We present a model-independent way to characterise properties of the magnetic-field turbulence in the emitting regions of Gamma-Ray Burst afterglows. Our only assumption is that afterglows’ synchrotron radiation is efficient. It turns out that the gyroradius of plasma particles must be smaller (with a good margin) than the correlation length of the magnetic-field fluctuations. Such turbulence is essentially non-linear and therefore must be produced by some kind of magnetohydrodynamical instability, likely acting on top of kinetic Weibel instability. We also find that the emitting particles are loosely confined to local magnetic-field structures and diffusion allows them to sample the entire distribution of local magnetisation values. This means that one-zone approach to modelling the afterglow spectra is still valid despite the non-linear nature of the magnetic turbulence. However, the non-linear turbulence may (and likely will) change the synchrotron spectrum of individual electrons.

Indoor ventilation is underutilized for the control of exposure to infectious pathogens. Occupancy restrictions during the pandemic showed the acute need to control detailed airflow patterns, particularly in heavily occupied spaces, such as lecture halls or offices, and not just to focus on air changes. Displacement ventilation is increasingly considered a viable energy efficient approach. However, control of airflow patterns from displacement ventilation requires us to understand them first. The challenge in doing so is that, on the one hand, detailed numerical simulations – such as direct numerical simulations (DNSs) – enable the most accurate assessment of the flow, but they are computationally prohibitively costly, thus impractical. On the other hand, large eddy simulations (LES) use parametrizations instead of explicitly capturing small-scale flow processes critical to capturing the inhomogeneous mixing and fluid–boundary interactions. Moreover, their use for generalizable insights requires extensive validation against experiments or already validated gold-standard DNSs. In this study, we start to address this challenge by employing efficient monotonically integrated LES (MILES) to simulate airflows in large-scale geometries and benchmark against relevant gold-standard DNSs. We discuss the validity and limitations of MILES. Via its application to a lecture hall, we showcase its emerging potential as an assessment tool for indoor air mixing heterogeneity.

The theoretical investigation of relevant turbulent transport mechanisms in H-mode pedestals is a great scientific and numerical challenge. In this study, we address this challenge by global, nonlinear gyrokinetic simulations of a full pedestal up to the separatrix, supported by a detailed characterisation of gyrokinetic instabilities from just inside the pedestal top to the pedestal centre and foot. We present ASDEX Upgrade pedestal simulations using an upgraded version of the gyrokinetic, Eulerian, delta-f code GENE (genecode.org) that enables stable global simulations at experimental plasma $\beta$ values. The turbulent transport is found to exhibit a multi-channel, multi-scale character throughout the pedestal with the dominant contribution transitioning from ion-scale trapped electron modes/micro-tearing modes at the pedestal top to electron-scale electron temperature gradient modes in the steep gradient region. Consequently, the turbulent electron heat flux changes from ion to electron scales and the ion heat flux reduces to almost neoclassic values in the pedestal centre. $E\times B$ shear is found to strongly reduce heat flux levels in all channels (electron, ion, electrostatic, electromagnetic) and the interplay of magnetic shear and pressure gradient is found to locally stabilise ion-scale instabilities.

We study the correlation between the non-thermal velocity dispersion ($\sigma_{nth}$) and the length scale (L) in the neutral interstellar medium (ISM) using a large number of Hi gas components taken from various published Hi surveys and previous Hi studies. We notice that above the length-scale (L) of 0.40 pc, there is a power-law relationship between $\sigma_{nth}$ and L. However, below 0.40 pc, there is a break in the power law, where $\sigma_{nth}$ is not significantly correlated with L. It has been observed from the Markov chain Monte Carlo (MCMC) method that for the dataset of L $\gt$ 0.40 pc, the most probable values of intensity (A) and power-law index (p) are 1.14 and 0.55, respectively. Result of p suggests that the power law is steeper than the standard Kolmogorov law of turbulence. This is due to the dominance of clouds in the cold neutral medium. This is even more clear when we separate the clouds into two categories: one for L is $\gt$ 0.40 pc and the kinetic temperature ($T_{k}$) is $\lt$250 K, which are in the cold neutral medium (CNM) and for other one where L is $\gt$0.40 pc and $T_{k}$ is between 250 and 5 000 K, which are in the thermally unstable phase (UNM). Most probable values of A and p are 1.14 and 0.67, respectively, in the CNM phase and 1.01 and 0.52, respectively, in the UNM phase. A greater number of data points is effective for the UNM phase in constructing a more accurate estimate of A and p, since most of the clouds in the UNM phase lie below 500 K. However, from the value of p in the CNM phase, it appears that there is a significant difference from the Kolmogorov scaling, which can be attributed to a shock-dominated medium.

This paper presents a fault detection and diagnosis (FDD) algorithm for various faults in the primary air data sensors (PADS) of an aircraft in the presence of external disturbances such as atmospheric turbulence. Rapid wind variations due to turbulence induce excessive error in the externally fitted air data probe measurements, which may lead to loss of control and misinterpretations by the flight crew. In adverse environmental conditions, the FDD of air data prefers robust and adaptive air data estimates that use an analytical redundancy approach with fewer computations. The proposed method considers the kinematics of the aircraft instead of the dynamics used in the state-of-the-art algorithms. The advantage of using kinematics is that it can reduce modeling errors significantly, avoiding high false alarm rates in the FDD process. For the estimation of stable and accurate air data under external disturbance, the inertial navigation system and global positioning system (INS/GPS) output are considered instead of actual air data probe or sensor measurements. The proposed algorithm uses estimates of air data using an exponentially weighted adaptive extended Kalman filter (EW-AEKF) to detect and diagnose PADS faults, which can perform well even in the presence of uncertain noise due to atmospheric turbulence experienced during flight. The simulation was carried out to validate the algorithm with flight data obtained from the X-Plane flight simulator under moderate atmospheric turbulence. The simulation experiments were carried out using the MATLAB programming platform. The results show that the proposed method achieves satisfactory FDD performance with lower root mean square error (RMSE) and computation time than traditional EKF-based algorithms.

The effect of sheared E × B flow on the blob dynamics in the scrape-off layer (SOL) of HL-2A tokamak has been studied during the plasma current ramp-up in ohmically heated deuterium plasmas by the combination of poloidal and radial Langmuir probe arrays. The experimental results indicate that the SOL sheared E × B flow is substantially enhanced as the plasma current exceeds a certain value and the strong sheared E × B flow has the ability to slow the blob radial motion via stretching its poloidal correlation length. The locally accumulated blobs are suggested to be responsible for the increase of plasma density just outside the Last Closed Flux Surface (LCFS) observed in this experiment. The results presented here reveal the significant role played by the strong sheared E × B flow on the blob dynamics, which provides a potential method to control the SOL width by modifying the sheared E × B flow in future tokamak plasmas.

We consider the mixing dynamics of an air–liquid system driven by the rotation of a pitched blade turbine (PBT) inside an open, cylindrical tank. To examine the flow and interfacial dynamics, we use a highly parallelised implementation of a hybrid front-tracking/level-set method that employs a domain-decomposition parallelisation strategy. Our numerical technique is designed to capture faithfully complex interfacial deformation, and changes of topology, including interface rupture and dispersed phase coalescence. As shown via transient, a three-dimensional (3-D) LES (large eddy simulation) using a Smagorinsky–Lilly turbulence model, the impeller induces the formation of primary vortices that arise in many idealised rotating flows as well as several secondary vortical structures resembling Kelvin–Helmholtz, vortex breakdown, blade tip vortices and end-wall corner vortices. As the rotation rate increases, a transition to ‘aeration’ is observed when the interface reaches the rotating blades leading to the entrainment of air bubbles into the viscous fluid and the creation of a bubbly, rotating, free surface flow. The mechanisms underlying the aeration transition are probed as are the routes leading to it, which are shown to exhibit a strong dependence on flow history.