A particle system for interactive visualization of 3D flows

We present a particle system for interactive visualization of steady 3D flow fields on uniform grids. For the amount of particles we target, particle integration needs to be accelerated and the transfer of these sets for rendering must be avoided. To fulfill these requirements, we exploit features of recent graphics accelerators to advect particles in the graphics processing unit (GPU), saving particle positions in graphics memory, and then sending these positions through the GPU again to obtain images in the frame buffer. This approach allows for interactive streaming and rendering of millions of particles and it enables virtual exploration of high resolution fields in a way similar to real-world experiments. The ability to display the dynamics of large particle sets using visualization options like shaded points or oriented texture splats provides an effective means for visual flow analysis that is far beyond existing solutions. For each particle, flow quantities like vorticity magnitude and A2 are computed and displayed. Built upon a previously published GPU implementation of a sorting network, visibility sorting of transparent particles is implemented. To provide additional visual cues, the GPU constructs and displays visualization geometry like particle lines and stream ribbons.     

A Survey on Multimodal Medical Data Visualization

Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview visualizations guide physicians to suspicious regions to be analysed in detail, e.g. with slice‐based viewing. We give an overview of state of the art in multi‐modal medical data visualization techniques. Multi‐modal medical data consist of multiple scans of the same subject using various acquisition methods, often combining multiple complimentary types of information. Three‐dimensional visualization techniques for multi‐modal medical data can be used in diagnosis, treatment planning, doctor–patient communication as well as interdisciplinary communication. Over the years, multiple techniques have been developed in order to cope with the various associated challenges and present the relevant information from multiple sources in an insightful way. We present an overview of these techniques and analyse the specific challenges that arise in multi‐modal data visualization and how recent works aimed to solve these, often using smart visibility techniques. We provide a taxonomy of these multi‐modal visualization applications based on the modalities used and the visualization techniques employed. Additionally, we identify unsolved problems as potential future research directions.     

Feature Driven Combination of Animated Vector Field Visualizations

Animated visualizations are one of the methods for finding and understanding complex structures of time-dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image-based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection-and-classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.     

Particle Video: Long-Range Motion Estimation Using Point Trajectories

This paper describes a new approach to motion estimation in video. We represent video motion using a set of particles. Each particle is an image point sample with a long-duration trajectory and other properties. To optimize particle trajectories we measure appearance consistency along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and cannot be directly obtained using existing methods such as optical flow or feature tracking. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformations.     

Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data

The purpose of multi‐run simulations is often to capture the variability of the output with respect to different initial settings. Comparative analysis of multi‐run spatio‐temporal simulation data requires us to investigate the differences in the dynamics of the simulations' changes over time. To capture the changes and differences, aggregated statistical information may often be insufficient, and it is desirable to capture the local differences between spatial data fields at different times and between different runs. To calculate the pairwise similarity between data fields, we generalize the concept of isosurface similarity from individual surfaces to entire fields and propose efficient computation strategies. The described approach can be applied considering a single scalar field for all simulation runs or can be generalized to a similarity measure capturing all data fields of a multi‐field data set simultaneously. Given the field similarity, we use multi‐dimensional scaling approaches to visualize the similarity in two‐dimensional or three‐dimensional projected views as well as plotting one‐dimensional similarity projections over time. Each simulation run is depicted as a polyline within the similarity maps. The overall visual analysis concept can be applied using our proposed field similarity or any other existing measure for field similarity. We evaluate our measure in comparison to popular existing measures for different configurations and discuss their advantages and limitations. We apply them to generate similarity maps for real‐world data sets within the overall concept for comparative visualization of multi‐run spatio‐temporal data and discuss the results.     

Clustering revealed in high‐resolution simulations and visualization of multi‐resolution features in fluid–particle models

Simulating natural phenomena at greater accuracy results in an explosive growth of data. Large‐scale simulations with particles currently involve ensembles consisting of between 10⁶ and 10⁹ particles, which cover 10⁵–10⁶ time steps. Thus, the data files produced in a single run can reach from tens of gigabytes to hundreds of terabytes. This data bank allows one to reconstruct the spatio‐temporal evolution of both the particle system as a whole and each particle separately. Realistically, for one to look at a large data set at full resolution at all times is not possible and, in fact, not necessary. We have developed an agglomerative clustering technique, based on the concept of a mutual nearest neighbor (MNN). This procedure can be easily adapted for efficient visualization of extremely large data sets from simulations with particles at various resolution levels. We present the parallel algorithm for MNN clustering and its timings on the IBM SP and SGI/Origin 3800 multiprocessor systems for up to 16 million fluid particles. The high efficiency obtained is mainly due to the similarity in the algorithmic structure of MNN clustering and particle methods. We show various examples drawn from MNN applications in visualization and analysis of the order of a few hundred gigabytes of data from discrete particle simulations, using dissipative particle dynamics and fluid particle models. Because data clustering is the first step in this concept extraction procedure, we may employ this clustering procedure to many other fields such as data mining, earthquake events and stellar populations in nebula clusters. Copyright © 2003 John Wiley & Sons, Ltd.     

Visual Analysis of the Finite-Time Lyapunov Exponent

In this paper, we present an integrated visual analytics approach to support the parametrization and exploration of flow visualization based on the finite-time Lyapunov exponent. Such visualization of time-dependent flow faces various challenges, including the choice of appropriate advection times, temporal regions of interest, and spatial resolution. Our approach eases these challenges by providing the user with context by means of parametric aggregations, with support and guidance for a more directed exploration, and with a set of derived measures for better qualitative assessment. We demonstrate the utility of our approach with examples from computation fluid dynamics and time-dependent dynamical systems.     

A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization

The model of a Double Gyre flow by Shadden et al. is a standard benchmark data set for the computation of hyperbolic Lagrangian Coherent Structures (LCS) in flow data. While structurally extremely simple, it generates hyperbolic LCS of arbitrary complexity. Unfortunately, the Double Gyre does not come with a well‐defined ground truth: the location of hyperbolic LCS boundaries can only be approximated by numerical methods that usually involve the gradient of the flow map. We present a new benchmark data set that is a small but carefully designed modification of the Double Gyre , which comes with ground truth closed‐form hyperbolic trajectories. This allows for computing hyperbolic LCS boundaries by a simple particle integration without the consideration of the flow map gradient. We use these hyperbolic LCS as a ground truth solution for testing an existing numerical approach for extracting hyperbolic trajectories. In addition, we are able to construct hyperbolic LCS curves that are significantly longer than in existing numerical methods.     

Phase Space Projection of Dynamical Systems

Dynamical systems are commonly used to describe the state of time-dependent systems. In many engineering and control problems, the state space is high-dimensional making it difficult to analyze and visualize the behavior of the system for varying input conditions. We present a novel dimensionality reduction technique that is tailored to high-dimensional dynamical systems. In contrast to standard general purpose dimensionality reduction algorithms, we use energy minimization to preserve properties of the flow in the high-dimensional space. Once the projection operator is optimized, further high-dimensional trajectories are projected easily. Our 3D projection maintains a number of useful flow properties, such as critical points and flow maps, and is optimized to match geometric characteristics of the high-dimensional input, as well as optional user constraints. We apply our method to trajectories traced in the phase spaces of second-order dynamical systems, including finite-sized objects in fluids, the circular restricted three-body problem and a damped double pendulum. We compare the projections with standard visualization techniques, such as PCA, t-SNE and UMAP, and visualize the dynamical systems with multiple coordinated views interactively, featuring a spatial embedding, projection to subspaces, our dimensionality reduction and a seed point exploration tool.     

Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots

Scatter plots are a powerful and well-established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real-world data sets and validate its effectiveness in a user study.     

Diamagnetic particle focusing using ferromicrofluidics with a single magnet

Focusing particles into a tight stream is critical to many applications such as microfluidic flow cytometry and particle sorting. Current magnetic field-induced particle focusing techniques rely on the use of a pair of repulsive magnets, which makes the device integration and operation difficult. We develop herein a new approach to focusing nonmagnetic particles in ferrofluid flow through a T-microchannel using a single permanent magnet. Particles are deflected across the suspending ferrofluid by negative magnetophoresis and confined by a water flow to the center plane of the microchannel, leading to a focused particle stream flowing near the bottom channel wall. Such three-dimensional diamagnetic particle focusing is demonstrated in a sufficiently diluted ferrofluid through both the top and side views of the microchannel. As the suspended particles can be visualized in bright field, this magnetic focusing method is expected to find applications to label-free (i.e., no magnetic or fluorescent labeling) cellular focusing in lab-on-a-chip devices.     

Adaptive particles for incompressible fluid simulation

We propose a particle-based technique for simulating incompressible fluid that includes adaptive refinement of particle sampling. Each particle represents a mass of fluid in its local region. Particles are split into several particles for finer sampling in regions of complex flow. In regions of smooth flow, neighboring particles can be merged. Depth below the surface and Reynolds number are exploited as our criteria for determining whether splitting or merging should take place. For the fluid dynamics calculations, we use the hybrid FLIP method, which is computationally simple and efficient. Since the fluid is incompressible, each particle has a volume proportional to its mass. A kernel function, whose effective range is based on this volume, is used for transferring and updating the particle’s physical properties such as mass and velocity. Our adaptive particle-based simulation is demonstrated in several scenarios that show its effectiveness in capturing fine detail of the flow, where needed, while efficiently sampling regions where less detail is required.     

Two-dimensional time-dependent vortex regions based on the acceleration magnitude

Acceleration is a fundamental quantity of flow fields that captures Galilean invariant properties of particle motion. Considering the magnitude of this field, minima represent characteristic structures of the flow that can be classified as saddle- or vortex-like. We made the interesting observation that vortex-like minima are enclosed by particularly pronounced ridges. This makes it possible to define boundaries of vortex regions in a parameter-free way. Utilizing scalar field topology, a robust algorithm can be designed to extract such boundaries. They can be arbitrarily shaped. An efficient tracking algorithm allows us to display the temporal evolution of vortices. Various vortex models are used to evaluate the method. We apply our method to two-dimensional model systems from computational fluid dynamics and compare the results to those arising from existing definitions.     

Selective Visualization of Vector Fields

In this paper, we present an approach to selective vector field visualization. This selective visualization approach consists of three stages: selectdon creation, selection processing and selective visualization mapping. It is described how selected regions, called selections, can be represented and created, how selections can be processed and how they can be used in the visualization mapping. Combination of these techniques with a standard visualization pipeline improves the visualization process and offers new facilities for visualization. Examples of selective visualization of fluid flow datasets are provided.     

基于物理模型的实时喷泉水流运动模拟

本文基于流体动力学和粒子系统给出了一个模拟实时喷泉水流运动的方法。     

Advanced object-oriented methods and concepts for simulations of multi-body systems

A common way to simulate the motion and behaviour of a complex system is to approximate it with a multi-body system based on particles, and to evaluate the particle interactions over time. Normally, the calculations require such a large amount of computer power that methods have been developed to decrease computation time by taking advantage of new computer architectures. These methods have been in use since the early 1980s, but there is still a lack of concepts supporting the application of these methods, to build highly efficient simulation systems with reusable entities. In this paper we describe a new approach for the development of flexible, efficient methods and techniques to control multi-body systems. The major goal of our concept is the integration of advanced modern simulation techniques such as particle-based modelling and simulated annealing, and efficient data structures such as octrees and the Greengard algorithm. We introduce high-level concepts represented by agents, environments, and controllers based on and supported by the object-oriented paradigm. We demonstrate the flexibility of our new simuation system for applications related to environmental research and air quality control.     

Adaptive Surface Visualization of Vessels with Animated Blood Flow

The investigation of hemodynamic information for the assessment of cardiovascular diseases (CVDs) gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics (CFD) simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi‐transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.     

槽流拟颗粒模型的并行算法

将流体处理为离散粒子,应用拟颗粒硬球模型来研究槽流中的流动现象,与分子动力学模拟的算法类似,是研究槽流机理的一种行之有效的方法。为了作大规模的模拟,本文采用区域分解算法和消息传递编程模型技术,将该模型串行程序并行化,应用一维划分、单相传递的方法简化了并行算法,采用轮换搜索法来避免硬球碰撞次序对结果的影响。在可扩展的机群系统上用实例计算,通过与串行程序的对比,验证了并行程序的正确性,表明本文设计的并行算法取得了较高的并行计算效率。     

A model‐based development approach for the verification of real‐time Java code

Many real‐time systems are safety‐and security‐critical systems and, as a result, tools and techniques for verifying them are extremely important. Simulation and testing such systems can be exceedingly time‐consuming and these techniques provide only probabilistic measures of correctness. There are a number of model‐checking tools for real‐time systems. Although they provide formal verification for models, we still need to implement these models. To increase the confidence in real‐time programs written in real‐time Java, this paper proposes a model‐based approach to the development of such programs. First, models can be mechanically verified, to check whether they satisfy particular properties, by using current real‐time model‐checking tools. Then, programs can be derived from the model by following a systematic approach. We introduce a timed automata to RTSJ Tool (TART), a prototype tool to automatically generate real‐time Java code from the model. Finally, we show the applicability of our approach by means of four examples: a gear controller, an audio/video protocol, a producer/consumer and the Fischer protocol. Copyright © 2011 John Wiley & Sons, Ltd.