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The near-axis description of optimised stellarator fields has proven to be a powerful tool both for the design and understanding of this magnetic confinement concept. The description consists of an asymptotic model of the equilibrium in the distance from its centremost axis, and is thus only approximate. Any practical application therefore requires the eventual construction of a global equilibrium. This paper presents a novel way of constructing global equilibria using the DESC code that guarantees the correct asymptotic behaviour imposed by a given near-axis construction. The theoretical underpinnings of this construction are carefully presented, and benchmarking examples provided. This opens the door to an efficient coupling of the near-axis framework and that of global equilibria for future optimisation efforts.
We investigate the influence of shear-thinning and viscoelasticity on turbulent drag reduction in lubricated channel flow – a configuration where a thin lubricating layer of non-Newtonian fluid facilitates the transport of a primary Newtonian fluid. Direct numerical simulations are performed in a channel flow driven by a constant mean pressure gradient at a reference shear Reynolds number $\textit{Re}_\tau = 300$. The interface between the two fluid layers is characterised by Weber number $\textit{We} = 0.5$. The fluids are assumed to have matched densities. In addition to a single-phase reference case, we analyse four configurations: a Newtonian lubrication layer, a shear-thinning Carreau fluid layer, a shear-thinning and viscoelastic FENE-P fluid layer, and a purely viscoelastic FENE-CR fluid layer. Consistent with previous findings (Roccon et al. 2019, J. Fluid Mech., vol. 863, R1), surface tension is found to induce significant drag reduction across all cases. Surprisingly, variations in the lubricating layer viscosity do not yield noticeable drag-reducing effects: the Carreau fluid, despite its lower apparent viscosity, behaves similarly to the Newtonian case. In contrast, viscoelastic effects lead to a further reduction in drag, with both the FENE-P and FENE-CR fluids demonstrating enhanced drag-reducing capabilities.
We detail here a semi-analytical model for the pellet rocket effect, which describes the acceleration of pellets in a fusion plasma due to asymmetries in the heat flux reaching the pellet surface and the corresponding ablation rate. This effect was shown in experiments to significantly modify the pellet trajectory, and previously projected deceleration values of ${\sim} 10^6\,\textrm {m}\,\textrm{s}^{-2}$ for reactor-scale devices indicated that it may severely limit the effectiveness of pellet injection methods. We account for asymmetries stemming both from plasma parameter gradients and an asymmetric plasmoid shielding caused by the drift of the ionised pellet cloud. For high temperature, reactor relevant scenarios, we find a wide range of initial pellet sizes and speeds – particularly those relevant for large fragments of shattered pellet injection for disruption mitigation – where the rocket effect has a major impact on the penetration depth. In these cases, the plasma parameter profile variations dominate the rocket effect. We find that for small and fast pellets, where the rocket effect is less pronounced, plasmoid shielding-induced asymmetries dominate.
Previous literature has shown that the introduction of homogeneous perforation on plates and cylinders decreases aerodynamic drag. Here, it is shown that the opposite is true for a sphere; drag can increase with porosity. Hollow porous spheres exposed to a uniform free stream are studied experimentally using force and flow field measurements. The parameter space encompasses moderate to high Reynolds numbers ($5 \times 10^4 \leq \textit{Re} \leq 4 \times 10^5$) and porosities ranging from $0\,\%$ to $80\,\%$. The main conclusion is that drag increases with porosity, at super-critical Reynolds numbers, for all studied porosities. At low porosities (less than $9\,\%$), the effect of porosity on drag can be explained by shifts in the separation point. At higher porosities the drag increase cannot be explained by separation shifts, and instead is explained by two competing forms of kinetic energy dissipation: (i) shear on the macro-scale of the body, and (ii) hole losses from flow through the pores. The former generally decreases with porosity, as bleeding flow passing through the body decreases the characteristic velocity difference in the body-scale wake. In a sphere, hole losses increase with porosity sufficiently fast to overcome decreasing body-scale shear losses, in contrast to plates and cylinders where this is not the case. Relatively weak wake vortex structures, and associated low drag coefficient at zero porosity, for a sphere reduce the impact of wake bleeding. Moreover, fluid entering the fore of a sphere can exit perpendicular to the free stream, further reducing wake bleeding while still contributing to hole losses.
This work aims to complement the description of the atomisation process in a typical commercial pressure-swirl atomiser. Conventional characterisation focuses on the final spray, where established experimental techniques allow for measuring spherical droplets in a dilute regime. However, the early stages of atomisation involve distorted liquid structures with complex interface morphology that challenge both experimental and numerical approaches. While numerical simulations with interface-capturing methods have provided access to this region, they currently remain computationally prohibitive to follow the atomisation process until the formation of the final spherical droplets. To characterise the evolving interface morphology, we propose analysing the curvature distribution obtained from both simulations and two-photon laser-induced fluorescence (2P-LIF) imaging. This curvature-based methodology, recently developed to characterise numerical sprays (Palanti et al. Intl J. Multiphase Flow 147, 2022, 103879; Ferrando et al. Atomiz. Sprays 33, 2023, 1–28), is here extended to experimental data. Both approaches are compared with available phase Doppler anemometry (PDA) measurements performed further downstream on spherical droplets. The morphological evolution of the atomising spray is interpreted through curvature statistics, which provide a unified framework applicable to all atomisation stages. When applied to spherical droplets, the curvature distribution recovers the conventional drop size distribution, linking early interface deformation to the final spray structure. The birth of this final drop size distribution can thus be observed by comparing the three approaches – numerical simulation limited to the early stage of atomisation, curvature derived from 2P-LIF images limited to two-dimensional (2-D) contour analysis, and PDA measurements of the dilute spray. The results show that curvature properties evolve in a way that can be directly representative of the final spray even at early atomisation stages.
A lattice Boltzmann method is adopted to investigate the breakup of surfactant-free and surfactant-laden droplets in both regular and irregular T-junction microchannels. During droplet neck contraction, the neck thinning shifts from inertia dominated to interfacial tension dominated, causing spontaneous rapid neck collapse due to Rayleigh–Plateau instability. For the regular rectangular microchannels, we find that the prerequisite for the spontaneous breakup of a surfactant-free droplet is that the local capillary pressure in the triggering area exceeds the Laplace pressure difference between the inside and outside of the droplet neck. Results show that the critical neck thickness $\delta _\textit{cr}^{*}$ for the droplet spontaneous breakup increases with increasing height-to-width ratio $\chi$ of the microchannel in both surfactant-free and surfactant-laden systems. The presence of surfactants decreases $\delta _\textit{cr}^{*}$ at the identified $\chi$, while the surfactant effects are gradually enhanced as $\chi$ increases. Subsequently, a constriction section is incorporated into the upper microchannel wall to establish an irregular microchannel. As constriction depth (length) increases, $\delta _\textit{cr}^{*}$ linearly decreases (increases) in the surfactant-free system, while $\delta _\textit{cr}^{*}$ exponentially decreases (linearly increases) in the surfactant-laden system. Four empirical formulas are proposed to predict the values of $\delta _\textit{cr}^{*}$ under varying constriction depths and lengths in the two systems.
Reverberation mapping (RM) is a technique in which the mass of a Seyfert I galaxy’s central supermassive black hole is estimated, along with the system’s physical scale, from the timescale at which variations in brightness propagate through the galactic nucleus. This mapping allows for a long baseline of time measurements to extract spatial information beyond the angular resolution of our telescopes, and is the main means of constraining supermassive black hole masses at high redshift. The most recent generation of multi-year RM campaigns for large numbers of active galactic nuclei (AGN) (e.g. OzDES) have had to deal with persistent complications of identifying false positives, such as those arising from aliasing due to seasonal gaps in time-series data. We introduce LITMUS (Lag Inference Through the Mixed Use of Samplers), a modern lag recovery tool built on the ‘damped random walk’ model of quasar variability, built in the automatic differentiation framework jax. LITMUS is purpose-built to handle the multimodal aliasing of seasonal observation windows and provides Bayesian evidence integrals for model comparison and null hypothesis testing, a more quantified alternative to existing post-fit selection methods. LITMUS also offers a flexible and modular framework for using more expressive high-dimensional models for the AGN variability and includes jax-enabled implementations of other popular lag recovery methods like nested sampling and the interpolated cross-correlation function. We test LITMUS on a number of mock light curves modelled after the OzDES sample and find that it recovers their lags with high precision and successfully identifies spurious lag recoveries, reducing its false positive rate to drastically outperform the state-of-the art program JAVELIN. LITMUS’s high performance is accomplished by an algorithm for mapping the Bayesian posterior density which both constrains the lag and provides Bayesian evidences for model comparison and null hypothesis testing while outperforming nested sampling in computational cost by an order of magnitude.
This innovative textbook has been designed with approachability and engagement at its forefront, using language reminiscent of a live lecture and interspersing the main text with useful advice and expansions. Striking a balance between theoretical- and experimental-led approaches, this book immediately immerses the reader in charge and neutral currents, which are at the core of the Standard Model, before presenting the gauge field, allowing the introduction of Feynman diagram calculations at an early stage. This novel and effective approach gives readers a head start in understanding the Model's predictions, stoking interest early on. With in-chapter problem sessions which help readers to build their mastery of the subject, clarifying notes on equations, end of chapter exercises to consolidate learning, and marginal comments to guide readers through the complexities of the Standard Model, this is the ideal book for graduate students studying high energy physics.
The chapter begins with discussion of intelligence in simple unicellular organisms followed by that of animals with complex nervous systems. Surprisingly, even organisms that do not have a central brain can navigate their complex environments, forage, and learn. In organisms with central nervous system, neurons and synapses in the brain provide elementary basis of intelligence and memory. Neurons generate action potentials that represent information. Synapses hold memory and control the signal transmission between neurons. A key feature of biological neural circuits is plasticity, that is, their ability to modify the circuit properties based both on stimuli and time intervals between them. This represents one form of learning. The biological brain is not static but continuously evolves based on the experience. The field of AI seeks to learn from biological neural circuitry, emulate aspects of intelligence and learning and attempts to build physical devices and algorithms that can demonstrate features of animal intelligence. Neuromorphic computing therefore requires a paradigm shift in design of semiconductors as well as algorithm foundations that are not necessarily built for perfection, rather for learning.
This chapter provides, we believe, for the apogee of what we think will form the base for success of the quantum physics–like applications. Readers are invited in this chapter to carefully study the two-slit interference experiment with agents (and the agent two-preference interference) for a variety of real potential functions.
Resonance lines encode rich information about astrophysical sources and their environments, yet fully analytic treatments of multi-line radiative transfer remain almost entirely unexplored. We present exact, closed-form solutions for steady-state resonant-line radiativeP transfer in “V-shaped” atomic networks, where a single ground state couples to multiple transitions. Starting from the full angle-dependent transfer equation, we generalise absorption and emission coefficients to an arbitrary number of lines, derive a modified Fokker–Planck expansion of the frequency-redistribution integral, and use a judicious change of variables to reduce the problem to a Helmholtz equation with point-like sources in frequency space. This transformation admits analytic solutions for arbitrary sets of lines with fixed frequency offsets and strengths in both slab and spherical geometries. We implement V-shaped line networks in the colt Monte Carlo radiative transfer code and find excellent agreement with the analytic predictions across a wide range of line separations, optical depths, and damping parameters, establishing our solutions as stringent validation benchmarks. For concrete applications related to the Lyman-alpha (Ly$\alpha$) transition of neutral hydrogen, we examine how fine-structure splitting and deuterium injection modify the emergent spectra, internal radiation field, and radiative force multiplier. We show that these effects leave previous conclusions about Ly$\alpha$ feedback in the early universe essentially unchanged. Even when direct observational diagnostics are subtle, our framework provides novel analytic and numerical insights into coupled resonance-line transport and facilitates progress in general modelling of multi-line radiative transfer in diverse astrophysical settings.
The effects of confinement and polydispersity on the shear-induced diffusivity of non-Brownian, neutrally buoyant spheres suspended in a Newtonian fluid are investigated using simulations that incorporate short-range lubrication forces, surface roughness and frictional contacts. Simulations were performed at a fixed volume fraction of 0.45 for multiple values of particle roughness and friction coefficient. Confinement by bounding walls promoted layered structures that suppressed particle mobility and reduced diffusivity, while also diminishing the influence of friction and roughness. In contrast, high polydispersity disrupted layering and enhanced diffusivity, even in confined systems. Polydispersity also led to size-dependent demixing, with smaller particles preferentially migrating towards the walls and exhibiting higher mobility. These results have implications for modelling and controlling transport in suspensions, where confinement and polydispersity alter the effects of friction and roughness on shear-induced diffusion.
This chapter offers an in-depth discussion of various nanoelectronic and nanoionic synapses along with the operational mechanisms, capabilities and limitations, and directions for further advancements in this field. We begin with overarching mechanisms to design artificial synapses and learning characteristics for neuromorphic computing. Silicon-based synapses using digital CMOS platforms are described followed by emerging device technologies. Filamentary synapses that utilize nanoscale conducting pathways for forming and breaking current shunting routes within two-terminal devices are then discussed. This is followed by ferroelectric devices wherein polarization states of a switchable ferroelectric layer are responsible for synaptic plasticity and memory. Insulator–metal transition-based synapses are described wherein a sharp change in conductance of a layer due to external stimulus offers a route for compact synapse design. Organic materials, 2D van der Waals, and layered semiconductors are discussed. Ionic liquids and solid gate dielectrics for multistate memory and learning are presented. Photonic and spintronic synapses are then discussed in detail.
The classical problem of steady rarefied gas flow past an infinitely thin circular disk is revisited, with particular emphasis on the gas behaviour near the disk edge. The uniform flow is assumed to be perpendicular to the disk surface. An integral equation for the velocity distribution function, derived from the linearised Bhatnagar–Gross–Krook model of the Boltzmann equation and subject to diffuse reflection boundary conditions, is solved numerically. The numerical method fully accounts for the discontinuity in the velocity distribution function that arises due to the presence of the edge. It is found that a kinetic boundary layer forms near the disk edge, extending over several mean free paths, and that its magnitude scales as $\textit{Kn}^{1/2}$ as the Knudsen number $\textit{Kn}$ (defined with respect to the disk radius) tends to zero. A thermal polarisation effect, previously studied for spherical geometries, is also observed in the disk case, with a more pronounced manifestation near the edge that exhibits the same $\textit{Kn}^{1/2}$ scaling. The drag force acting on the disk is computed over a wide range of Knudsen numbers and shows good agreement with existing results for a hard-sphere gas and in the near-free-molecular regime.