5. The bubble amplitude hB is found to grow in two stages. Initially, hB undergoes a phase-inversion and grows linearly in time (t) at a rate consistent with a linear theory. Asymptotically, hB grows as a power law ∼tθ when k|hB| > O(1), where k is the average wavenumber for the initial spectrum. The RMI behavior in simulations and modeling agree over a wide range of exponent m, spectral width, initial amplitude, and time. The agreement is quantified objectively using statistical analysis.
The Richtmyer–Meshkov instability (RMI) is shock driven and affects many phenomena from inertial fusion to supernova explosions. The behavior of single-modes in the RMI has been studied extensively but less is known with the broadband perturbations that occur in applications. Here, we describe extensive numerical simulations and modeling of the RMI with broadband perturbations with an initial power spectrum of the form P∝ k m , where k is the wavenumber. The hydrodynamic condition is the same as in the $\theta$-Group Collaboration [Thornber et al., “Late-time growth rate, mixing, and anisotropy in the multimode narrowband Richtmyer–Meshkov instability: The $\theta$-group collaboration,” Phys. Fluids 29, 105107 (2017)] with a Mach ~1.86 shock and gamma-law = 5/3 fluids with Atwood number A = 0.5. The bubble amplitude h B is found to grow in two stages. Initially, h B undergoes a phase-inversion and grows linearly in time (t) at a rate consistent with a linear theory. Asymptotically, h B grows as a power law $~t^{\theta}$ when $\langle{k}\rangle{|h_B|}$ > O(1), where $\langle{k}\rangle$ is the average wavenumber for the initial spectrum. The RMI behavior in simulations and modeling agree over a wide range of exponent m, spectral width, initial amplitude, and time. The agreement is quantified objectively using statistical analysis.
The accurate modeling of high explosive (HE) detonation and the con- sequent large solid deformation, failure, plastic flow, porosity growth, and shock wave propagation is important because simulations can capture spatial and temporal features that experimental diagnostics cannot capture. However, the simulation of the explosive event poses challenges to a computational scientist. These include the accurate modeling of ductile damage, crack formation, plastic deformation, as well as physical and nu- merical instabilities. The material response can be history-dependent and subject to large material deformation. Our research simulates the impact of a high explosive (Detasheet) onto a tantulum metal plate. We performed the simulations using CartaBlanca at different mesh resolutions. We decided the study would be impactful if we perform the simulations with the Material Point Method. Differences were observed at the different mesh resolutions in velocity and nodal stress magnitude, so increased mesh resolutions may be required. In addition, the Discontinuous Galerkin method would be needed to account for the large gas expansion.