Papers

The presence of sidewalls in turbulent duct flows leads to the emergence of secondary flow structures in the form of counter-rotating streamwise vortices near the corners (Prandtl's secondary flow of the second kind). This work develops reduced-order Galerkin projection models that can predict the emergence and structure of these turbulent mean secondary flows in a square duct geometry, without requiring any prior knowledge of the turbulent statistics. The models are obtained by projecting the Navier–Stokes equations onto eigenmodes of the linearised system, with the resulting systems of ordinary differential equations simulated with the addition of zero-mean forcing. We show that most models obtained using leading streamwise-constant eigenmodes predict the correct shape and direction of the secondary mean, with the minimal such model requiring only two modes. In these models, the secondary mean contribution arises due to the nonzero average coefficient of an eigenmode that possesses all of the symmetry properties expected of a secondary mean. The models are sufficiently simple such that the relationship between this mean coefficient and the joint second moment of other mode coefficients can be computed analytically. We confirm that running streamwise-constant direct numerical simulations (DNS) with the same forcing structure as used in the reduced-order models produces similar secondary-flow structures. We additionally demonstrate that our models produce qualitatively similar Reynolds stress distributions to fully resolved direct numerical simulations, with improved agreement as model dimension increases.

Modern transmission electron microscopes are versatile instruments which have become indispensable tools for understanding structure and chemical composition at the nano- and atomic scale. In the physical sciences these instruments are still largely manually controlled, requiring significant operator expertise, limiting throughput, and precluding statistical analysis of large datasets. Recent technical advances in both hardware and in control software now allow for the interaction with almost every functionality of the microscope through a programming interface. This enables better experimental design and data collection automation while also reducing operator collection bias and required expertise. In this study, we present an automated data collection routine with machine-driven decision-making to enable the collection of hundreds of 4D-STEM nanobeam diffraction and ptychography data from a large distribution of size-selectively deposited Pt nanoparticles. We present a semi-automated data analysis workflow to extract pertinent information from the large volumes of collected data. For the nanobeam diffraction data, reducing each dataset to its azimuthal variance profile and combining automated crystal orientation mapping with per-particle morphology descriptors reveals the orientation, shape and phase distributions across the ensemble, including a weak {110} texture. For the ptychographic data, an automated screening pipeline identifies on-zone-axis particles and enables atomic-resolution phase imaging and lattice-strain mapping of individual grains. Together these demonstrate how automation turns instrument throughput into statistically meaningful, atomic-scale microstructural information.

Multiband Fano resonances are demonstrated in the near-infrared (near-IR) using an all-dielectric metasurface whose unit cell consists of four silicon nanoblocks on a glass substrate. An in-plane asymmetry triggers symmetry-protected quasi-bound states in the continuum (QBICs), producing multiple high-Q resonances. Their origin is identified through multipolar decomposition of the scattering cross section and field distributions at the resonances. The strong field localization at these resonances enables efficient multiband third-harmonic (TH) generation in the ultraviolet (UV), with a maximum simulated conversion efficiency of $8.5 \times 10^{-3}$ at a peak pump intensity of $1.6\,\mathrm{GW/cm^{2}}$. The metasurface is fabricated in symmetric and asymmetric configurations, and its linear and nonlinear responses are measured under normal incidence. A TH conversion efficiency of $1.2 \times 10^{-6}$ is obtained at a peak pump intensity of $3.25\,\mathrm{GW/cm^{2}}$. These results establish a route to multiband photonic devices, including multiwavelength lasers, multiband harmonic generation, and single-photon sources for quantum photonics.

Maxwell's wave equation in the presence of a cubic nonlinearity and a periodically time-varying refractive index (a photonic time crystal) is reduced, for spatially monochromatic waves, to a nonlinear Mathieu equation. Near the principal momentum gap this equation admits an autonomous two-dimensional reduction whose complete Hamiltonian phase portrait can be obtained analytically. We derive the two homoclinic separatrices corresponding to temporally localised momentum gap solitons, identify the nonlinear centres and the critical Hamiltonian value $H_c$, and calculate the point of maximum linear parametric gain. We then consider spatially localised pulses and show how the nucleation of multiple spatiotemporal gap solitons can produce a broad supercontinuum in momentum space; for stronger seeds, transient extreme nonlinear localisation can accompany an abrupt additional broadening of this momentum spectrum. These results establish a direct connection between Floquet amplification, nonlinear saturation, homoclinic dynamics, and momentum space spectral broadening in nonlinear photonic time crystals.

This paper describes the development of the RIOT 2, an observational tool that can be used to study classrooms in which there are multiple members of an instructional team. The RIOT 2 is based on the Real-time Instructor Observational Tool (RIOT), which characterizes the behavior of an instructor over time. While the RIOT was previously developed for classrooms with only one instructor, our team has developed a version to observe classrooms supported by Learning Assistants (LAs). LA are undergraduate peer educators who facilitate discussions in active-learning classrooms. Unlike the RIOT, the RIOT 2 allows for the categorization of LA-instructor interactions. In this paper, we describe the development of RIOT 2 and present some initial data collected using the RIOT 2. While our data are preliminary, they illustrate how the RIOT 2 may be useful for future research and practice. This work is part of a broader research effort to understand how LA-faculty partnerships impact what LAs and instructors do in the classroom.

The dependence of ion pairing on osmotic stress may strongly affect the performance of ionic materials and membranes whose interior is often osmotically stresses, yet quantitative understanding of this dependence and, specifically, the effects of water and ion activities is limited. Motivated by this gap, we analyze the enhancement of ion pairing with osmotic pressure for concentrated aqueous KCl, NaCl, and LiCl solutions using molecular dynamics simulations. Based on rigorous thermodynamic relations, we separate the contributions of ion non-ideality to the pairing constant, varying with osmotic pressure, from other effects including water release and type of ion-pair. Our analysis reveals that ion non-ideality indirectly generates a stronger effect on pairing than the direct one of water release. However, its effect is moderated and may even be reversed for more hydrated pairs by a similarly large and opposite effect of ion-pair non-ideality assigned to varying dielectric properties of the solution and water restructuring upon pairing. The interplay between these contributions, including large and pair type-specific hydration effects on the cost of pairing, explains the observed opposing trends: pairing decreases with osmotic pressure for more hydrated solvent-separated pair types while increasing for contact pairs. The trend becomes more pronounced for more hydrated smaller cations, but was fairly independent of the water model used. The results further suggest that dielectric effects enhanced in ionic materials-and, as a result, larger variations of ion-pair non-ideality, compared with aqueous solutions, should have a more significant impact on pairing than water release.

Accurate Monte Carlo simulation of plastic scintillator detectors requires precise wavelength-dependent optical properties. We present measurements of wavelength-dependent refractive index $n(\lambda)$ and bulk absorption length $L_{\mathrm{abs}}(\lambda)$ for six commercial polyvinyltoluene (PVT)-based scintillators (Saint Gobain BC-400, BC-404, BC-408, BC-418, BC-422, and BC-430) at five wavelengths between 372 and 660 nm. Using a precision refraction apparatus and variable-thickness transmission measurements, we characterize material-specific dispersion that deviates substantially from both the manufacturer specification and literature values for pure PVT. Illustrative Geant4 simulations of a BC-422 detector demonstrate a 7.2% reduction in photocathode energy-deposition threshold events when using our measured $n(\lambda)$ compared to the constant manufacturer value, highlighting the practical importance of accurate optical property data for detector design and calibration. We successfully measure absorption lengths for BC-430 at four wavelengths between 372 and 505 nm (2.2-12.4 mm) and for all materials at 372 nm (2.2-38 mm), while establishing lower bounds exceeding 750 mm for weakly absorbing formulations at longer wavelengths.

We present high-precision calculations of the hyperfine constants, isotope shift, and isomer shift in low-lying states of Th3+ relevant to spectroscopy of the 229Th nuclear isomer. The removal energy and hyperfine constants are calculated using a relativistic coupled-cluster method including single, double, and triple excitations, demonstrating the importance of higher-order correlation effects. The isotope shift of the 5f_5/2 -7s transition is evaluated using both the CI+MBPT and CI+all-order methods, providing an estimate of the theoretical uncertainty. We find that the isotope shift is dominated by the field shift, with the mass shift contributing negligibly. Combining the calculated field-shift coefficient with the measured difference in the nuclear mean-square charge radii of the ground and isomeric states, we predict the 7s isomer shift to be (Delta E^(m,g))= 0.9(1) GHz. These results provide benchmark atomic data for Th3+ and support ongoing precision spectroscopy of 229Th and the development of a nuclear clock.

Secondary-electron (SE) emission in the helium ion microscope (HIM) becomes sensitive to membrane thickness when the sample is thin enough for He-ion transmission and when the SEs emitted from the bottom surface are collected. We correlated the total SE intensity of suspended, nanometer-thick MoS$_2$ flakes on lacey carbon with thickness measured independently by electron energy-loss spectroscopy. The response peaks at 40-55 nm, with an apparent back-to-front SE signal ratio reaching 4.7. The peaked, thickness-dependent component is attributed primarily to SE emission at the bottom surface of the flake, rather than to transmitted ions striking instrument surfaces. Applying SRIM ionization profiles, an asymmetric SE-escape model with a longer escape depth on the exit side reproduces the response. We find an effective exit-side escape depth of approximately 10 nm, five times the assumed 2 nm entrance value, suggesting that deposited energy reaches the exit surface far more efficiently than the entrance surface or that the SRIM model's energy deposition profile is shifted by an effect such as channeling. The correlation provides a rapid thickness screen for suspended membranes and a route to testing low-energy ion-solid interaction models in thin materials.

We extend our recently developed semiclassical strong-field Herman-Kluk (SFHK) propagator method to calculate high-order harmonic generation (HHG) in diatomic molecules driven by few-cycle intense laser fields. On the example of applications to H2 and N2, we show that our method, based on a combination of the Herman-Kluk propagator and the strong-field approximation, can provide very accurate results for both HHG yield and phase, nearly identical to those from the exact numerical solutions of the time-dependent Schrodinger equation. To compare with experimental measurements, averaging over molecular orientations must be performed. Here we demonstrate a distinct and powerful advantage of the SFHK, as its Monte Carlo sampling for the integration over the alignment distribution can be efficiently combined with the integration over the initial momentum distributions of electron wave-packet right after the tunnel exit. Therefore, the total number of trajectories used for the alignment-averaged HHG spectrum does not increase much compared to that for a single fixed alignment. Similar to atomic targets, the main computational task in the SFHK is to solve the classical Hamiltonian equations for the active electron in the combined electron-target ion potential and electron-laser interaction. The motion of the center of each electron wave packet in the continuum, represented by a coherent state, is governed by an independent classical trajectory so that the computation can be parallelized very efficiently.

Nonlinear space-charge effects play a critical role in the dynamics of high-intensity, high-brightness beams. While conventional space-charge forces scale inversely with the square of the relativistic Lorentz factor ($\gamma$) as beam energy increases, this suppression may not apply to all components of the electromagnetic force. In this paper, we present a generalized space-charge solver that accounts for contributions from transverse currents in three-dimensional (3D) converging or diverging beams. We demonstrate that these additional forces from transverse currents lack an explicit dependence on $\gamma$, potentially rendering them significant in specific high-energy applications. We provide a semi-analytical solution for a 3D converging or diverging beam with the Gaussian density distribution and identify the regimes in which these transverse current forces become comparable to conventional space-charge forces. Furthermore, we present a numerical solution for the generalized model applicable to arbitrary density distributions. Finally, we evaluate the impact of these forces on strongly converging electron beams within the interaction regions of the FCC-ee and ILC, as well as the second bunch compressor of LCLS-II-HE; our results indicate that these extra forces do not significantly alter the final electron beam quality in these specific cases.

We have developed a dual-species optical tweezer array apparatus combining $^{87}\text{Rb}$ and $^{174}\text{Yb}$ atoms with a permanent hybrid Twyman-Green–Fizeau interferometer, which provides precision co-alignment of two opposing 0.6-numerical aperture (NA) objectives and the high NA beams that pass through the system. This technique is extensible to other tweezer platforms operating at high numerical aperture across widely-separated wavelengths. Here we present simultaneous trapping and single-site resolved imaging of both species in co-aligned tweezer arrays with $^{87}\text{Rb}$ confined at $840\ \rm{nm}$ and $^{174}\text{Yb}$ at $532\ \rm{nm}$. The platform provides a foundation for hybrid quantum register operation, including mid-circuit measurements and asymmetric intra- and inter-species interactions, greatly expanding the capabilities of neutral atom arrays.

Bipartite qubit entanglement distillation is the process of converting noisy ebits into pure ebits using only local operations and classical communication. This is a core operation for quantum repeaters, enabling such crucial tasks as long-distance quantum communication and distributed quantum computing. In this work, we introduce a method for searching for entanglement distillation protocols and, using this technique, distil qubit Werner states at a higher rate than could be achieved using previously discovered protocols. In particular, we demonstrate the advantage of our new distillation strategy by improving the best-known lower bound for the two-way-assisted quantum capacity of the qubit depolarising channel across a wide range of channel parameters, making progress in one of the long-standing problems of quantum information theory.

We consider a new methodology for modelling the reaction-diffusion systems based on systems of ordinary differential equations. In contrary to the specialised numerical methods like straight line method, this new methodology is positioned as a pure alternative at the model level for partial differential equations. In its description, the new method is largely similar to the finite volume method, but unlike the latter, it uses statistical simplifications and principles of geometric probability to describe diffusion. The main objectives of this approach are to simplify the qualitative analysis of reaction-diffusion systems and to improve the efficiency of numerical model implementation. The first objective is successfully addressed, as it becomes possible to use the apparatus of classical dynamical systems theory for a qualitative analysis of model dynamics based on the systems of ordinary differential equations. The second objective is only partially addressed, as the gain in efficiency while maintaining acceptable accuracy for numerical implementation will be significant only for certain simple initial distribution of molecules and for specific diffusion coefficients. Furthermore, to formulate criteria for practical applicability, we separately evaluate the modelling error using this new methodology.

This year marks 360 years since the founding of the French Academy of Sciences. This is the story of the academy's daring 17th century expedition to measure the Earth-sun distance. A truncated version first appeared in National Geographic Magazine; the following is closer to the original draft, with updated information and references.

Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the question not just of whether they fit, but of whether they are wrong in ways we did not anticipate: the unknown unknowns. This challenge of model misspecification is not unique to machine learning. In physics, misspecification is sometimes exactly what we want to find: new discoveries appear as failures of existing models. At other times, we want such effects absorbed into the analysis without biasing the measurement. A robust analysis is one that absorbs the misspecifications we are not interested in, while preserving sensitivity to the ones we are. Machine learning can both amplify misspecification and provide new tools to address it. We discuss the challenges of model misspecification, diagnostics for detecting it, and strategies for mitigation. No single diagnostic can confirm that a model is correctly specified: detection and mitigation are two halves of an iterative loop, in which a battery of complementary diagnostics is applied, the model is updated, and the process repeated. Robustness against unknown unknowns is ultimately less about any single technique than about a disposition: a willingness to suspect one's own model, and to design analyses that can survive being wrong in ways one did not anticipate.

Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or unfavorable evolution in Abdominal Aortic Aneurysms (AAA). While Physics-Informed Deep Operator Networks (PI-DeepONets) show promising results in complementing established 5 tools such as Computational Fluid Dynamics (CFD), a persistent architectural challenge remains for complex 3D flows. In this direction, we propose a Modified Multi-Input Multi-Output PI-DeepONets (M3PI-DeepONet) designed for predicting unsteady flows in an idealized AAA geometry. Central to our model is the Aggregated Injection strategy, where latent representations from multiple input branches are fused prior to trunk injection, allowing the coordinate basis to adapt to multiple physical constraints. To the best of our knowledge, this is the first architecture to combine the layer-wise gating mechanism with a multi-branch operator-network topology, yielding an input-adaptive trunk basis. Additionally, we integrate the 3D Navier-Stokes equations as governing physical laws, so the model is trained based on physics-informed residuals, initial and boundary conditions, and only 0.3% of the labeled internal data together with the selected branch-conditioning signals. The M3PI-DeepONet simultaneously predicts unsteady 3D flow velocity and pressure fields with an average relative L2 velocity error below 4% and pressure error around 5% while achieving a conservative retained-cycle inference speedup of approximately 36x compared to reference CFD simulations once the branch inputs used for conditioning are available. This work advances the application of deep learning in cardiovascular disease modeling, marking step toward real-time, non-invasive clinical diagnostics.

The orbital angular momentum (OAM) of light is a discrete, unbounded degree of freedom that underpins mode-multiplexed communications and high-dimensional quantum photonics. Yet, dynamic OAM control remains dependent on bulky free-space optics or cascaded architectures that separate switching from wavefront shaping, hindering nanoscale integration. Here, we engineer artificial van der Waals crystals from rhombohedrally stacked (3R) MoS$_2$, in which spatial control of the local crystal orientation imprints a nonlinear geometric phase onto the second-harmonic (SH) field, enabling background-free generation of SH vortex beams in an ultrathin (46 nm) van der Waals platform. Leveraging the C$_{3v}$ symmetry of 3R-MoS$_2$, we demonstrate monolithic, all-optical switching with sub-optical-cycle precision between Hermite-Gauss-like and Laguerre-Gaussian vortex SH beams with opposite topological charges ($l=\pm1$). Our results establish artificial 3R-MoS$_2$ crystals as a monolithic platform for the generation and all-optical reconfiguration of nonlinear structured light at the nanoscale, advancing active nanophotonic sources for integrated classical and quantum photonic technologies.

We report simulations of the dissolution and diffusion of Nb's surface oxide layer in vacuum. While this chemical doping process is important for the surface preparation of Nb superconducting radio frequency (SRF) cavities - common components of particle accelerators - quantitatively linking the resulting oxygen distributions to superconducting performance remains challenging. In this work, we simulate the reaction-diffusion process numerically for treatment temperatures $T = 50^{\circ}$C to $200^{\circ}$C and times $t = 0.5$ h to $120$ h, and calculate the effect of the spatially inhomogeneous oxygen doping on Nb's superconducting properties. We find that oxygen doping redistributes the Meissner screening current, reducing its value at the surface and shifting its maximum several nanometres into the material. These results provide a microscopic link between oxygen diffusion profiles and the electromagnetic response of Nb relevant for SRF cavity operation. This work provides a quantitative framework linking oxygen diffusion profiles to superconducting performance and establishes a foundation for future studies involving time-dependent and multi-step heat treatment protocols.

Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate atomistic simulation. Classical density functional theory offers a reusable variational description, but its central excess free-energy functional is generally unknown, and learned approximations have largely remained restricted to planar or lower-dimensional settings. Here we show that this functional can be learned directly from fully three-dimensional equilibrium density fields while preserving spatial symmetry and variational consistency, without free-energy or chemical-potential labels. A single learned functional transfers across temperatures, system sizes and statistical ensembles, and recovers structure factors, the equation of state, liquid–vapor coexistence and interfacial broadening, none of which are used as training targets. Applied to complex three-dimensional geometries, it predicts the non-monotonic force associated with formation and rupture of a solvent-depleted bridge between colloids and adsorption in an interconnected gyroid pore. These results demonstrate that equilibrium density data can be converted into a transferable thermodynamic generator connecting microscopic liquid structure to response, phase behavior and collective phenomena.

Complex multipath environments are usually avoided in wave-based information processing because repeated scattering creates many interfering propagation paths, obscuring controllability and generating extreme sensitivity to perturbations. The addition of nonlinear mechanisms fundamentally alters the wave-control landscape by breaking the superposition principle that underpins most wave-management strategies. Here, we show that these two apparent impediments – multipath complexity and nonlinearity – can instead be harnessed as key resources for physical optimization. We demonstrate an in-situ adjoint optimization protocol in a wave-chaotic platform incorporating a single localized nonlinear defect, in which the system itself performs both the forward and the adjoint propagations required for gradient evaluation. Recurrent multipath returns repeatedly expose the wave to the defect, producing from a minimal hardware a rich nonlinear input-output map with many pathway-mediated degrees of freedom. At the same time, a suitable adjoint excitation enables direct extraction of the sensitivities from measurements alone, without a digital twin or conventional numerical backpropagation. We experimentally validate the protocol on a minimal nonlinear multipath platform composed of incommensurate coaxial cables connected via T-junctions, one of which hosts a diode-loaded cavity. Our approach opens a route to adaptive wireless communications, imaging and analog intelligence in complex, partially unknown environments where conventional modeling is impractical.

AI weather emulators benefit from clear objectives and metrics, which have led to the rapid development of models that outperform traditional benchmarks. In contrast, long-term climate emulators must reliably reproduce forced responses over months to centuries, while relying on training objectives that span a small number of model time steps. We assess autoregressive, full-depth ocean emulators using data from the midHolocene experiment of a numerical climate model to examine their skill in responding to surface forcings from an in-distribution, out-of-sample climate. We demonstrate that these emulators generalize to new orbital forcings, reproducing the spatial structure of the large-scale response as well as changes in seasonal patterns and in the spatial structure of ocean variability, while underestimating their amplitude. Baselines that infer the ocean state directly from the boundary forcings also recover much of the large-scale pattern, but only near the surface, and capture neither the seasonal nor the variability changes, indicating that these require some representation of dynamics. Despite these successes, the emulators fail to reproduce the slow, internally driven evolution of the ocean interior. We then show that the emulators' total forced response is well reconstructed by linearly composing their independent responses to each forcing component. Tracking response across training epochs, we find that convergence on mean state metrics in the training climate does not guarantee that the emulators capture the dynamics necessary for a skillful response. Together, these experiments establish the midHolocene as a controlled, ground-truthed setting for diagnosing forced-response failures before emulators are pushed to out-of-distribution climates.

Deep learning surrogates for RANS flow prediction around airfoils face two persistent bottlenecks. A single neural architecture cannot simultaneously resolve sharp near-wall boundary layers and smooth far-field potential flow. Additionally, force prediction is undermined by the numerical instability of computing wall-normal velocity gradients from continuous-field approximations. Both of these points are addressed with a DD-RNO (domain-decomposed routed neural operator), combining a spectral geometry encoder with two physics-guided innovations: (a) a differentiable domain routing mechanism that partitions the flow field into inviscid, boundary-layer, and wake regimes—dispatching query points to specialized regional decoders, and (b) learned canonical quadrature (LCQ), which replaces unstable pressure integration with flow-conditioned, learned integration weights that predict lift and drag directly from surface pressure. On the AirfRANS benchmark, DD-RNO cuts velocity field mean-square error (MSE) by 17$x$ ($u_x$) and 12$x$ ($u_y$) over the strongest baseline, widening to 23$x$ under out-of-distribution Reynolds extrapolation—evidence that the routing mechanism generalizes with the physics it encodes rather than merely fitting the training distribution. LCQ reduces drag MSE by 7.5$x$ relative to conventional pressure integration and raises drag rank correlation from $\rho = 0.250$ to $\rho = 0.997$. Ablations confirm that both components are indispensable to performance: removing domain routing increases velocity error by 8.2$x$, and removing LCQ increases relative drag error more than 40-fold. At ~144 ms per sample—a 10,000$x$ speedup over conventional RANS solvers—DD-RNO offers a surrogate accurate and fast enough for real-time aerodynamic design and optimization loops.

The macroscopic ideal-MHD stability of an axisymmetric mirror device with sonic levels of plasma rotation is analyzed by approximating the plasma equilibrium as a rotating isothermal theta pinch possessing an artificial gravity. Starting from a set of single-fluid, ideal-MHD, isothermal fluid equations, a linear, second-order, eigenmode equation for the radial component of the Lagrangian fluid displacement is derived which governs the stability of the equilibrium to small perturbations in the case of an arbitrary plasma angular velocity profile. The stability of the m=1 and m=2 modes is investigated. For the case of rigid plasma rotation, the plasma is found to be stable to these two modes provided that it is sufficiently short in the axial direction. For the case of sheared plasma rotation, the $m=2$ mode is found to be difficult to stabilize. This is the case for both monotonically decreasing rotation profiles, and profiles that peak off the magnetic axis.

We report the formation of an original dome-shaped air-water cavity at the impact site of closely-packed plunging water jets. The injector assembly consists of concentric rings of circular jets, similar to a shower-head configuration. We infer from high-speed imaging, LASER-induced fluorescence, and optical phase detection probes that the cavity consists of multiple stems and sheets of air that stretch, retract and pinch off to produce bubbles. We also illustrate various bubble production mechanisms in the cavity, along with coalescence and bubble breakup scenarios in the subsequent bubble cloud. Thereby, we describe the wide range of bubble sizes generated by such jets, from a few micrometers up to about $5$~mm. Measurements of the cavity size are carried out for varying impact velocity, number of jets, and jet spacing. We propose that this distinctive air-water cavity is generated by liquid entrainment when successive rings of plunging jets progressively mix with the pool water.

A lattice Boltzmann scheme for the three-dimensional compressible Navier–Stokes–Fourier equations, derived automatically from the declared system by a symbolic compiler, is validated against exact solutions and published reference data. The declared system carries the viscous stress and the heat flux as transported state, and is discretized on a D3Q7 lattice in single precision. Against the exact Sod and Becker solutions the captured shock thickness converges at first order. On the supersonic Taylor-Green vortex at $M_0 = 1.25$ the scheme at $512^3$ matches the reference dilatational dissipation more closely than seven compared solvers, by thirty percent over the next best. Every operator in this solver, the generated collision and constitutive closure and the added shock sensor alike, reads only the cell it acts on, and data reaches a neighbor only by streaming along the lattice characteristics.

Metallic nanostructures confine electromagnetic fields at subwavelength scales, making them attractive as plasmonic nanoantennas. At these scales, the response of metals becomes nonlocal, and the hydrodynamic model is widely used to capture this response. However, existing solvers provide only the response to a prescribed excitation and do not directly reveal the intrinsic resonances of the structure. This work extends the characteristic mode analysis to plasmonic nanostructures to enable excitation-independent modal analysis of their resonant behavior. For simple metals, the coupled hydrodynamic and volume integral equations are reduced to a single hydrodynamic volume integral equation in terms of the induced current. The equation is discretized and cast as a generalized eigenvalue problem within the characteristic mode analysis framework, whose solution yields the characteristic mode currents and modal significance curves of the structure. The proposed framework is validated through three metallic nanostructures: a nanosphere, a nanorod, and a nanodimer. The results show that the method identifies the intrinsic resonances of each structure, including resonances not excited by a given source and additional resonances arising from the nonlocal response, which are absent in local models. The proposed framework provides physical insight into the modal mechanisms of plasmonic nanostructures and serves as a practical tool for their analysis and design.

Type Ia supernovae (SNe Ia) explosively destroy carbon-oxygen white dwarfs (WDs) in multiple stellar systems. They produce approximately 50% of the iron-group elements in the Universe, synthesize electron-capture (EC) elements, drive nuclear physics experiments, and underpin high-precision cosmology. To first order, the outcome is governed by nuclear physics, a property often described as stellar amnesia. Recently, this stellar amnesia has begun to be broken by the nearly universal detection of EC elements with JWST. These elements trace high-density burning, largely ruling out the currently popular helium-triggered, sub-Mch detonation models as the dominant channel. Instead, the ubiquitous presence of EC is shifting back the focus to dynamical and secular mergers, and near-Mch explosions similar to the deflagration model W7, but in which the nuclear flame undergoes a deflagration-to-detonation transition. The early deflagration phase is especially important because spherical simulations identify the central WD density, and thus the WD mass, as a key parameter governing the explosion. Here, we present detailed magneto-hydrodynamical simulations. We find that small-scale, pre-existing turbulence expected from the pre-explosion smoldering phase is essential for overcoming the fundamental challenges imposed by the intrinsic 3D physics. This turbulence systematically reduces the production of EC elements by about a factor of two, implying the need for WD central densities closer to those associated with accretion-induced collapse to a neutron star. We also demonstrate the effect of magnetic fields near the saturation field strength and highlight the need for higher-precision EC rates at low Ye.

Bond-dependent magnetic interactions, exemplified by the Kitaev model, are known to arise from the interplay between spin-orbit coupling (SOC) and specific coordination geometries, but have so far been almost exclusively identified in edge-sharing octahedral systems. Whether such interactions persist in edge-sharing tetrahedral environments, characteristic of the parent compounds of iron-based superconductors, remains an open question. Here, we construct a Kitaev-like model for monolayer FeTe and FeSe and demonstrate the presence of a previously unrecognized bond-dependent Ising-type interaction, induced jointly by chalcogen-mediated SOC and the tetrahedral crystal-field geometry. A microscopic spin model for these bond-dependent interactions is derived via strong-coupling perturbation theory, and the strengths of the individual exchange terms are extracted by partitioning the magnetic anisotropy energy calculated using density functional theory across various collinear magnetic orders. We reveal that the Kitaev-like interaction dominates the magnetic anisotropy in FeTe, whereas in FeSe, it strongly competes with a single-ion anisotropy of opposite sign. The resulting noncollinear local anisotropy axes generate intrinsic single-site spin frustration, providing a microscopic mechanism for magnetic disorder that transcends isotropic exchange models. Our results establish edge-sharing tetrahedral magnets as a new platform for bond-dependent interactions and extend the scope of Kitaev physics beyond octahedral coordination.

Aerodynamic unsteadiness is inherent to the operation of many engineering applications, especially those that involve large-scale rotating blades, such as modern wind turbines. Over the past few decades, wind turbine rotors have grown rapidly and are now exceeding 200 meters in diameter, causing them to operate in conditions where limited empirical data are available, and where models have not been validated. Here, we use a highly pressurized wind tunnel to probe these conditions and evaluate the quasi-steady approximation, often used to simplify the modeling of the unsteady aerodynamic response of an airfoil to slow changes in inflow conditions. We find that the quasi-steady approximation is not valid for a large range of frequencies where it is normally applied. This finding suggests that the loading on wind turbine blades will be significantly underestimated when using conventional models, especially near stalling conditions, which modern wind turbines typically encounter.