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We numerically examine dynamo action generated by a flow of an electrically conducting fluid in a precessing cylindrical cavity. We compare a simplified kinematic approach based on the solution of the magnetic induction equation with a prescribed velocity field with the results from a self-consistent three-dimensional simulation of the complete set of magnetohydrodynamic equations. The different kinematic models show a strong influence of the electromagnetic properties of outer layers, which manifests itself in the existence of two different branches with dynamo action. In all cases, we observe a minimum for the onset of dynamo action in a transitional regime, within which the hydrodynamic flow undergoes a change from a large-scale to a more small-scale, turbulent behaviour. However, significant differences in the absolute values for the critical magnetic Reynolds number occur, and they can be explained by the low rotation rate in the MHD simulations achievable for technical reasons. In these models the character of the dynamo solution is small-scale and the magnetic energy remains significantly smaller than the kinetic energy of the flow. In irregular intervals, we observe dynamo bursts with a local concentration of the magnetic field, resulting in a global increase of the magnetic energy by a factor of 3 to 5. However, diffusion of the local patches caused by strong local shear is too rapid, causing these features to exist for only a short period (i.e., shorter than a rotation period) so that their dynamical impact on the dynamo remains small. As the magnetic field is small-scale and weak, the nonlinear feedback on the flow through the Lorentz force remains small and arises essentially in terms of a slight damping of the fast timescales, whereas there is no noticeable change in flow amplitude compared to the hydrodynamic case.

Space debris and inactive satellites pose a threat to the safety and integrity of operational spacecraft and motivate the need for space situational awareness techniques. These uncooperative targets create a challenging tracking and detection problem due to a lack of prior knowledge of their features, trajectories, or even existence. Recent advancements in computer vision models can be used to improve upon existing methods for tracking such uncooperative targets to make them more robust and reliable to the wide-ranging nature of the target. This paper introduces an autonomous detection model designed to identify and monitor these objects using learning and computer vision. The autonomous detection method aims to identify and accurately track the uncooperative targets in varied circumstances, including different camera spectral sensitivities, lighting, and backgrounds. Our method adapts to the relative distance between the observing spacecraft and the target, and different detection strategies are adjusted based on distance. At larger distances, we utilize You Only Look Once (YOLOv8), a multitask Convolutional Neural Network (CNN), for zero-shot and domain-specific single-shot real time detection of the target. At shorter distances, we use knowledge distillation to combine visual foundation models with a lightweight fast segmentation CNN (Fast-SCNN) to segment the spacecraft components with low storage requirements and fast inference times, and to enable weight updates from earth and possible onboard training. Lastly, we test our method on a custom dataset simulating the unique conditions encountered in space, as well as a publicly-available dataset., Comment: Small Satellite 2024 Conference, 13 pages, 8 figures, 6 tables

We study the phenomenon of information propagation on mobile geometric scale-free random graphs, where vertices instantaneously pass on information to all other vertices in the same connected component. The graphs we consider are constructed on a Poisson point process of intensity $\lambda>0$, and the vertices move over time as simple Brownian motions on either $\mathbb{R}^d$ or the $d$-dimensional torus of volume $n$, while edges are randomly drawn depending on the locations of the vertices, as well as their a priori assigned marks. This includes mobile versions of the age-dependent random connection model and the soft Boolean model. We show that in the ultrasmall regime of these random graphs, information is broadcast to all vertices on a torus of volume $n$ in poly-logarithmic time and that on $\mathbb{R}^d$, the information will reach the infinite component before time $t$ with stretched exponentially high probability, for any $\lambda>0$., Comment: 29 pages, 4 figures

We report a novel "cone-ray" model of background-oriented schlieren (BOS) imaging that accounts for depth-of-field effects. Reconstructions of the density field performed with this model are far more robust to the blur associated with a finite aperture than conventional reconstructions, which presume a "thin-ray" pinhole camera. Our model is characterized and validated using forward evaluations based on simulated and experimental BOS measurements of buoyancy-driven flow and hypersonic flow over a sphere. Moreover, we embed the model in a neural reconstruction algorithm, which is demonstrated with a total variation penalty as well as the compressible Euler equations. Our cone-ray technique dramatically improves the accuracy of BOS reconstructions: the shock interface is well-resolved in all our tests, irrespective of the camera's aperture setting, which spans f-numbers from 22 down to 4.

Recently, Josserand et al. proposed a stochastic nonlinear Schroedinger model for finite-time singularity-mediated turbulence [Phys. Rev. Fluids 5, 054607 (2020)]. Here, we use instanton calculus to quantify the effect of extreme fluctuations on the statistics of the energy dissipation rate. While the contribution of the instanton alone is insufficient, we obtain excellent agreement with direct simulations when including Gaussian fluctuations and the corresponding zero mode. Fluctuations are crucial to obtain the correct scaling when quasi-singular events govern the turbulence statistics., Comment: 5 pages, 3 figures, 5 pages appendix

Synthetic turbulence is a relevant tool to study complex astrophysical and space plasma environments inaccessible by direct simulation. However, conventional models lack intermittent coherent structures, which are essential in realistic turbulence. We present a novel method, featuring coherent structures, conditional structure function scaling and fieldline curvature statistics comparable to magnetohydrodynamic turbulence. Enhanced transport of charged particles is investigated as well. This method presents significant progress towards physically faithful synthetic turbulence., Comment: 7 pages, 5 figures. Accepted for publication in Europhysics Letters (EPL). Fixed small error in Fig. 5. The pseudocode listing is provided as an ancillary file

This text describes a method to simultaneously reconstruct flow states and determine particle properties from Lagrangian particle tracking (LPT) data. LPT is a popular measurement strategy for fluids in which particles in a flow are illuminated, imaged (typically with multiple cameras), localized in 3D, and then tracked across a series of frames. The resultant "tracks" are spatially sparse, and a reconstruction algorithm is commonly employed to determine dense Eulerian velocity and pressure fields that are consistent with the data as well as the equations governing fluid dynamics. Existing LPT reconstruction algorithms presume that the particles perfectly follow the flow, but this assumption breaks down for inertial particles, which can exhibit lag or ballistic motion and may impart significant momentum to the surrounding fluid. We report an LPT reconstruction strategy that incorporates the transport physics of both the carrier fluid and particle phases, which may be parameterized to account for unknown particle properties like size and density. Our method enables the reconstruction of unsteady flow states and determination of particle properties from LPT data and the coupled governing equations for both phases. We use a neural solver to represent flow states and data-constrained polynomials to represent the tracks (though we note that our technique is compatible with a variety of solvers). Numerical tests are performed to demonstrate the reconstruction of forced isotropic turbulence and a cone-cylinder shock structure from inertial tracks that exhibit significant lag, streamline crossing, and preferential sampling.

A large fraction of the baryons at low redshift are undetected, and likely reside in the tenuous, hot intergalactic medium (IGM). One way to probe the missing baryons is through their absorption of bright sources. The anomalous absorption excess in the X-ray afterglows of $\gamma$-ray bursts (GRBs) has been suggested to result from the missing baryons. In order to test this hypothesis, the present paper employs IllustrisTNG simulations to compute the X-ray absorption effect on cosmological distances. The simulation shows that ionization of H and He in the IGM leaves the metals responsible for $>60\%$ of the X-ray opacity of high-$z$ sources (the ionization of He isn't available in the simulation, we used here external knowledge). The high-$z$ asymptotic optical depth at 0.5\,keV in the simulation reaches $0.15\pm0.07$, while the GRB afterglow values tend to $\approx 0.4$, implying the missing baryons can account for a significant fraction of the observed opacity. The remaining discrepancy is ascribed mainly to the low average metallicity in the simulation, which drops from 0.06 solar at $z=0$ to 0.01 at $z=3$, and which is below previously measured values., Comment: Comments are welcome

Collsionless astrophysical and space plasmas cover regions that typically display a separation of scales that exceeds any code's capabilities. To help address this problem, the muphyII code utilizes a hierarchy of models with different inherent scales, unified in an adaptive framework that allows stand-alone use of models as well as a model-based dynamic and adaptive domain decomposition. This requires ensuring excellent conservation properties, careful treatment of inner-domain model boundaries for model coupling, and robust time stepping algorithms, especially with the use of electron subcycling. This multi-physics approach is implemented in the muphyII code, tested on different scenarios of space plasma reconnection and evaluated against space probe data and higher-fidelity simulation results from literature. Adaptive model refinement is highlighted in particular, and a hybrid model with kinetic ions, pressure-tensor fluid electrons, and Maxwell fields is appraised.