Large-scale Flow Field Research Articles

An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows

Small-scale motions in turbulent flows play a significant role in various small-scale processes, such as particle relative dispersion and collision, bubble or droplet deformation, and orientation dynamics of non-sphere particles. Recovering the small-scale flows that cannot be resolved in large eddy simulation (LES) is of great importance for such processes sensitive to the small-scale motions in turbulent flows. This study proposes a subgrid-scale model for recovering the small-scale turbulent velocity field based on the artificial neural network (ANN). The governing equations of small-scale turbulent velocity are linearized, and the pressure gradient and the nonlinear convection term are modeled with the aid of the ANN. Direct numerical simulation (DNS) and filtered direct numerical simulation (FDNS) provide the data required for training and validating the ANN. The large-scale velocity and velocity gradient tensor are selected as inputs for the ANN model. The linearized governing equations of small-scale turbulent velocity are numerically solved by coupling the large-scale flow field information. The results indicate that the model established by the ANN can accurately recover the small-scale velocity lost in FDNS due to filtering operation. With the ANN model, the flow fields at different Reynolds numbers agree well with the DNS results regarding velocity field statistics, flow field structures, turbulent energy spectra, and two-point, two-time Lagrangian correlation functions. This study demonstrates that the proposed ANN model can be applied to recovering the small-scale velocity field in the LES of isotropic turbulent flows at different Reynolds numbers.

Synthetic color-and-depth encoded (sCade) illumination-based high-resolution light field particle imaging velocimetry

Light-field particle imaging velocimetry (LF-PIV) is widely used in large-scale flow field measurement scenarios due to its instant 3D imaging capability. However, conventional LF-PIV systems suffer low axial resolution and thereby have limited application in high-resolution and volumetric velocity measurements. Here, we report the use of synthetic color-and-depth-encoded (sCade) illumination to improve the axial resolution of LF-PIV. The sCade LF-PIV illuminated the imaging region with a color-and-depth encoded beam synthesized by structured beams of three lasers with distinct wavelengths and attained high-fidelity particle localization by decoding the color and depth information encoded in the acquired image. We systematically characterized the system performance by imaging particles and obtained 29 times improvement in axial resolution when compared to traditional LF-PIV. The high axial resolution of sCade LF-PIV allowed it to reconstruct vortices generated by square lid-driven cavity flow and a stirring disk with higher accuracy and smaller errors than the conventional method, highlighting the possibility and advantage of sCade LF-PIV for high-resolution and volumetric flow measurement applications. This approach can favorably advance the development of fluid measurement technology.

Lagrangian particle tracking in the atmospheric surface layer

Abstract Field measurements in the atmospheric surface layer (ASL) are key to understanding turbulent exchanges in the atmosphere, such as fluxes of mass, water vapor, and momentum. However, current field measurement techniques are limited to single-point time series or large-scale flow field scans. Extending image-based laboratory measurement techniques to field-relevant scales is a promising route to more detailed atmospheric flow measurements, but this requires significant increases in the attainable measurement volume while keeping the spatiotemporal resolution high. Here, we present an adaptable particle tracking system using helium-filled soap bubbles, mirrorless cameras, and high-power LEDs enabling volumetric ASL field measurements. We conduct analyses pertinent to image-based field measurement systems and develop general guidelines for their design. We validate the particle tracking system in a field experiment. Single-point Eulerian velocity statistics are presented and compared to data from concurrently operated sonic anemometers. Lagrangian displacement statistics are also presented with a comparison to Taylor’s theory of dispersion. The system improves the state-of-the-art in field measurements in the lower atmosphere and enables unprecedented insights into flow in the ASL.

Modeling the effects of starspots on stellar magnetic cycles

Context. Observations show that faster rotating stars tend to have stronger magnetic activity and shorter magnetic cycles. The cyclical magnetic activity of the Sun and stars is believed to be driven by the dynamo process. The success of the Babcock-Leighton (BL) dynamo in explaining the solar cycle suggests that starspots could play an important role in stellar magnetic cycles. Aims. We aim to extend the BL mechanism to solar-mass stars with various rotation rates and explore the effects of emergence properties of starspots in latitudes and tilt angles on stellar magnetic cycles. Methods. We adopt a kinematic BL-type dynamo model operating in the bulk of the convection zone. The profiles of the large-scale flow fields are from the mean-field hydrodynamical model for various rotators. The BL source term in the model is constructed based on the rotation dependence of starspot emergence; that is, faster rotators have starspots at higher latitudes with larger tilt angles. Results. Faster rotators have poloidal flux appearing closer to about ±55° latitudes, where the toroidal field generation efficiency is the strongest because of the peak in the strength of the latitudinal differential rotation there. It takes a shorter time for faster rotators to transport the surface poloidal field from their emergence latitude to the ±55° latitudes of efficient Ω-effect, which shortens their magnetic cycles. The faster rotators operate in a more supercritical regime because of a stronger BL α-effect relating to the tilt angles, which leads to stronger saturated magnetic fields and makes the coupling of the poloidal field between two hemispheres more difficult. The magnetic field parity therefore shifts from the hemispherically asymmetric mixed mode to quadrupole, and further to dipole when a star spins down. Conclusions. The emergence of starspots plays an essential role in the large-scale stellar dynamo.

The Impact of Offshore Photovoltaic Utilization on Resources and Environment Using Spatial Information Technology

In recent years, the rapid development of the photovoltaic (PV) industry has resulted in a saturation of research on onshore PV power plant construction. However, current studies on the impact of marine PVs on the marine environment remain limited and scarce. In order to facilitate the implementation of carbon reduction goals and promote the sustainable development of the offshore PV industry, this study analyzes the environmental impact of PV sea-use resources based on spatial information technology in the western part of Gaotang Island. The findings show that the MIKE21FM model provides relatively accurate simulations of tidal flow and tide level in the marine PV area. Flow velocity in the marine PV area exhibits a decreasing trend, with an average decrease ranging from 0.03 to 0.07 m/s. This decrease minimally affects surrounding navigational channels and large-scale flow fields. The resulting siltation is also deemed less significant, with an annual deposition from 0.03 to 0.06 m/a. Moreover, offshore PV construction resulted in a total intertidal biological loss of 123.45 t. The suspension of sediment during cofferdam construction and removal has a potential effect on zooplankton and fishery resources. Overall, it is proposed that careful planning, prudent site selection, and the execution of countermeasures during marine PV construction will combine to minimize the impact on the marine environment.

Large-scale flow field super-resolution via local-global fusion convolutional neural networks

Particle image velocimetry (PIV) techniques have a limited field of view of the flow field and can only capture high-resolution flow fields in localized areas. To obtain a larger measurement range, multiple cameras must be used to capture the flow field simultaneously and then stitch the parts together. However, this method can be expensive. We propose the local-global fusion convolutional neural network (LGF-CNN) for reconstructing large-field flow fields with high spatial resolution based only on two flow data types: local small-field high spatial resolution wake velocity fields and global large-field low spatial resolution velocity fields. The core of the model consists of convolutional neural network (CNN) architecture to learn the mapping relationship between the small field of view with high spatial resolution and the large field of view with low spatial resolution. Using the effectively trained LGF-CNN model, we demonstrate its ability to reconstruct high-resolution velocity fields around the circular cylinder. The LGF-CNN is rigorously validated on a number of representative datasets, including simulated data for Reynolds numbers of 200 and 500, as well as experimental data for a Reynolds number of 3.3 × 104 with the steady jet at the rear stagnation point of the cylinder. The results demonstrate the ability of LGF-CNN to generate accurate velocity fields with high spatial resolution, including reliable detection of high-frequency components. The proposed method could reduce the number of cameras required for large-field, high spatial resolution PIV measurements, thereby reducing experimental costs.

Study on the safety of a magnetic strainer at the deaerator outlet under two-phase and large-scale flow fields

Iron oxide corrosion exists in the deaerator, which causes serious damage to the water supply system. To absorb iron oxide impurities for the feed water quality, a magnetic strainer device has been designed at the deaerator outlet. In order to ensure the safe and stable operation of the deaerator, the computational fluid dynamics (CFD) method was used in this work to analyze the impact of complex outlet conditions on the gas-liquid two-phase flow field, pressure field, and structural safety within a deaerator. After the strainer is installed, turbulent kinetic energy (TKE) and turbulence intensity (TI) are not monotonically changing throughout strainer region and depend on the local flow state and geometry. TI relies more on turbulence disturbance and geometry. The overall flow near the strainer is accelerated, and the dimensionless velocity in the shear layer on both sides of the strainer center bracket increases by a maximum of 153.44%. Stable vortices and complex secondary vortices are formed near the strainer. The overall eddy is more fragmented, and eddy dissipation is prone to occur in the outlet region. However, this does not cause additional large turbulence losses. The impact of the strainer on the static pressure loss of the entire deaerator is very limited, and the reduction in static pressure difference does not exceed 0.04%. Finally, suggestions are made for the structural improvement and maintenance of the strainer based on the simulation results, which provide a reference for further safe and stable application of the deaerator magnetic strainer.

Performance and configuration optimization of proton exchange membrane fuel cell considering dual symmetric Tesla valve flow field

Aiming at the advantages of Tesla valve channel (TVC), such as low resistance and smooth flow, a new bionic-like channel structure with dual symmetric (DS) TVC as cathode is proposed. Firstly, through a series of structural parameters optimization, including experimental verification of the model, the arrangement of modules in the channel, the internal angle and the number of modules, the best design parameters are obtained. Secondly, the mass spatial distribution characteristics of proton exchange membrane fuel cell (PEMFC) with large-scale dual symmetric Tesla valve flow field (DSTVFF) are investigated. Finally, the DSTVFF structure is improved by baffles with a front inclination angle of 30° to reduce the contact area of the ridge. The simulation results indicate that the pressure drop of the fourth DSTVC is small and the oxygen concentration distribution is uniform. The increase of the inner angle promotes the convection effect, and the power density at the angle of 30° is 17.6 % higher than that at the angle of 15°. Reasonable number of modules can increase the activation area of catalyst layer (CL) and enhance the output performance of fuel cell (FC).

Large-scale flow field and aerosol particle transport investigations in a classroom using 2D-Shake-The-Box Lagrangian Particle Tracking

Infections with COVID-19 in enclosed public spaces, where virus-laden aerosol particles can accumulate over time, have significantly contributed to the rapid spread of the virus. It is therefore of great importance to understand the transport and dispersion process of aerosol particles in such spaces, especially against the background of future pandemics. In this work, we present a Lagrangian-Particle-Tracking experiment to assess the mixed convective flow in a classroom with different ventilation strategies. For this purpose, thermal plumes were created by heated dummies, and a collimated LED light-sheet with ∼0.4 m thickness was used for illumination of helium filled soap bubbles (HFSB) acting as passive tracer particles. In this way, the Lagrangian trajectories of the particles were recorded at two approximately 4.2 m × 2.8 m large fields using the novel 2D-Shake-The-Box-Method. As a result, time-resolved trajectories of over 300,000 simultaneously tracked HFSB have been reconstructed, so that both small-scale and large-scale properties of the flow are visualized quantitatively across the entire cross-section of the room. The trajectories show that the thermal plumes create lengthwise circulating vortices, which cannot be destroyed across the entire cross-section of the room by opening or tilting a window. Furthermore, the mixing in the room through the operation of an air purifier is higher compared to opening a window, which suggests that this strategy in combination with its air filtering capability is the most effective strategy to prevent infections.

An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow

Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps sequentially can result in substantial frame delay, whereas loading and rendering all time steps simultaneously can result in excessive memory usage. To address these issues, the proposed method analyzes the variable description information in the data files to bypass redundant variables and read the flow field data as required. Second, a hash table is constructed to derive the two-dimensional surface mesh of the flow field and complex mesh cells are simplified into simple linear cells to reduce the mesh’s complexity. This paper presents a method for reducing the memory usage of complex data sets by more than 90%, compared with the ParaView data reading method. The proposed method is tested on four sets of unstructured unsteady flow field data with different data structures. The animation visualization method based on simplified data can achieve an average frame rate of less than 100ms and supports real-time user interaction on personal computers. It extends the ability of personal computers to analyze large-scale unstructured unsteady flow fields.

Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data

With the increasing demand for high precision in numerical simulations using computational fluid dynamics (CFD), the use of large-scale grids for discretized solutions has become a trend, resulting in an explosive growth of flow-field data size. To address the challenges posed by large-scale flow-field data for real-time interactive visualization, this paper proposes novel strategies for data partitioning and communication management. Firstly, we propose a data-partitioning strategy based on grid segmentation. This approach constructs metadata to create file viewports for each process and performs grid partitioning. Subsequently, it reconstructs sub-grids within each process and utilizes a coordinate-mapping algorithm to map global coordinates to local process coordinates, facilitating access to attribute variables through a lookup table. Secondly, we introduce a real-time interactive method for large-scale flow fields. This method leverages the system architecture of high-speed interconnection among compute nodes in a cluster environment and low-speed interconnection between service nodes and rendering nodes. It enables coordinated management of parallel rendering and synchronized rendering methods. The experimental results on typical flow-field data demonstrate that the proposed data-partitioning strategy improves the loading speed of millions of grid-level data by a factor of 7, surpassing ParaView’s performance by 1.5 times. Furthermore, it achieves system load balancing. Real-time interaction experiments with datasets containing 500 million and 800 million grid cells exhibit millisecond-level latencies, demonstrating the effectiveness of the proposed communication management method in meeting the real-time interactive visualization demands of large-scale flow-field data.

Developing and testing a web-based platform for visualizing flow in a watershed

Abstract Thanks to the rapid development of internet technology and computer hardware, it is now possible to use web services to provide visual simulations of flow field calculation results. Visualization technology can display complex water flow data and the laws that govern water flow through graphical means, and can be used to solve scientific and engineering problems related to water conservancy. In this study, we developed a platform for visualizing flow in a watershed based on a Cesium rendering framework with Browser/Server (B/S) architecture that used isosurface, particles, texture-based, and dynamic flow visualization techniques to visualize scalar field, vector field, and dynamic flow field data. Furthermore, our performance test results indicate that the rendering performance meets the practical application requirements for visualizing large-scale flow fields by employing frame interpolation and viewpoint-based dynamic rendering techniques. The results from testing the water flow visualization platform in the Beijiang River Basin, Guangdong Province, China, demonstrated that the platform performed well on different devices and that the running frame rate reached 50–60 fps. These findings can be used to guide further development and applications of web-side flow field visualization technology.

Fast prediction of the performance of the centrifugal pump based on reduced-order model

In the present paper, the prediction of the performance of a centrifugal pump is investigated based on proper orthogonal decomposition (POD) reduced-order model (ROM). Firstly, the POD method is used to analyze the characteristics of the complex flow field inside the centrifugal pump, especially the eigenvalues of the velocity field and pressure field, and then the ROM of the flow field in the centrifugal pump is established then. After that, the established reduced-order model is used to predict the internal flow field. The results show that the POD mode field can reflect the spatial scale distribution of the flow field. The low-order flow field reflects the large-scale flow field structure, while the high-order flow field reflects the small-scale flow field structure. With the established ROM, the overall pump head (0D, 0-dimensional parameter) can be predicted with the relative error of less than 5%, and the standard root mean square error (normalized root mean square error, NRMSE) of pressure field and velocity field (3D, 3-dimensional parameter) is less than 4% and 8%, respectively. The efficiency and accuracy of both 0D and 3D parameters’ prediction motivate the fast prediction of multi-scale simulation of the large-scale industrial system in the future.

Local well-posedness for 2D stochastic tropical climate model

In this paper, we consider a class of 2D tropical climate model under random perturbations, which is used to describe the interaction of large-scale flow fields and precipitation in the tropical atmosphere. Under some appropriate assumptions, we establish the local existence and uniqueness of the 2D stochastic tropical climate model. Moreover, we also discuss the blow-up criterion of the solution.

Storms and Precipitation Across the continental Divide Experiment (SPADE)

Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

StreamLNet: In-Situ Compression of Flow Field Combined with Streamline Visualization

In order to solve the difficulties of visualization and data storage caused by the huge volume of computational fluid dynamics data, an in-situ streamline extraction and velocity field compression method is proposed. Firstly, a uniform random method is used in situ to distribute and generate a certain number of streamlines in the computational fluid dynamics. Then, a deep neural regression network model StreamLNet is trained to build the mapping between this set of streamlines and velocity of any position in the flow flied. Finally, the original flow field data is discarded and only the streamlines and the StreamLNet model are stored for subsequent data visualization and flow field recovery. Experimental results on typical fluid simulation data show that proposed method can achieve a compression ratio of tens to hundreds of times for large-scale flow field at the cost of a relative error of 2% to 3%, in addition to streamline visualization of the filed at the same time.

Adaptive large-scale particle image velocimetry method for physical model experiments of flood propagation with complex flow patterns

This work presents an Adaptive Large Scale Particle Image Velocimetry method (ALSPIV), which measures surface velocity of a physical model experiment with complex flow pattern. In the experiment, the eleven low-cost, high-resolution surveillance cameras are adopted to achieve a large-scale flow field in real time. The lenses of them are shined perpendicularly on water surface to minimize perspective distortion. A camera calibration method is also designed to enhance measurement accuracy. In addition, an adaptive cross-correlation algorithm can contribute to cross-correlation and a lower signal-to-noise ratio. Finally, an experimental method is employed to verify the ALSPIV method’s accuracy, and the surface velocity distributions under three different steady flows are measured to demonstrate its applicability. Results show that the method is demonstrated to be a low-cost and automatic flow diagnostic tool and an accurate means of measuring surface velocities in the physical model experiments of flood propagation with complex flow patterns.

An enhanced hybrid deep neural network reduced-order model for transonic buffet flow prediction

In the transonic flight environment, transonic buffet has significant influence on the aerodynamic characteristics of aircraft. In the face of the strong nonlinear characteristics of transonic buffet, the reduced-order model (ROM) technology based on deep learning (DL) is introduced to study the flow field. In this article, an enhanced hybrid deep neural network (eHDNN) consisting of Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (ConvLSTM) neural network is constructed as the ROM of unsteady transonic buffet flow field. By mining the spatial-temporal characteristic of buffet flow directly from large-scale flow field data, the eHDNN can predict complex flow characteristics such as shock wave motion in the unsteady buffet flow. Structural similarity information and nonlinear improvement strategies are introduced to improve the nonlinear expression ability of eHDNN. The predicted result is consistent with the numerical simulation and has high degree of prediction accuracy. In addition, the eHDNN has good generalization ability, which can predict the spatial-temporal evolution of buffet flow field at unknown angles of attack. The prediction method has good application prospect of the transonic buffet flow control and prediction, which also provides experience for DL modeling in other complex and strongly nonlinear flows.

Dynamic Simulation and Analysis of Large-Scale Debris Flow Field

The method of two-phase flow is used to analyze the three-dimensional debris flow field via the theory of computational fluid dynamics (CFD). The theory of computational fluid dynamics and finite volume method is introduced briefly, and the rheological characteristic of non-Newtonian fluid is illuminated. The three-dimensional topography model of Niwan gully was established, and the three-dimensional debris flow field was analyzed. The values and distributions of velocity and pressure field of the debris flow have been obtained; the relationship between the shear strain rate, viscosity, and velocity was analyzed. The results suggest that it is more accurate to catch the free surface of debris flow using the method of two-phase flow. The sediment area of the Niwan gully debris flow can be estimated quickly, and it may have significance for the engineering prevention of debris flow disasters.

Narrative In Situ Visual Analysis for Large-Scale Ocean Eddy Evolution.

The rapid development of high-performance computing systems has led to a rapid increase in the speed of flow field simulation calculations. However, large-scale simulation output data lead to storage bottlenecks and inefficient data analysis. In this work, we used in situ visualization to process the simulation analysis of large-scale flow fields. Combined with narrative visual analysis, we designed a large-scale ocean flow field eddy evolution analysis system based on in situ visualization. Our system can generate high-precision eddy streamline structures in real time and supports eddy statistical analysis and tracking analysis at different ocean regional scales. Through the case data analysis of ocean simulation, we demonstrated the efficiency and effectiveness of the system.