We present the design, construction and initial experimental validation of the Northwestern Polytechnical University Taylor–Couette (NPU-TC) apparatus, specifically developed to explore turbulent Taylor–Couette flows under conditions relevant to ultra-high-speed rotating machinery. The apparatus features an inner cylinder capable of rotating at speed of up to 10 000 rpm, corresponding to a Taylor number $Ta = 6.4 \times 10^8$, with an exceptionally narrow annular gap of 2.8 mm, yielding a radius ratio ($\eta$) of 0.98. Axial-scanning particle image velocimetry is employed here for the first time in air-based TC flows at such extreme conditions, which enables detailed velocity measurements without intrusive disturbances. Our velocity measurements demonstrate the absence of large-scale coherent flow structures, indicating a transition into the ultimate turbulence regime characterised by very thin boundary layers and nearly uniform velocity distributions in the bulk region. The NPU–TC apparatus thus represents a significant advance in experimental capabilities, providing critical insights into turbulent flow behaviour in high-speed rotating machinery.

Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. Consequently, extensive research has focused on analysing turbulent flows from a data-driven perspective. However, due to the complex and chaotic nature of these systems, traditional models often become unstable. To overcome these limitations, we propose a purely stochastic approach that separately addresses the evolution of large-scale coherent structures and the closure of high-fidelity statistical data. To this end, the dynamics of the filtered data are learnt using an autoregressive model. This combines a variational-autoencoder (VAE) and Transformer architecture. The VAE projection is probabilistic, ensuring consistency between the model’s stochasticity and the flow’s statistical properties. The mean realisation of stochastically sampled trajectories from our model shows relative $ {L}_1 $ and $ {L}_2 $ distances of 6% and 10%, respectively. Moreover, our framework enables the construction of meaningful confidence intervals, achieving a prediction interval coverage probability of 80% with minimal interval width. To recover high-fidelity velocity fields from the filtered space, Gaussian Process (GP) regression is employed. This strategy has been tested in the context of a Kolmogorov flow exhibiting chaotic behavior. We compare the performance of our model with state-of-the-art probabilistic baselines, including a VAE and a diffusion model. We demonstrate that our Gaussian process-based closure outperforms these baselines in capturing first and second moment statistics in this particular test bed, providing robust and adaptive confidence intervals.

As a direct consequence of liquid kerosene injection, aeroengine combustors may be categorized as non-premixed combustion systems, characterized by a swirl-stabilized and highly complex flow field. In addition to the flow of air through the fuel injector, there are a large number of other features through which the oxidizer can enter the heat release region. These can have an impact on local fuel–air mixing, inducing strong spatial and temporal variations in stoichiometry, thereby affecting emissions and combustion system performance. This article discusses a novel statistical methodology, based on principal component analysis (PCA) and K-means clustering, that aims to improve the understanding of fuel–air mixing in realistic aeroengine combustors. The method is applied in a post-processing step to data sampled from a large-eddy simulation, where every chamber inflow has been tagged with a unique passive scalar, which allows it to be traced across space and time. PCA is used to construct a low-dimensional, visually interpretable representation of a spatially localized fuel–air mixing process, while K-means clustering is employed to produce an unsupervised discretization of the flow field into regions of similar fuel–air mixing characteristics. The proposed methodology is computationally inexpensive, and the easily interpretable outputs can help the combustion engineer make better-informed decisions about combustor design.

In high-precision pulsar timing, the accurate recovery of intrinsic pulsar profiles and their associated scattering parameters is of paramount importance. In this paper, we present a comprehensive study focused on the retrieval of intrinsic pulsar profiles through the use of a CLEAN-based algorithm as described in Bhat et al. (2003, ApJ, 584, 782). The primary objective of this study is to elucidate the capabilities of our pipeline in the context of recovering the intrinsic profiles and associated parameters, such as dispersion measure, frequency scaling index, scattering time, pulse broadening function, and time of arrival residuals. We use simulated profiles to rigorously test and validate the efficiency of our recovery pipeline. These simulated profiles encompass single- and multi-component Gaussians, designed to emulate the diverse nature of pulsar profiles. By comparing the recovered profiles and parameters to their injected values, as derived from simulations, we provide a robust evaluation of the pipeline’s performance along with its drawbacks and limitations.

This study quantifies the viscous interaction between propeller tip vortices and a turbulent boundary layer developing over a semi-elliptic leading-edge plate, located downstream. The experimental wind-tunnel set-up is designed to be representative of the tractor–propeller–wing configuration. Using stereoscopic particle image velocimetry and static wall-pressure measurements, the near-wall flow topology is resolved over the plate, semi-immersed in the propeller slipstream. The results show that the interaction exhibits high spatio-temporal coherence and is dominated by a coupling between primary and secondary vortical structures. Two distinct interaction regions are identified relative to the tip-vortex core: on the inboard side, towards the slipstream interior, the boundary-layer flow experiences strong velocity gradient transitions and amplified near-wall vorticity. The flow on the outboard side, moving out of the slipstream, exhibits wall-parallel velocity deficits and vorticity lift-up consistent with unsteady vortex-induced separation mechanisms. Spanwise velocity induced by the wall-normal component of the primary vortex connects these two regions, with the secondary vortex structure identified as enhancing boundary-layer lift-up on the outboard side. Although no local flow reversal occurs under the tested conditions, localised shear amplification and vorticity roll-up indicative of separation-like behaviour were observed. These findings advance the understanding of viscous slipstream–boundary-layer interaction and its implications for tractor–propeller–wing integration.

Artificial intelligence (AI) has achieved human-level performance in specialised tasks such as Go, image recognition and protein folding, raising the prospect of an AI singularity – where machines not only match, but surpass human reasoning. Here, we demonstrate a step towards this vision in the context of turbulence modelling. By treating a large language model (LLM), DeepSeek-R1, as an equal partner, we establish a closed-loop, iterative workflow in which the LLM proposes, refines and reasons about near-wall turbulence models under adverse pressure gradients (APGs), system rotation and surface roughness. Through multiple rounds of interaction involving long-chain reasoning and a priori and a posteriori evaluations, the LLM generates models that not only rediscover established strategies, but also synthesise new ones that outperform baseline wall models. Specifically, it recommends incorporating a material derivative to capture history effects in APG flows, modifying the law of the wall to account for system rotation and developing rough-wall models informed by surface statistics. In contrast to conventional data-driven turbulence modelling – often characterised by human-designed, black-box architectures – the models developed here are physically interpretable and grounded in clear reasoning.

Russia’s full-scale invasion of Ukraine profoundly disrupted Arctic governance, challenging the long-standing notion of Arctic exceptionalism and creating enduring turbulence. While scholarly debate has largely focused on geopolitical and institutional consequences, the local-level impacts remain underexamined. This study investigates adaptive governance (AG) responses to the war’s effects in Norway’s northernmost counties, Troms and Finnmark, which share a direct border with Russia. The analysis draws on the concepts of crisis, turbulence, and AG, situating them within broader scholarship on how decision-making сenters respond to crises and turbulence and political adaptation. It examines stakeholder responses across four key domains: civilian preparedness, international cooperation, infrastructure development, and the economic repercussions of sanctions. Based on 19 semi-structured interviews, policy documents, and media analysis, the study reveals both adaptation and persistent challenges shaped by pre-existing governance structures, demographic and economic conditions, and past cooperation with Russia. The study contributes to AG literature by unpacking the interplay between strategies, highlighting structural constraints, and demonstrating how geopolitical disruptions shape local governance in strategically significant environments.

We report experimental optical and thermodynamical studies of convection cooling for face cooling of laser amplifier disks. Amplifier maquettes are used to explore the flow regime in laser relevant conditions, and to measure heat exchange coefficients $h$. We thus benchmark analytical and numerical predictions, based on common models of turbulence. The ${y}^{+}$ model appears best suited to compute $h$ in the laminar regime, and the Reynolds-Average Navier–Stokes model in the weakly turbulent regime. By strioscopic imaging, we examine the optical properties of the flows, in particular the onset of a striation instability occurring well before the transition to turbulence. At higher Reynolds numbers, the unstable thermal layer is shown to be pushed back onto the surface, suppressing effectively the wavefront distortions from striations. This super-forced thermal regime may be of high interest for very high thermal loads.

This paper is based on the Lanchester Lecture of the Royal Aeronautical Society held in London, UK, in October 2023. The lecture discussed the advances in computational modeling of separated flows in aerospace applications since Elsenaar’s Lanchester Lecture in 2000. Elsenaar’s efforts focused on assumptions primarily associated with separation for steady inflow and a static (non-moving) vehicle or component. Since that time, significant advancements in computational hardware, coupled with substantial investments in the development of algorithms and solvers, have led to important breakthroughs in the field. In particular, computational aerodynamics techniques are currently applied to complex aerospace problems that include unsteady or dynamic considerations, such as dynamic stall and gusts, which are discussed. A perspective of the technology developed over the past quarter-century, highlighting their importance to computational aerodynamics is discussed. Finally, the potential of future areas of development, such as machine learning, that may be exploited for the next generation of computational aerodynamics applications is explored.

Complex materials with internal microstructure such as suspensions and emulsions exhibit time-dependent rheology characterised by viscoelasticity and thixotropy. In many large-scale applications such as turbulent pipe flow, the elastic response occurs on a much shorter time scale than the thixotropy, hence these flows are purely thixotropic. The fundamental dynamics of thixotropic turbulence is poorly understood, particularly the interplay between microstructural state, rheology and turbulence structure. To address this gap, we conduct direct numerical simulations (DNS) of fully developed turbulent pipe flow of a model thixotropic (Moore) fluid as a function of the thixoviscous number $\Lambda$, which characterises the thixotropic kinetic rate relative to turbulence eddy turnover time, ranging from slow ($\Lambda \ll 1$) to fast ($\Lambda \gg 1$) kinetics. Analysis of DNS results in the Lagrangian frame shows that, as expected, in the limits of slow and fast kinetics, these time-dependent flows behave as time-independent purely viscous (generalised Newtonian) analogues. For intermediate kinetics ($\Lambda \sim 1$), the rheology is governed by a path integral of the thixotropic fading memory kernel over the distribution of Lagrangian shear history, the latter of which is modelled via a simple stochastic model for the radially non-stationary pipe flow. The DNS computations based on this effective viscosity closure exhibit excellent agreement with the fully thixotropic model for $\Lambda =1$, indicating that the purely viscous (generalised Newtonian) analogue persists for arbitrary values of $\Lambda \in (0,\infty ^+)$ and across nonlinear rheology models. These results significantly simplify our understanding of turbulent thixotropic flow, and provide insights into the structure of these complex time-dependent flows.

Intermittency as it occurs in fast dynamos in the magnetohydrodynamics (MHD) framework is evaluated through the examination of relations between normalized moments at third order (skewness $S$) and fourth order (kurtosis $K$) for both the velocity and magnetic field, and for their local dissipations. As investigated by several authors in various physical contexts such as fusion plasmas (Krommes 2008 Phys. Plasmas 15, 030703), climate evolution (Sura & Sardeshmukh 2008 J. Phys. Oceano. 38, 639-647), fluid turbulence or rotating stratified flows (Pouquet et al. 2023 Atmosphere 14, 01375), approximate parabolic $K(S)\sim S^\alpha$ laws emerge whose origin may be related to the applicability of intermittency models to their dynamics. The results analyzed herein are obtained through direct numerical simulations of MHD flows for both Taylor–Green and Arnold–Beltrami–Childress forcing at moderate Reynolds numbers, and for up to $3.14 \times 10^5$ turn-over times. We observe for the dissipation $0.2 \lesssim \alpha \lesssim 3.0$, an evaluation that varies with the field, the forcing and when filtering for high-skewness intermittent structures. When using the She & Lévêque (1994) Phys. Rev. Lett. 72, 336-339 intermittency model, one can compute $\alpha$ analytically; we then find $\alpha \approx 2.5$, clearly differing from a (strict) parabolic scaling, a result consistent with the numerical data.

We present the Okinawa Institute of Science and Technology – Taylor–Couette set-up (OIST-TC), a new experimental set-up for investigating turbulent Taylor–Couette (TC) flow. The set-up has independently rotating inner and outer cylinders, and can achieve Reynolds numbers up to $10^6$. Noteworthy aspects of its design include innovative strategies for temperature control and vibration isolation. As part of its flow-measurement instrumentation, we have implemented the first ‘flying hot-wire’ configuration to measure the flow velocity whilst either or both cylinders are rotating. A significant challenge for obtaining reliable measurements from sensors within the inner cylinder is the data distortion resulting from electrical and electromagnetic interference along the signal pathway. Our solution involves internal digitization of sensor data, which provides notable robustness against noise sources. Additionally, we discuss our strategies for efficient operation, outlining custom automation tools that streamline both data processing and operational control. We hope this documentation of the salient features of OIST-TC is useful to researchers engaged in similar experimental studies that delve into the enchanting world of turbulent TC flow.