Showing papers in "Journal of Visualization in 2020"

Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods

Abstract: A nanofluid containing copper (Cu) nanoparticles was simulated in a rectangular cavity using computational fluid dynamic (CFD). The upper and lower walls of the cavity were adiabatic, while the right and left walls had warm and cold temperatures, respectively. This temperature difference causes a thermal flow from the right wall to the left wall. The elements of the coordination system in different directions, including velocity in the Y direction (V) and fluid temperature, were obtained using CFD. Adaptive network-based fuzzy inference system (ANFIS) was used to train the CFD outputs and provided artificial flow field and temperature distribution along the cavity domain. The CFD outputs were used as input and output data for the ANFIS method. The position of the fluid layer in X and Y computing directions and fluid velocity (Y axis) were used as three inputs, and the fluid temperature was taken as the output in the ANFIS method training process. The data were categorized using fuzzy c-means clustering, and different numbers of clusters were taken as a key parameter in this method. Using the fuzzy inference system, it is possible to predict the nodes in the cavity not generated through CFD simulation so that different coordination of the fluid at these points can be computed. Using ANFIS method, it is possible to reduce the computation time of CFD method so that more nodes are predicted in a shorter period of time, while clustering method can enhance the computing time for each neural cell. The ANFIS method can also visualize the flow in the cavity and display the thermal distribution along with the heat source.

Visualizing surrogate decision trees of convolutional neural networks

Abstract: Interpreting the decision-making of black boxes in machine learning becomes urgent nowadays due to their lack of transparency. One effective way to interpret these models is to transform them into interpretable surrogate models such as decision trees and rule lists. Compared with other methods that open the black boxes, rule extraction is a universal method which can theoretically extend to any black boxes. However, in practice, it is not appropriate for deep learning models such as convolutional neural networks (CNNs), since the extracted rules or decision trees are too large to interpret and the rules are not at the semantic level. These two drawbacks limit the usability of rule extraction for deep learning models. In this paper, we adopt a new strategy to solve the problem. We first decompose a CNN into a feature extractor and a classifier. Then extract the decision tree only from the classifier. Then, we leverage lots of segmented labeled images to learn the concepts of each feature. This method can extract human-readable decision trees from CNNs. Finally, we build CNN2DT, a visual analysis system to enable users to explore the surrogate decision trees. Use cases show that CNN2DT provides global and local interpretations of the CNN decision process. Besides, users can easily find the misclassification reasons for single images and the discriminating capacity of different models. A user study has demonstrated the effectiveness of CNN2DT on AlexNet and VGG16 for image classification.

Novel jet impingement atomization by synchronizing the sweeping motion of the fluidic oscillators

Abstract: Graphic abstract

Design guidelines for augmenting short-form videos using animated data visualizations

Abstract: Short-form videos are an increasingly prevalent medium for storytelling in journalism and marketing, of which information can be greatly enhanced by animated data visualizations. However, there is no prior research that systematically investigates how to augment such short videos with data visualizations in an effective way. We conducted a design workshop with experienced video, animation designers and visualization experts to discuss principles and practices for augmenting short-form videos with data visualizations. After the workshop, we summarized the participants’ design considerations and proposed 20 design guidelines. We further collected design purposes of the participants and associated these purposes with the guidelines. Finally, we conducted a crowd-sourcing study and a task-based evaluation to validate the effectiveness and usability of the guidelines. Results indicate that our guidelines can significantly improve the videos accompanied with data visualizations and help novices easily obtain desired knowledge when augmenting videos.

A systematic literature review of modern software visualization

Abstract: We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013–2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.

High-speed visualization and image processing of sub-atmospheric water boiling on a transparent heater

Abstract: The paper is devoted to the processing and analysis of the high-speed visualization data on water pool boiling in the pressure range of 55–103 kPa The study of the vapor bubbles dynamics and evolution of void fraction near a heated wall during boiling was performed using a special design of a transparent heating element and high-speed visualization from its bottom side To analyze the wide array of video data, automatic image processing programs were developed As a result, a detailed statistical analysis of the growth curves and departure diameters of vapor bubbles during boiling at different pressures was carried out It was shown that at ultra-low pressure 55 kPa after the departure of massive vapor bubbles the pulsating boiling regime was occurred A method based on the estimation of void fraction near a heated wall was proposed and implemented for the description of this cyclic boiling regime at low sub-atmospheric pressure

Novel impinging jets-based non-periodic sweeping jets

Abstract: Graphic abstract

Experimental study on oblique water entry of two tandem spheres with collision effect

Abstract: In this paper, the hydrodynamic characteristics associated with oblique water entry of two tandem spheres were experimentally investigated using high-speed photography. The results indicated that in the cavity formed by the first sphere, a suction effect due to the pressure difference between inside and outside the cavity was developed, which consequently led the second sphere to accelerate when it was totally submerged in the cavity. In addition, the first sphere was accelerated with a sudden expansion of the cavity radius due to the energy transfer effect associated with the collision of the two spheres, with a ring joint geometry observed, and the cavity finally broke up into two sections. Since the second sphere moved inside the cavity with less drag, it finally reached the first sphere and a second collision event was observed.

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Abstract: Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.

Visualization of two-dimensional liquid sheets issued into subsonic gaseous crossflow

Abstract: Flow dynamics of two-dimensional liquid sheets discharged into low-speed gaseous crossflow were experimentally investigated. The flow characteristics of liquid sheets were visualized by taking advantage of diffused backlight shadowgraphy and high-speed photography. Three injectors with an equal thickness of 0.35 mm and aspect ratios of 30, 60 and 90 were manufactured and tested at different flow conditions. A full discussion about the flow characteristics of two-dimensional liquid sheets in the presence of transverse airflow is provided. Visualizations revealed that the liquid sheets represented a unique concave-like structure that was named as inflated sheet. This special characteristic was not previously seen on any other circular or non-circular liquid jets and therefore made the flow dynamics of liquid sheets in subsonic crossflow very distinguished. The inflated sheet was found to transform from an enclosed structure into an open structure. The open inflated sheet was disturbed by different breakup mechanisms including sheet rupture, bag breakup, and Rayleigh–Taylor instability. Based on the observed phenomena, the flow was grouped into five regimes including biconvex, enclosed inflated sheet, open inflated sheet, bag breakup/sheet rupture, and multimode breakup. Furthermore, it was found that the droplet region was bifurcated due to the different breakup mechanisms acting simultaneously upon the sheet. Measurements of sheet trajectory were performed and the effects of momentum ratio and Weber number were studied. It was found that Weber number was only effective at low values, while momentum ratio remarkably impacted the trajectory.

Numerical study of the pulsatile flow depending on non-Newtonian viscosity in a stenosed microchannel

Abstract: Considering the shear-thinning feature of blood viscosity, the characteristics of non-Newtonian fluids are important in pulsatile blood flows. Stenosis, with an abnormal narrowing of the vessel, blocks blood flow to downstream tissues and leads to plaque rupture. In smaller arteries of diameters up to a few hundred micrometers, such stenosis can result in severe consequences. Therefore, a systematic analysis of the blood flow around the stenosed microchannel is important. In this study, non-Newtonian behaviors of the blood flow around a microchannel of diameter 500 μm, with 60% severe stenosis, were examined using CFX under pulsatile flow condition, with a period of 1 s and Reynolds number of 14.025 at the systolic phase. The viscosity information of the two non-Newtonian samples and the used pulsatile profile were based on our previous study. For comparison, water at room temperature was used as the Newtonian fluid. During the pulsatile phase, wall shear stress (WSS) is highly oscillated. In the case of the water flow, the recirculation occurred downstream the stenosis. This recirculation made the vortex structures travel the longest and induced a low WSS distribution and rapid normalized pressure drop at downstream of the stenosis. Conversely, the non-Newtonian feature of viscosity made flow structures almost symmetric, with respect to the stenosis. However, the highly oscillating WSS enhances the tendency of plaque instability and damage to the endothelium. Our findings on the influence of blood viscosity on stenotic lesions may help clinicians understand relevant mechanisms.

A clustering-based approach to vortex extraction

Abstract: Since vortex is an important flow structure and has significant influence on numerous industrial applications, vortex extraction is always a research hotspot in flow visualization. This paper presents a novel vortex extraction method by employing a machine learning clustering algorithm to identify and locate vortical structures in complex flow fields. Specifically, the proposed approach firstly chooses an objective, physically based metric that describes the vortex-like behavior of intricate flow and then normalizes the metric for applying on different flow fields. After that, it performs the clustering on normalized metric to automatically determine vortex regions. Our method requires relatively few flow variables as inputs, making it suitable for vortex extraction in large-scale datasets. Moreover, this approach detects all vortices in the flow simultaneously, thereby showing great potential for automated vortex tracking. Extensive experimental results demonstrate the efficiency and accuracy of our proposed method in comparison with existing approaches.

Laser array beam propagation through liver tissue

Abstract: Laser array beam propagating through mouse liver tissue is investigated. The turbulence power spectrum of the liver tissue is employed in the extended Huygens–Fresnel method to obtain an optical intensity profile and beam broadening at the observation point in biological liver tissue. Variations of the beam profile and the beam broadening are simulated based on the number of beamlets, source size, wavelength and the ring radius of the array. A biological tissue, illuminated by the laser array beam, exhibits different beam profiles and beam spot radius variations when the number of beamlets, source size, wavelength and the ring radius of the laser array beam are varied. Examining these variations observed in the propagated optical beam and comparing them with the test cases, abnormalities such as cancer and tumor in a biological liver tissue can be diagnosed.

Visualization of nanofluid flow field by adaptive-network-based fuzzy inference system (ANFIS) with cubic interpolation particle approach

Abstract: The interior of a square cavity containing nanofluid with copper (Cu) nanoparticles (NPs) was simulated by a computational fluid dynamics (CFD) method. The flow rate parameters were obtained with different directions of the computing elements and temperature of the fluid inside the cavity. The CFD outputs were studied using an adaptive-network-based fuzzy inference system (ANFIS) to create an artificial fluid flow domain. The CFD outputs were used as the input and output data in the ANFIS method. Subtractive clustering was applied used for data clustering to look at the impact of the cluster influence range on the performance of the ANFIS method. After the highest level of performance was reached, the cavity nodes that were not involved in the learning process were predicted. Very good accordance was observed between the ANFIS method prediction and the results of the CFD method. The ANFIS method reduced the calculation time dramatically compared to the CFD method and has the ability to predict far more nodes in a short period of time. The results show that the clustering framework can visualize the flow pattern in the square-shaped cavity in a short time. Additionally, the combination of CFD and smart modeling enables us to specifically analyze and visualize one part of a fluid structure without several complex CFD analyses.

Visual analytics of urban transportation from a bike-sharing and taxi perspective

Abstract: The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies. The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies.

A new toolbox to distinguish the sources of spatial memory error.

Abstract: Studying the sources of errors in memory recall has proven invaluable for understanding the mechanisms of working memory (WM). While one-dimensional memory features (e.g., color, orientation) can be analyzed using existing mixture modeling toolboxes to separate the influence of imprecision, guessing, and misbinding (the tendency to confuse features that belong to different memoranda), such toolboxes are not currently available for two-dimensional spatial WM tasks. Here we present a method to isolate sources of spatial error in tasks where participants have to report the spatial location of an item in memory, using two-dimensional mixture models. The method recovers simulated parameters well and is robust to the influence of response distributions and biases, as well as number of nontargets and trials. To demonstrate the model, we fit data from a complex spatial WM task and show the recovered parameters correspond well with previous spatial WM findings and with recovered parameters on a one-dimensional analogue of this task, suggesting convergent validity for this two-dimensional modeling approach. Because the extra dimension allows greater separation of memoranda and responses, spatial tasks turn out to be much better for separating misbinding from imprecision and guessing than one-dimensional tasks. Code for these models is freely available in the MemToolbox2D package and is integrated to work with the commonly used MATLAB package MemToolbox.

Visualization of Lagrangian fluid transport of a vortex ring based on time-resolved PIV

Abstract: We carry out experiments on vortex ring flows submerged underwater which are generated by a pulsatile circular jet with the stroke ratio of 10. The pulsatile Reynolds number, based on centerline exit velocity and jet diameter, is a cosinusoidal function of time with a mean part of 2000 and an oscillating part of 820. This axisymmetric flow field is measured in a meridian symmetry plane by time-resolved planar particle image velocimetry. For revealing the interaction among the starting vortex ring, the trailing jet and the ambient quiescent fluid, we apply finite-time Lyapunov exponent in the Lagrangian framework to the Eulerian-based PIV dataset. By tracking trajectories of typical virtually color-dyed fluid parcels, we obtain clear visualizations of the entrainment and detrainment processes by the vortex ring, the advection transport of the trailing jet and the advection transport of trailing shear layer, during the longitudinal propagation of the vortex ring.

Exploring eye movement data with image-based clustering

Abstract: In this article, we describe a new feature for exploring eye movement data based on image-based clustering To reach this goal, visual attention is taken into account to compute a list of thumbnail images from the presented stimulus These thumbnails carry information about visual scanning strategies, but showing them just in a space-filling and unordered fashion does not support the detection of patterns over space, time, or study participants In this article, we present an enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended To also indicate the temporal sequence of the thumbnails, we add color-coded links and further visual features to dig deeper in the visual attention data The usefulness of the technique is illustrated by applying it to eye movement data from a formerly conducted eye tracking experiment investigating route finding tasks in public transport maps Finally, we discuss limitations and scalability issues of the approach

On the aerodynamic flow around a cyclist model at the hoods position

Abstract: Aerodynamic flow around an 1/5 scale cyclist model was studied experimentally and numerically. First, measurements of drag force were performed for the model in a low-speed wind tunnel at Reynolds numbers from $$5.5 \times 10^{4}$$ to $$1.8 \times 10^{5}$$. Meanwhile, numerical computation using a large eddy simulation method was performed at three Reynolds numbers of $$1.1 \times 10^{4}$$, $$6.5 \times 10^{4}$$ and $$1.5 \times 10^{5}$$ to obtain the drag coefficients for comparison. Second, flow visualization was made in a water channel and the wind tunnel mentioned to examine the three-dimensional flow separation pattern on the model surface, which could also be realized from the numerical results. Finally, a wake flow survey based on the hot-wire measurements in the wind tunnel showed that in the near-wake region, the flow was featured with the formation of multiple streamwise vortices. The numerical results further indicated that these vortices were evolved from the separated flows occurred on the model surface.

Interaction of loosely packed bubbly flow passing through a pore space

Abstract: The interactions of the bubbles in a loosely packed bubbly flow in a high viscous fluid approaching a pore space are studied using a shadowgraph imaging technique. The motion of the bubbles has been evaluated by considering shape analysis of their deformation and the variation in the velocity and pressure distribution of the phase. A comparison of two cases of a linear array and a matrix of bubbles emphasizes the importance of the arrangement on the deformation and motion of the dispersed phase in the pore space. The deformation of the bubbles in both cases results in a deceleration and acceleration process of the dispersed phase in the pore region. This process was a function of size, number of the bubbles competing in the pore throat and the arrangement of the competing bubbles. The variation in the motion of the dispersed phase will ultimately lead to different flow motion and phenomena at the entrance of the pore throat. The results also highlight that although bubbles had different motion approaching the pore throat, they follow similar deformation transition as they enter and exit the pore throat. This work contributes to existing knowledge of multi-phase flow in pore space by providing further understanding the effect of the interaction of phases based on the arrangement and their motion in a porous geometry.

Experimental study on fluid selection for a stable Taylor cone formation via micro-PIV measurement

Abstract: In this study, the visualization of the flow inside a Taylor cone formed during an electrohydrodynamic (EHD) spraying is conducted to analyze its stability among five liquid candidates. A micro-PIV with a micro-nozzle is used for the visualization, and the physical properties as well as measured values are utilized in the analysis. First, in forming the Taylor cone, the electrohydrodynamic force is required to be sufficiently large in order to overcome the surface tension of the liquid. Thus, among the five liquids tested here, three, in this case IPA, EtOH, and MeOH, form a Taylor cone due to the relatively low surface tension levels as compared to the others. Once electrohydrodynamic jetting occurs, the average and maximum velocities become monotonically proportional to the average current. As the velocities are the smallest in using IPA, the circulation flow becomes superior to the extrusive flow, which yields the stable formation of a Taylor cone. Also, low fluctuation of the instantaneous currents supports the stable formation of IPA. Consequently, IPA shows the most stable formation of the Taylor cone in our condition due to the lowest average current and low-level surface tension. Eventually, micro-PIV would be a good tool in choosing an optimal fluid for stable EHD spraying.

Visualization and analysis of muzzle flow fields using the Background-Oriented Schlieren technique

Abstract: Several experimental and numerical studies on muzzle blast and flow fields have been performed. However, due to the extremely short duration and the spatiotemporal evolution of these flows, experimental quantitative techniques are limited. As a consequence, the number of validated numerical calculations is limited as well. On the other hand, despite the development of computer models that have succeeded in predicting the measured pressure and velocity, they show unrealistic temperatures and densities. Therefore, temperature and/or density measurements are required to validate these codes, thus the motivation of this research. The present paper focuses on the development of a density-sensitive and non-intrusive measurement technique and the implementation of a quantitative flow visualization method based on Background-Oriented Schlieren (BOS) combined with a high-speed camera. In BOS, the experimental setup of conventional Schlieren (mirrors, lenses, and knife-edge) is replaced by a background pattern and a single digital camera. The muzzle flow fields and the flow field around a 5.56-mm projectile in flight were successfully visualized. Indeed, the implemented experimental high-speed BOS setup has demonstrated its ability to capture clearly the salient features of the precursor and the propellant gas flow fields and their interactions. The captured structures such as vortex, barrel shock, Mach disk, and blast wave show a good agreement with that issued from a realized conventional Schlieren setup and the bibliography, confirming the BOS capability to visualize complex density flow fields.

Evaluation of aerodynamic performance enhancement of Risø_B1 airfoil with an optimized cavity by PIV measurement

Abstract: Airfoils are mostly inefficient in their off-design conditions. In order to improve the aerodynamic performance of airfoils in these conditions, using an optimized cavity on airfoils as a passive method can be useful. In this study, a cavity on a Riso_B1_18 airfoil, which is used as a wind turbine airfoil, was optimized at an off-design angle of attack by incorporating a genetic algorithm into a RANS flow solver. For the cavity optimization, the geometry and downstream suction surface were defined by 16 parameters, and the lift-to-drag ratio was considered as the cost function at 14° angle of attack. The numerical solution showed that the optimized cavity traps a vortex, which postpones the stall. Due to the uncertainty of CFD especially at off-design conditions, it was necessary to evaluate the performance of the optimized cavity in a wide range of angles of attack. This study used the particle image velocimetry (PIV) measurement method to evaluate the improved flow structures over the optimized cavity. Two models of airfoils with and without the cavity were made of aluminum and installed inside the test section of an open-jet wind tunnel with an air speed of 30 m/s and a cross section of 30 × 30 cm2. The air flow on the suction side of the airfoils was measured at 7°–15° angles of attack by PIV. A comparison between the measured flow fields over the two airfoils showed that the optimized cavity postpones the stall angle by 3°. Furthermore, the cavity increases the momentum behind the airfoil at the angles of attack greater than 9°. After this angle, a further increase in the angle of attack increases the difference between the momentums behind the airfoils with and without cavity. The Riso_B1_18 airfoil with the optimized cavity can be used as a wind turbine airfoil at high angles of attack to increase the stall angle and decrease the instability and fluctuation at off-design conditions.

On the application of non-standard rainbow schlieren technique upon supersonic jets

Abstract: A quantitative rainbow schlieren study was conducted on an over-expanded jet at nozzle pressure ratio of 2.8, based on two different schlieren set-ups: the standard z-type and a single-mirror schlieren set-up. The technique used a single, weak focal-length lens placed in the field of view of the system to provide the calibration information required for the extraction of the quantitative data. In the case of the single-mirror set-up, the calibration image required further post-processing procedures to take into account the double refraction experienced by the light. Density gradients were calculated using Abel transform and compared to validated reference data. Results indicate that the single-mirror set-up is able to improve prediction of the density gradient field as compared to the standard z-type schlieren, due to its inherent property of higher sensitivity. The study has shown that the single-mirror set-up performs on average better than the standard z-type system, yielding an overall averaged error of ± 20%, with localized values as low as ± 5% where the shock cell structure is clearly defined, with respect to the validated reference data. At the same time, both systems perform poorly in regions where the flow structure displays poor image contrast.

Unsteady skin-friction field estimation based on global luminescent oil-film image analysis

Abstract: A global luminescent oil-film (GLOF) image analysis method to estimate unsteady skin-friction fields in an unsteady flow field is proposed and demonstrated A governing equation describing the dynamics of the oil film (the thin-oil-film equation) is employed for the unsteady oil-film images The frequency response of the oil-film movement is analyzed, and a cutoff frequency is defined as a function of the oil-film thickness and the kinematic oil viscosity The estimating skin-friction vector is defined along with a spatiotemporal weighted window and obtained by solving the overdetermined system of the thin-oil-film equation The system can be solved by using the weighted linear least-squares method, and the time-resolved skin-friction field can be estimated The time-resolved GLOF image analysis method is demonstrated on an experiment of a junction flow on a flat surface with a square cylinder The GLOF images in the Karman vortex shedding bounding the flat surface were acquired, and the time-resolved skin-friction fields were obtained The results showed that fluctuation in the skin-friction vectors corresponds to the shedding frequency, and the vortices bounding the surface were extracted The averaged skin-friction field is compared with the result of the previous study based on the time-independent model The normalized skin friction from both methods showed good agreement, which indicates that the quantitative value will be obtained when a calibration process is involved in a future study

Flow transitions in collisions between vortex-rings and density interfaces

Abstract: Flow transitions and vortical developments during vortex-ring collisions with a sharp water–oil density interface are studied using planar laser-induced fluorescence and time-resolved particle-image velocimetry techniques. Circular vortex-rings at Reynolds numbers of $$\hbox {Re}=1000, 2000$$ and 4000 colliding with a density interface characterized by an Atwood number of approximately $$A=0.045$$ were investigated. Results show that at $$\hbox {Re}=1000$$ , collision with the density interface produces vortical structures and flow transitions that are relatively similar to those for a solid-boundary collision. However, the dynamics underlying the present vortical formations and behaviour are different from those associated with solid-boundary collisions, in that the former are driven by baroclinic vorticity generation. Flow behaviour at $$\hbox {Re}=2000$$ shows more significant deformation of the density interface by the vortex-ring but overall behaviour remains comparable. Last but not least, at $$\hbox {Re}=4000$$ , the largest Reynolds number investigated here, the vortex-ring penetrates the density interface almost completely. However, buoyancy effects eventually limit its penetration and reverse its translational direction, such that it crosses back into the oil layer again with its vortex core rotational senses reversed as well. At the same time, vortex-ring fluid is shed and a significant trailing-jet is left in the former’s wake.

AirExplorer: visual exploration of air quality data based on time-series querying

Abstract: Air pollution has become an important environmental issue, attracting more and more attention from many scholars and experts recently. Understanding air quality patterns in urban areas is essential for air pollution prevention and treatment. However, most existing studies usually cannot effectively capture air quality patterns from large-scale air quality data, due to lacking effective interaction approaches and intuitive methods that reveal sequential and multivariable information. In this paper, we present AirExplorer, a novel visual analysis system providing abundant interactive ways and intuitive views to help users explore the time-varying and multivariable patterns of air quality data. We design a time-embedded RadViz view that not only shows the relationship between data and multivariable attributes, but also puts the air quality temporal variations among the observation stations into perspective. Furthermore, we suggest a time-series querying algorithm, which combines hierarchical Piecewise Linear Representation and Dynamic Time Warping, to help users query time-series patterns of interest accurately by a sketch-based interaction. The experiment results based on the real dataset demonstrate that our method can help users understand the spatial-temporal multi-dimensional characteristics effectively and discover some potential laws of air quality patterns. AirExplorer with easy-to-use interactions can improve the efficiency of analyzing air quality data.

Visualization of shock wave propagation due to underwater explosion

Abstract: Researchers have a great interest in determining the characteristics of shock wave propagation by an underwater explosion But it involves complex physical processes near boundaries during the underwater explosion The major issues are explosions, fluid interactions, fluid–structure interactions, and shock wave propagation In general, the underwater explosion leads to shock wave propagation and it moves toward two types of boundaries The first one is the free surface of the water, and the second is fluid–structure interaction The underwater explosion also deals with bulk cavitation behind shock waves This paper deals with the shock wave propagation and bulk cavitation because of the underwater explosion near the free surface The computation and analytical results of the underwater explosion were discussed and compared Also, polynomial and Mie–Gruneisen (shock) equation of state (EOS) of water was implemented to determine the incompressible nature of water and to get realistic results The results of both the EOS of water were compared against each other and validated against analytical results These results show that the equation of state of water and water–air interface has a significant influence on the characteristics of the shock wave propagation and unsteady cavitation near the free surface

Visual analytics and prediction system based on deep belief networks for icing monitoring data of overhead power transmission lines

Abstract: In this paper, a system is proposed for visualizing and analyzing icing monitoring data of power transmission lines. The distributions of temperature and humidity are visualized by two-dimensional maps with customizable map layers. The multi-dimensional monitoring data are visualized as parallel coordinates. Moreover, a prediction algorithm that is based on a hybrid deep belief network is integrated into the system for predicting the icing thickness. If the icing thickness of a certain location exceeds the threshold value, the historical meteorological data of the location can be visualized as line graphs, which helps to choose the appropriate de-icing measures. According to the experimental results, our system is capable of reflecting the statistical features of icing monitoring data with high accuracy of icing thickness prediction.

Flow characteristics in a model of a left ventricle in the presence of a dysfunctional mitral mechanical heart valve

Abstract: Graphic abstract