Dense Flow Research Articles

Ordered/disordered monodisperse dense granular flow down an inclined plane: dry versus wet media in the capillary bridge regime

Ordered/disordered monodisperse dense granular flow down an inclined plane: dry versus wet media in the capillary bridge regime

Development of supercritical motion and internal jumps within lock-release radial currents and draining flows

When released from rest in a cylindrical reservoir, relatively dense fluid flows radially and may drain freely over an edge or may propagate along the underlying boundary as a gravity current. Here we show that in both scenarios the unsteady fluid motions develop the same supercritical state, a phenomenon that does not occur in equivalent two-dimensional flows and which strongly influences the ensuing dynamics, even at long times after the release. In particular, we demonstrate the onset and persistence of supercritical outflow when fluid drains over an edge, and the development of internal bores within gravity current flow.

Binary dense sift flow based two stream CNN for human action recognition

Binary dense sift flow based two stream CNN for human action recognition

Capturing both carrier and disperse phases in dense suspensions using harmonic ultrasound imaging velocimetry

In this study, a new ultrasound-based method for measuring both carrier-fluid and suspended-particle (disperse phase) motion is introduced. The method can extract flow quantities, such as slip velocity, from statistical information obtained in dense two-phase flows. The carrier fluid velocity is measured by leveraging the non-reflective properties of nearly-linear materials at harmonic frequencies to filter out the suspended particles. In contrast, the disperse phase velocity is measured using the ultrasound’s fundamental imaging mode and the slight impedance mismatch between the suspended particles and fluid. As a result, the carrier phase can be captured without cross-talk from the disperse phase. Finally, it is shown that the proposed technique allows for the measurement of suspension dynamics in vortex rings with high volume fractions on the order of 40%. Here, the proposed method is tested in dense suspensions, made from super-absorbent polymer beads with nearly-linear material properties mixed in deionized water, with volume fractions ranging from 20% to 40%.

Simulating dense granular flow using the μ(I)‐rheology within a space‐time framework

AbstractA space‐time framework is applied to simulate dense granular flow. Two different numerical experiments are performed: a column collapse and a dam break on an inclined plane. The experiments are modeled as two‐phase flows. The dense granular material is represented by a constitutive model, the (I)‐rheology, that is based on the Coulomb's friction law, such that the normal stress applied by the pressure is related to the tangential stress. The model represents a complex viscoplastic material behavior. The interface between the dense granular material and the surrounding light fluid is captured with a level set function. Due to discontinuities close to the the interface, the mesh requires a sufficient resolution. The space‐time approach allows unstructured meshes in time and, therefore a well‐refined mesh in the temporal direction around the interface. In this study, results and performance of a flat and a simplex space time discretization are verified and analyzed.

Monitoring of cyanobacterial blooms and assessing polymer-enhanced microfiltration and ultrafiltration for microcystin removal in an Italian drinking water treatment plant

The water intake of a drinking water treatment plant (DWTP) in Central Italy was monitored over six bloom seasons for cyanotoxin severity, which supplies drinking water from an oligo-mesotrophic lake with microcystin levels up to 10.3 μg/L. The historical data showed that the water temperature did not show extreme/large seasonal variation and it was not correlated either with cyanobacterial growth or microcystin concentration. Among all parameters, the cyanobacteria growth was negatively correlated with humidity and manganese and positively correlated with atmospheric temperature. No significant correlation was found between microcystin concentration and the climatic parameters. Polymer(chitosan)-enhanced microfiltration (PEMF) and ultrafiltration (PEUF) were further tested as an alternative microcystin removal approach from dense cyanobacteria-rich flows. The dominant cyanobacteria in the water intake, Planktothrix rubescens, was isolated and enriched to simulate cyanobacterial blooms in the lake. The PEMF and PEUF were separately applied to enriched P. rubescens culture (PC) (microcystin = 1.236 μg/L) as well as to the sand filter backwash water (SFBW) of the DWTP where microcystin concentration was higher than 12 μg/L. The overall microcystin removal rates from the final effluent of PC (always <0.15 μg/L) were between 90.1-94.7% and 89.5–95.4% using 4 and 20 mg chitosan/L, respectively. Meanwhile, after the PEMF and PEUF of SFBW, the final effluent contained only 0.099 and 0.057 μg microcystin/L with an overall removal >99%. The presented results are the first from the application of chitosan to remove P. rubescens as well as the implementation of PEMF and PEUF on SFBW to remove cyanobacterial cells and associated toxins.

Stress level effect on mobility of dry granular flows of angular rock fragments

Granular flows of angular rock fragments such as rock avalanches and dense pyroclastic flows are simulated numerically by means of the discrete element method. Since large-scale flows generate stresses that are larger than those generated by small-scale flows, the purpose of these simulations is to understand the effect that the stress level has on flow mobility. The results show that granular flows that slide en mass have a flow mobility that is not influenced by the stress level. On the contrary, the stress level governs flow mobility when granular flow dynamics is affected by clast agitation and collisions. This second case occurs on a relatively rougher subsurface where an increase of the stress level causes an increase of flow mobility. The results show also that as the stress level increases, the effect that an increase of flow volume has on flow mobility switches sign from causing a decrease of mobility at low stress level to causing an increase of mobility at high stress level. This latter volume effect corresponds to the famous Heim’s mobility increase with the increase of the volume of large rock avalanches detected so far only in the field and for this reason considered inexplicable without resorting to extraordinary mechanisms. Granular flow dynamics is described in terms of dimensionless scaling parameters in three different granular flow regimes. This paper illustrates for each regime the functional relationship of flow mobility with stress level, flow volume, grain size, channel width, and basal friction.

Modelling and Analysis of Facial Expressions Using Optical Flow Derived Divergence and Curl Templates

Facial expressions are integral part of non-verbal paralinguistic communication as they provide cues significant in perceiving one’s emotional state. Assessment of emotions through expressions is an active research domain in computer vision due to its potential applications in multi-faceted domains. In this work, an approach is presented where facial expressions are modelled and analyzed with dense optical flow derived divergence and curl templates that embody the ideal motion pattern of facial features pertaining to unfolding of an expression on the face. Two types of classification schemes based on multi-class support vector machine and k-nearest neighbour are employed for evaluation. Promising results obtained from comparative analysis of the proposed approach with state-of-the-art techniques on the Extended Cohn Kanade database and with human cognition and pre-trained Microsoft face application programming interface on the Karolinska Directed Emotional Faces database validate the efficiency of the approach.

Parameterization of Submesoscale Symmetric Instability in Dense Flows Along Topography

AbstractWe develop a parameterization for representing the effects of submesoscale symmetric instability (SI) in the ocean interior. SI may contribute to water mass modification and mesoscale energy dissipation in flow systems throughout the World Ocean. Dense gravity currents forced by surface buoyancy loss over shallow shelves are a particularly compelling test case, as they are characterized by density fronts and shears susceptible to a wide range of submesoscale instabilities. We present idealized experiments of Arctic shelf overflows employing the GFDL‐MOM6 in z* and isopycnal coordinates. At the highest resolutions, the dense flow undergoes geostrophic adjustment and forms bottom‐ and surface‐intensified jets. The density front along the topography combined with geostrophic shear initiates SI, leading to onset of secondary shear instability, dissipation of geostrophic energy, and turbulent mixing. We explore the impact of vertical coordinate, resolution, and parameterization of shear‐driven mixing on the representation of water mass transformation. We find that in isopycnal and low‐resolution z* simulations, limited vertical resolution leads to inadequate representation of diapycnal mixing. This motivates our development of a parameterization for SI‐driven turbulence. The parameterization is based on identifying unstable regions through a balanced Richardson number criterion and slumping isopycnals toward a balanced state. The potential energy extracted from the large‐scale flow is assumed to correspond to the kinetic energy of SI which is dissipated through shear mixing. Parameterizing submesoscale instabilities by combining isopycnal slumping with diapycnal mixing becomes crucial as ocean models move toward resolving mesoscale eddies and fronts but not the submesoscale phenomena they host.

The study on flow properties of nonconvex particles down a rough inclined plane

This study numerically examines the flow of nonspherical particles down a rough inclined plane based on the discrete element method. We use triangular, nonconvex particles to systematically explore the effect of their nonconvexity on the flow properties while weakening the impact of their orientation such that the degree of nonconvexity can be accurately varied. We first present a phase diagram of flow states in the parameter space of the angle of inclination and particle nonconvexity. The flow properties of nonconvex particles, especially the profiles of several characteristic quantities along the vertical direction of flow, are then examined in the steady flow regime. Finally, rheological models of the steady flow of nonconvex particles down a rough inclined plane are formulated based on the well-established laws of friction and dilatancy for spheres/disks, and particle nonconvexity is introduced to them as an independent parameter based on two rescaling functions. This proposed method to construct rheological models of nonspherical particles is expected to be applicable to dense flows of particles with more complex shapes and flow conditions.

Ecological network analysis to link interactions between system components and performances in multispecies livestock farms

Organic farming relies on ecological processes to replace chemical inputs, and organic farmers have developed various strategies, including several forms of diversification, to remain viable. Herein, we hypothesized that diversified organic farming systems can enhance their performance by increasing the level of interactions between system components. We therefore performed an ecological network analysis to characterize both within-farm and farm-environment interactions. Flows were expressed on an annual basis according to the quantity of biomass exchanges multiplied by nitrogen content. Seventeen organic farms were surveyed in French grassland areas, each associating beef cattle with either sheep, pigs, or poultry. The ecological network analysis was then coupled with the assessment of farm economic, environmental, and social performances. A hierarchical clustering on principal components distinguished five groups of farms based on farm and herd size, presence of monogastrics, percentage of crops in the farm area, and system activity indicators. A large farm size, in terms of area or number of workers, can limit the implementation of a homogeneous flow network within the system. A higher level of within-system interactions did not lead to better farm economic, environmental, and social performances. Systems with large monogastric production enterprises were highly dependent on inputs, which led to less homogeneous flow networks and a poor farm nitrogen balance without gaining economic efficiency. Managing a complex system with a dense and complex flow network did not appear to increase farmers’ mental workload. To our knowledge, this study is the first to quantify farm-scale interactions using ecological network indicators in temperate livestock farms and to analyze the links between farm performance and operating processes. The ecological network analysis thus potentially provides a common framework for comparing a wide range of livestock farms. Given the variability of multispecies livestock farms, a larger database will be used to extend our conclusions.

Investigation on ultralight particle dispersions in dense two-phase flow using a kinetic friction stress model

The graphite particle dispersion characteristics with irregular shapes of dense gas-solid turbulent flow in downer reactor is numerically investigated using an improved kinetic friction stress model and a adopted drag coefficient describing momentum transfer between gas and non-spherical particles. Hydrodynamic predictions are in better line with experimental results. Redistributions of Reynolds normal and shear stresses with strongly anisotropic dispersions are captured. Comparisons to glass bead, ultralight particles elongate the second acceleration length, gather the concentration peaks at near wall region, and weak the maximum dense ring. Maximum kinetic energy at fully development region is approximately 1.2 times larger than that of second acceleration region. Lower particle sphericities are contribution to the enhancement of particle velocity, particle concentration, particle shear stress, particle collision frequency, and granular temperature, except for the reduction of turbulent kinetic energy. The negative amplitudes of shear stresses at fully development regions are approximately 4.0 times larger than those of second acceleration regions.

Coarse-grained discrete element simulation of particle flow and mixing in a vertical high-shear mixer

There is a huge demand for the development of a coarse-grained model of a discrete element method (DEM) to perform a numerical simulation of a high-shear mixer with large-scale production. Recently, we developed a coarse-grained method for granular shear flow (CGSF) for coarse-graining dense granular shear flow. Using the CGSF, we here perform a coarse-grained DEM simulation of a vertical high-shear mixer at different coarse-graining ratios and different rotational speeds of the impeller. The simulation results with the CGSF are quite similar to those in the original case in terms of the particle velocity distribution, geometry of the moving powder bed, and particle mixing behavior. The CGSF is effective at various rotational speeds of the impeller. In particular, the CGSF exhibits high accuracy under a fully developed granular shear flow at a high rotational speed. We demonstrate the effectiveness of CGSF for coarse-grained DEM simulation of a high-shear mixer.

Automating the analysis of fish grazing behaviour from videos using image classification and optical flow

Studying and quantifying behaviour is important to understand how animals interact with their environments. However, manually extracting and analysing behavioural data from the large volume of camera footage collected is often time consuming. Deep learning techniques have emerged as useful tools in automating the analysis of certain behaviours under controlled or laboratory conditions, but the complexities of using raw footage from the field has resulted in this technology remaining largely unexplored as a possible data analysis alternative for animals in situ. Here, we use deep learning techniques to automate the analysis of fish grazing behaviour from real-world field imagery. We collected video footage in sea grass meadows in Queensland, Australia, and trained models on a training data set of over 3000 annotations. We used a combination of dense optical flow to assess pixel movement in underwater footage, spatiotemporal filtering to increase accuracy, and deep learning algorithms to classify grazing behaviour of luderick, Girella tricuspidata . When tested on novel videos the model had not seen in training, the model correctly identified nearly all individual grazing events. Deep learning shows promise as a viable tool for determining animal behaviour from underwater videos, and with further development offers an alternative to current time-consuming manual methods of data extraction. • Deep learning and dense optical flow can automate the analysis of behaviour. • This technique has a 92% accuracy in detecting grazing behaviour events. • Using spatiotemporal filtering postprocessing significantly increases performance. • Promising results call for further study into using automation of video analysis.

Frictional hyperspheres in hyperspace.

We extend the formulation of the discrete element method, which is typically used to simulate granular media, to describe arbitrarily large numbers of spatial dimensions and the collisions of frictional hyperspheres in these simulations. These higher dimensional simulations require complex visualization techniques, which are also developed here. Under uniaxial compression, we find that the stiffness of a granular medium is independent of the dimension for dimensions greater than one. In the dense flow regime, we show that the compressibility and frictional properties of higher dimensional granular materials can be described by a common rheology, with the main distinction between dimensions being the packing fraction. Results from these simulations extend our understanding of the effects of dimensionality on the behavior of granular materials, and on elastic and frictional properties in higher dimensions.

A resolved CFD-DEM method based on the IBM for sedimentation of dense fluid-particle flows

Motivated by the interest in accurate simulation of dense fluid-particle flows, a resolved CFD-DEM method is developed by incorporating computational fluid dynamics and discrete element method (CFD-DEM) with the immersed boundary method (IBM). In this approach, the fluid phase is simulated by the CFD while the individual particles are analyzed by means of the DEM. The direct-forcing IBM, which satisfies the no-slip boundary condition and the divergence-free condition simultaneously, is introduced to handle the interactions between rigid boundaries and fluid with inherent ease of tracking drastically moving geometries by a set of Lagrangian points. The strongly coupled system is then achieved through the successive iterative procedure. A significant advantage of the proposed method is that the fluid phase is fully resolved around the particle phase and as a result, for dense particulate flows where the interaction effects on either phase is considerably strong, the essential influence of the wake flow on particles could be reflected truthfully. The gravity-induced sedimentations of particles are performed to demonstrate the accuracy of the presented resolved method, and the comparison with the results obtained by the conventional unresolved CFD-DEM method is also performed which reveals that the proposed method shows a superiority of the precise description of the fluid-particle interactions over the conventional method especially when the dense particulate flows are involved.

Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

This paper presents a non-rigid biomedical image registration method based on Adaptive feature density flow (AFDF). Considering the muhiplicative characteristics of metadata signal for instance Magnetic Resonance Imaging (MRI) with high-resolution and high-sensitivity, the Non-Local Means Filtering Adapted to Rician Noise (NLMr) is employed to remove Rician noise rather than Gaussian noise with higher Peak Signal to Noise Ratio (PSNR). The deformation information and local shape information are homogeneous, so a flexible dimension of Fast Local Self-Similarity (FLSS) feature descriptor can be constructed to obtain adaptive FLSS (AFLSS) feature descriptor with adaptive Angle and Radial intervals. On the basis of AFLSS, the Adaptive feature density flow (AFDF) is proposed by calculating the dense feature flow field via Belief Propagation algorithm and thereby further optimized by the Pyramid iterative method. Finally, the image registration is completed by using the bicubic interpolation algorithm according to the dense feature flow field. Aiming at radiotherapy positioning control through precise robust registration of biomedical images, experiments of human brain MR images are carried out. The comparative results with PSNR, Root Mean Square Error (RMSE) and normalize mutual information (NMI) show that AFDF performs better than the optical flow method and Scale Invariant Feature Transform (SIFT) flow method, especially for the biomedical scenario with unconscious creep deformation.

Human action recognition using high-order feature of optical flows

Optical flow is widely used in human action recognition. However, the influence of complex background on optical flow often leads to low recognition efficiency. To deal with this issue, an optical flow-based physical feature-driven action recognition framework is proposed in this paper. We first calculate the original dense optical flow field. Then, for reducing computational burden, joint action relevance that can eliminate the pseudo-optical flow in complex background is developed. After that, a more state flow field is obtained by local spatial–temporal thermal diffusion processing. On this basis, we design a feature descriptor that takes the divergence, curl and gradient features of flow field into consideration. Finally, we adopt Fisher vector to encode descriptors for classification. Experimental on HMDB51, KTH and UCF101 datasets proves that actions in complex background can be recognized accurately by the proposed framework, which outperforms the already developed methods.

Side information hybrid generation based on improved motion vector field

The quality of side information has an important impact on the performance of a distributed video coding system. At present, the generation of side information is mainly based on the translational motion model using the inter-frame correlation for motion estimation. Considering that the generated side information is prone to block effect and ghosting, as well as the situation that the nonlinear motion is not fully considered and the intra-frame correlation is not fully utilized, a side information hybrid generation algorithm based on an improvement model for the motion vector field is proposed. The side information frame to be generated for the current Wyner-Ziv frame is divided into easy-to-estimate and difficult-to-estimate macroblocks. For difficult-to-estimate macroblocks, the Horn and Schunck dense optical flow method is used to generate reliable motion vectors, for easy-to-estimate macroblocks, the block matching method is used to generate unreliable motion vectors which are modified by the proposed scheme, and then, the improved motion vectors are used for motion compensation to produce the final side information frame. Experiment results show that the quality of side information obtained by using the improved motion vector field for motion compensation has been significantly improved, thus the overall performance of the distributed video coding system has been effectively improved.

Laboratory Landquakes: Insights From Experiments Into the High‐Frequency Seismic Signal Generated by Geophysical Granular Flows

AbstractGeophysical granular flows exert basal forces that generate seismic signals, which can be used to better monitor and model these severe natural hazards. A number of empirical relations and existing models link these signals' high‐frequency components to a variety of flow properties, many of which are inaccessible by other analyses. However, the range of validity of the empirical relations remains unclear and the models lack validation, owing to the difficulty of adequately controlling and instrumenting field‐scale flows. Here, we present laboratory experiments investigating the normal forces exerted on a basal plate by dense and partially dense flows of spherical glass particles. We measured the power spectra of these forces and inferred predictions for these power spectra from the models for debris flows' seismic signals proposed by Kean et al. (2015, https://doi.org/10.1002/2015GL064811), Lai et al. (2018, https://doi.org/10.1029/2018GL077683), and Farin, Tsai, et al. (2019, https://doi.org/10.1002/esp.4677), using Hertz theory to extend Farin, Tsai, et al. (2019)'s models to higher frequencies. Comparison of our observations to these predictions, and to predictions derived from Bachelet (2018) and Bachelet et al. (2021)'s model for granular flows' seismic signals, shows those of Farin, Tsai, et al. (2019)'s “thin‐flow” model to be the most accurate, so we examine explanations for this accuracy and discuss its implications for geophysical flows' seismic signals. We also consider the normalization, by the mean force exerted by each flow, of the force's mean squared fluctuations, showing that this ratio varies by 4 orders of magnitude over our experiments, but is determined by the bulk inertial number of the flow.