Turbulent Field Research Articles

High-frequency oscillations occur in the centrally staged combustor during operation. To effectively suppress them, real-time monitoring of the combustor exit temperature is critical. However, traditional contact temperature measurement methods are inadequate for accurately capturing temperature variations in the turbulent flow field. Tunable Diode Laser Absorption Spectroscopy (TDLAS) with a high acquisition frequency is employed to measure the temperature of the centrally staged combustor, utilizing a non-contact sensing method. The influence of various combustion parameters on the uniformity of combustion within the chamber and the capability of TDLAS to capture temperature data of the combustion chamber under different acquisition frequencies are studied. The results indicate that the staging ratio causes irregular oscillations in the combustion chamber outlet temperature. At an acquisition frequency of 1 kHz, an increase in the staging ratio raises the average temperature at the outlet and slows down the temperature oscillation when other parameters remain constant. At an acquisition frequency of 10 kHz, more small, high-frequency variations in the centrally staged combustor outlet temperature are observed. When the TDLAS system operates at 10 kHz, it can capture more details of the combustion chamber outlet temperature oscillation under the same working conditions and exhibits stronger noise immunity. However, compared with the acquisition frequency of 1 kHz, it cannot sustain long-term measurement.

We provide an overview of theoretical developments in the rapidly developing field of active-fluid turbulence. After a summary of the partial differential equations (PDEs) for active fluids with scalar, polar, and nematic order parameters, we concentrate on a minimal description that employs the Toner-Tu-Swift-Hohenberg (TTSH) model. We present illustrative results —numerical, theoretical, and rigorous— for TTSH turbulence. In particular, we present statistical properties of such turbulence in both Eulerian and Lagrangian frameworks. We end with a perspective of new trends in the field of active-fluid turbulence.

An Investigation of the Effect of Intermittency on the Turbulent Field on Particle Acceleration in the Plasma Sheet of the Earth’s Magnetotail

The robustness of skyrmion numbers of structured optical fields in atmospheric turbulence

Investigation of the critical flow velocity for detachment between various hydrophobic glass beads and bubbles in turbulent flow field

Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field - Sequence development and in-vitro validation.

Fluctuating pressure within the turbulent boundary layer is a primary source of structural vibration and flow-induced noise, which is crucial for extending the operational life of aircraft, enhancing the stability of ships, and improving the stealth capabilities of underwater vehicles. This work investigates the flow characteristics of wall fluctuating pressures on flat and curved plates in a high-speed water tunnel using miniature fluctuating pressure sensors. The results indicate that an adverse pressure gradient causes flow separation on the lower surface of a concave curved plate, leading to pronounced fluctuations in large-scale pressure signals over time. The wavelet energy of the mid-to-high-frequency band signal significantly increases. This effect becomes more pronounced as curvature increases. Additionally, multi-scale analysis reveals the wavelet energy dynamics of fluctuating pressure as water flow speed changes. It shows that fluctuations are most evident within the mid-scale frequency bands. These findings suggest that fluctuating pressure is primarily driven by the interaction of large-scale vortices within the turbulent flow field. This study provides valuable data support and theoretical foundations for the optimization and development of subsequent fluctuating pressure prediction models, contributing to enhanced accuracy in flow-induced noise calculations.

Analysis of the turbulence field in a low-speed fan test rig with distorted inflow

New Index Characterizing Magnetic Field Turbulence in Relation to the Galactic Cosmic Ray Intensity Variation in the Period 1968 – 2023

Introduction. The subject of the study is a method of flotation, in accordance with which a mixture of air with hot (with saturation temperature > 104 °C) water vapour – vapour-air mixture is used as a gas phase in flotation. One of the consequences of aeration of cold slurry by air bubbles filled with hot water vapour is a change in their size distribution. Materials and methods. The study used a system in which water is separated from its vapour by a heat-insulating quasi-baffle as a thermophysical model of the process. The two-phase vapour-liquid flow starts at the moment when the quasi-baffle is instantly removed. The effectiveness of pulp aeration was experimentally tested on samples of copper-nickel ores from two deposits. Results. It is shown that the initial size distribution function is shifted towards smaller bubbles as a result of the turbulent velocity field. Discussion. Phase transitions cause changes in bubble size. As the vapour condenses, the bubble size decreases, but at the same time the heat exchange between the liquid medium and the vapour deteriorates, the pressure of which increases sharply as a result of overheating. As a result, the surface of the bubble makes periodic radial pulsations (oscillations). However, heat exchange between the phases is irreversible: the amount of heat received by the bubble during expansion due to evaporation of the carrier medium is less than it gives to the carrier medium in the form of condensation heat during compression. Therefore, the bubble size fluctuations are damped. But it is possible that the energy accumulated by the bubble during radial compression can be transferred into the energy of the pressure wave and the deformation oscillations caused by it, which are performed with a large amplitude and crush the bubble. Conclusion. The interaction of a vapour-air bubble with a liquid consists in phase transitions – condensation of vapour and evaporation of the carrier medium. The change of bubble size as a result of thermophysical transitions of vapour as a gas is of subordinate importance. Resume. High technological efficiency of the developed technology has been confirmed on samples of ores of the exploited deposits. The development of the theory of flotation by thermally loaded bubbles can go in the direction of research of physical and chemical regularities of the process.

An experiment is conducted to investigate the turbulent flow field close to a wall-fastened horizontal cylinder. The evolution of the flow field is analyzed by evaluating turbulent flow characteristics and fluid dynamics along the lengthwise direction. The approach flow velocity retards in the immediate upstream area of the cylinder. At the crest level of the cylindrical pipe, the turbulence characteristics such as Reynolds stresses and turbulence intensities are attaining their peaks. Gram–Charlier (GC) series-based Hermite polynomials yield probability density functions that better match experimental data than those from Gram–Charlier (GC) series-based exponential distributions, demonstrating the superiority of the Hermite polynomial method. Quadrant analysis reveals that sweeps (Q4) dominate intermediate and free-surface zones, while ejections (Q2) prevail near the bed, both being primary contributors to Reynolds shear stress (RSS). The stress component remains minimal or zero for all events when hole size H≥six. Larger hole sizes (≥five) drastically reduced the stress fraction, approaching zero. The stress fraction was highest near the cylinder, decreasing with distance and eventually plateauing. The study enhances the understanding of flow hydraulics around cylindrical objects in rough-bed natural streams.

Wind noise impairs the functionality of hearing aids and hearables outdoors or during sports by interfering with communication signals. This study aims to visualize the wind noise generation patterns around the human head by validated scale-resolved flow simulations. For the first time, the three-dimensional turbulent flow field at wind speeds of 10 km/h and 20 km/h around a female, a male and an artificial head is analyzed. It is possible to extract non-accessible data even inside the body, e.g., the pressure field deep inside the ear cavity in front of the eardrum. Head-geometry-independent flow features are identified. In the temple area, large-scale vortex shedding occurs. Small-scale vortices detach at the upper edge of the pinna and across the entire ear area. At typical microphone positions of behind the ear worn hearing devices, the pressure fluctuations are more pronounced than those at the auditory canal entrance. The tragus of the pinna plays a decisive role in attenuating wind noise in front of the entrance to the auditory canal. Anatomically exact ear canals ensure that velocity fluctuations are attenuated more effectively compared to an artificial one. At 20 km/h, the A-weighted pressure levels recorded at the microphone location of a behind the ear worn hearing devices exceed 85 dB(A). The results lead to a first understanding of wind noise effects and how they increase the perception threshold for recognition. Manufacturers can use the model to facilitate the wind noise optimal placement of microphones in new products to enhance communication under windy conditions.

The study of radio emission in starburst dwarf galaxies provides a unique opportunity to investigate the mechanisms responsible for the amplification and transport of magnetic fields. Local dwarfs are often considered proxies for early Universe galaxies, so this study may provide insights into the role of non-thermal components in the formation and evolution of larger galaxies. By investigating the radio continuum spectra and maps of the starburst dwarf galaxies, we aim to draw conclusions on their magnetic field strengths and configurations, as well as on the dynamics of cosmic ray (CR) transport. We performed a radio continuum polarimetry study of two of the brightest starburst IRAS Revised Bright Galaxy Sample (RBGS) dwarf galaxies, NGC,3125 and IC,4662. By combining data of the Australian Telescope Compact Array (2.1,GHz) and MeerKAT (1.28,GHz), we analysed the underlying emission mechanism and the CR transport in these systems. We find flat spectra in the dwarf galaxies over the entire investigated frequency range, which sharply contrasts with observations of massive spiral galaxies. Because the expected cooling time of CR electrons is much shorter than their escape time, we would expect a steepened steady-state CR electron spectrum. The flat observed spectra suggest a substantial contribution from free-free emission at high frequencies and absorption at low frequencies, which may solve this puzzle. For NGC,3125, we measured a degree of polarisation between $0.75,%$ and $2.6,%$, implying a turbulent field and supporting the picture of a comparably large thermal emission component that could be sourced by stellar radiation feedback and supernovae.

During high-speed flight, the aircraft causes rapid compression of the surrounding air, creating a complex turbulent flow field. This high-speed flow field interferes with the optical transmission of optical imaging systems, resulting in high-frequency random displacement, blurring, intensity attenuation, or saturation of the target scene. Aero-optical effects severely degrade imaging quality and target recognition capabilities. Based on the spectral characteristics of aero-optical degraded images and the deep learning approach, this paper proposes an adaptive frequency selection network (AFS-NET) for correction. To learn multi-scale and accurate features, we develop cascaded global and local attention mechanism modules to capture long-distance dependency and extensive contextual information. To deeply excavate the frequency component, an adaptive frequency separation and fusion strategy is proposed to guide the image restoration. Integrating both spatial and frequency domain processing and learning the residual representation between the observed data and the underlying ideal data, the proposed method assists in restoring aero-optical degraded images and significantly improves the quality and efficiency of image reconstruction.

We use direct numerical simulations to investigate the energy pathways between the velocity and the magnetic fields in a rotating plane layer dynamo driven by Rayleigh–Bénard convection. The kinetic and magnetic energies are divided into mean and turbulent components to study the production, transport and dissipation in large- and small-scale dynamos. This energy balance-based characterisation reveals distinct mechanisms for large- and small-scale magnetic field generation in dynamos, depending on the nature of the velocity field and the conditions imposed at the boundaries. The efficiency of a dynamo in converting the kinetic energy to magnetic energy, apart from the energy redistribution inside the domain, is found to depend on the kinematic and magnetic boundary conditions. In a small-scale dynamo with a turbulent velocity field, the turbulent kinetic energy converts to turbulent magnetic energy via small-scale magnetic field stretching. This term also represents the amplification of the turbulent magnetic energy due to work done by stretching the small-scale magnetic field lines owing to fluctuating velocity gradients. The stretching of the large-scale magnetic field plays a significant role in this energy conversion in a large-scale turbulent dynamo, leading to a broad range of energetic scales in the magnetic field compared with a small-scale dynamo. This large-scale magnetic field stretching becomes the dominant mechanism of magnetic energy generation in a weakly nonlinear dynamo. We also find that, in the weakly nonlinear dynamo, an upscale energy transfer from the small-scale magnetic field to the large-scale magnetic field occurs owing to the presence of a gradient of the mean magnetic field.

The application of hydrocyclones can be traced back more than 100 years. Due to their unique advantages, they have been widely used in various separation industries. However, the classification efficiency of hydrocyclones is relatively low due to structural limitations. Therefore, improving the classification performance of hydrocyclones has become a research hotspot and challenge. As critical components, the vortex finder and underflow outlet significantly affect the classification performance of hydrocyclones. This paper designs a series of vortex finder and underflow outlet structures to improve classification accuracy. Numerical analysis and experimental verification methods are used, with pressure field, velocity field, turbulence field, and classification efficiency as evaluation criteria, to investigate the mechanisms underlying the classification performance of hydrocyclones with different structures. Results show that the pressure drop of the Type D (double outlet expansion) hydrocyclone is the smallest, indicating lower energy consumption under identical operating conditions, thereby reducing operating costs. The Type D with an arc vortex finder shifts the LZVV inward, enlarging the separation space for coarse particles while reducing it for fine particles. The circulating flow of the Type D hydrocyclone decreases by 2.26 percentage points compared to the Type A (Base) hydrocyclone, and the short-circuit flow decreases by 4.43 percentage points, resulting in a more stable internal flow field. Type D has a smaller cut size and a higher steepness index, demonstrating stronger cutting ability and higher classification accuracy. Experimental verification shows that the −6 μm underflow content of the Type D hydrocyclone is 13.93 percentage points lower than that of the Type A hydrocyclone, while the −75 μm overflow content decreases by 3.97 percentage points. The issues of fine particles in the underflow and coarse particles in the overflow are effectively mitigated. The data obtained in this study provide theoretical and empirical support for the development of novel hydrocyclone structures.

The prediction of high-resolution turbulent flow field is often difficult as the experimental measurements are spatially sparse in nature. Due to the limitations of the measurement instruments, the feature at major areas in the flow field could not be captured. The present work uses a Bayesian-inference-based physics-informed neural network framework to reconstruct high-resolution turbulent flow field with high accuracy using sparse particle image velocimetry (PIV) measurements. The periodic hill case is selected to demonstrate the framework, which is trained with the mean flow measurements. The degree of sparsity of the PIV measurement and the locations of measurement data are varied, and it is found that the recirculation zone of the periodic hill requires more sensors for efficient reconstruction of the flow field. The uncertainty in the prediction is quantified at various locations across the flow field. It is identified that the prediction uncertainty is greater where sensors are insufficient, or there is no sensor. However, the overall uncertainty is within acceptable limits. Finally, the robustness of the model is tested against the various noise levels in the measurement and with different sensor arrangements. With the reduction in the number of measurement data points, the effect of noise in measurement becomes significant, and the uncertainty in the prediction increases. The model can accurately (≤±10%) reconstruct the high-resolution flow field with high noise levels (up to 20%) when there are more labeled data points. Further, the adaptability of the framework has been tested for flow over a square cylinder.

Abstract Wind energy, as an important component of renewable energy, is inexhaustible and has developed rapidly in recent years. With the increasing installed capacity of wind turbines, more and more turbines are being installed on complex terrains such as ridges. The installation of wind turbines on complex terrains can easily lead to the formation of slope terrains, altering the original topography and, consequently, changing the distribution of wind resources, which seriously affects the power generation efficiency and safe operation of wind turbines. This study focuses on a two-dimensional ridge as the research object, generating a realistic atmospheric turbulent wind field based on the NSRFG method to serve as inflow conditions. Large Eddy Simulation (LES) is employed to conduct numerical simulations of five excavation depths for the lifting platform. The results indicate that as the excavation depth increases, the wind speed variations in the low-height region (z < 1H) become more pronounced. Specifically, the wind speed variation range at an excavation depth of 0.195H is 5.6 times that at 0.075H, demonstrating that the size of the excavation depth significantly affects the wind speed distribution at low heights. Therefore, the greater the depth, the more dramatic the wind speed changes, with 0.075H being the optimal depth among the five excavation depths.

In the field of fluid mechanics, inferring high-dimensional data from low-dimensional observations enables the reconstruction of the high-dimensional flow field, allowing for a more comprehensive understanding of the flow, which is of great importance for analyzing flow mechanisms and optimizing aerodynamic designs. However, the lack of theoretical methodologies makes reconstructing three-dimensional (3D) flow fields a challenging task. To overcome this difficulty, a diffusion transformer-based method is proposed in this paper for reconstructing two-dimensional (2D) flow fields into 3D flow fields. The method introduces plane position embedding to enable reconstruction from arbitrary 2D planes, significantly enhancing flexibility. Furthermore, a local attention mechanism based on windows and planes is introduced to replace the vanilla global attention mechanism, improving computational efficiency by approximately 25%–40%. The developed method is applied to the reconstruction of several 3D turbulent flow fields both with and without objects. The results show that the diffusion transformer-based method in this paper exhibits high accuracy in flow field reconstruction for all numerical experiments compared to other methods such as U-Net and generative adversarial network-based approaches and achieves an average structural similarity index measure improvement of 20%, with normalized root mean square error improvements exceeding 50% in some test cases, and due to its lower resource consumption, the method demonstrates tremendous potential for reconstructing complex 3D fluid flows.

This study presents a novel deep learning framework aimed at achieving super-resolution of velocity fields within turbulent channel flows across various wall-normal positions. The model excels at reconstructing high-resolution flow fields from low-resolution data, with an emphasis on accurately capturing spatial structures and spectral energy distributions. Input data are generated through fine-grid large eddy simulations, employing a data-driven approach. The model's efficacy is evaluated using standard image quality metrics, including peak signal-to-noise ratio, structural similarity index measure, root mean square error, mean absolute error, good pixel percentage, as well as spectral analyses to encapsulate the complex dynamics of turbulent flow physics. The findings demonstrate substantial correlations between model performance and wall-normal location. Specifically, the model performs superior in regions distal from the channel wall but faces challenges in accurately recovering small-scale turbulent structures near the boundary layer.