Journal of Visualization最新文献

Abstract

Streamline is one of the main methods for flow field visualization, which describes the distribution pattern of the flow field through the flow trajectory of seed points. Currently, most of the work focuses on seed point placement and streamline generation in feature regions. For context regions (blank areas), i.e., context regions without features, however, there is little research conducted. In fact, the context regions carry some flow field information, which can assist researcher in deeply understanding the entire spatial distribution of the flow field as well as the continuous transition between different feature regions. However, it is a challenging problem to generate suitable streamlines in context regions. If the streamlines are not positioned properly or have a too large number, they may severely occlude the feature regions, while too few streamlines may be difficult to fill in the entire information of the flow field. To address the problem, this article proposes a new method for seed point placement that mainly focuses on context regions. The method is divided into two steps: finding context regions and then placing seed points in context regions. Firstly, use 3D to 2D projection transformation and region connectivity algorithm to find context regions, where no feature streamlines pass through. The streamlines in a context region often have similar directions due to being away from critical points. Then, according to the direction of the streamlines, evenly place seed points in the 3D space. As a result, spatially uniform streamlines are generated to fill the context regions, which makes the flow field information more complete. Qualitative and quantitative evaluations show that the method proposed in this article can generate visually uniform streamlines in context regions, together with feature streamlines, which can help researchers to coherently understand the overall characteristics of the flow field.

Graphical Abstract

In a supersonic cylindrical isolator at Mach 2, the structures and frequency characteristics of shock train flow fields were experimentally studied by the schlieren measurement method. According to the design principle of parallel light through schlieren windows in a cylindrical duct, a high-precision conformal optical window pair was designed and integratively processed before. Based on a self-built pipeline structure with conformal windows in a direct-connect wind tunnel under adjustable back-pressure conditions, the shock surfaces in a cylindrical isolator at Mach 2 were first captured by the schlieren method. Then, the schlieren photographs were corrected by a nonlinear image transformation algorithm for the restoration of real shock train structures, and the experimental results were compared with numerical simulation results quantitatively. Finally, the shock train positions were calculated by an image recognition algorithm to analyze the self-excited oscillation frequency characteristics of shock train structures. The methods and experiments in this study enriched optical observation methods of supersonic flows through non-rectangular cross-section isolators in scramjet.

Graphical abstract

As data journalism continues to rise, narrative visualization has emerged as an essential method for conveying information. To improve the user experience of narrative visualization projects for data journalism, this study introduces an innovative approach for narrative visualization design centered on user experience. Firstly, through an in-depth analysis of existing research, we constructed a comprehensive user-experience-based narrative visualization model, considering the designers’ design process and the multiple levels of the user experience process. Then, through case analysis and user interviews, we identified the key elements that influence the user experience. Through the analysis of multiple cases, this study presents a practical narrative visualization design methodology comprising eight dimensions, aimed at enhancing user experience. The primary contribution of this research lies in the proposal of a practical narrative visualization model and the clear definition of key design elements, providing a comprehensive reference framework for designers and researchers to effectively optimize the user experience of narrative visualization. Moreover, our research findings unveil the inherent correlation between user experience and design elements, offering valuable insights for future research and practical applications.

Graphical Abstract

Abstract

To help people learn the touch-based gestures needed to perform various tasks, researchers commonly use training from an experimenter. However, it leads to dependence on a person, as well as memory problems with increasing number and complexity of gestures. Several on-demand training and feedback methods have been proposed that provide constant support and help people learn novel gestures without human assistance. Non-speech audio with the visual clue, a gesture training/feedback method, could be extended in the interactive visualization tools. However, the literature offers several options in the non-speech audio and visual clues but no comparisons. We conducted an online study to identify suitable non-speech audio representations with the visual clues of 12 touch-based gestures. For each audiovisual combination, we evaluated the thinking, time demand, frustration, understanding, and learnability of 45 participants. We found that the visual clue of a gesture, either iconic or ghost, did not affect the suitability of an audio representation. However, the preferences in audio channels and audio patterns differed for the different gestures and their directions. We implemented the training/feedback method in an Infovis tool. The evaluation showed significant use of the method by the participants to explore the tool.

Graphical Abstract

Abstract

The effect of secondary gas injection on the flow through an axisymmetric Mach 2 nozzle is studied experimentally. The experiments were conducted for a nozzle pressure ratio of 5. The secondary injection locations chosen for this study were L_s = 0.5L_d and 0.75L_d, and the secondary gas was injected at secondary pressure ratios (SPR) 0.5, 1.0, and 1.5. It is found that for injection at 0.5 L_d, the flow emanating is not deflected for all SPRs. For injection at 0.75 L_d, the flow is deflected by about 5.5⁰, 5.5⁰ and 10⁰, for SPRs 0.5, 1.0, and 1.5, respectively. These flow deflections are observed only in the plane of injection. The effect of secondary injection was not observed in the plane normal to the secondary injection.

Graphical abstract

For a large complex industrial equipment with high-density sensors, exploring the potential influence of generated multiregion monitoring parameters on subsequent control links can be very meaningful to optimize the control process. However, the influencing mechanism and randomness between such numerous monitoring parameters and subsequently influenced parameters are intertwined, and each working condition of the control system has its unique running characteristics and control rules, which makes it challenging to analyze the correlations between these different categories of parameter sets effectively. In this paper, we propose a comprehensive approach that combines parameter fusion and canonical correlation analysis for this kind of high-dimensional industrial control data and constructs a visual analysis framework CAPVis that supports multi-perspective and multi-level exploration of canonical correlation patterns. For a single working condition, we visualize the intricate structure inside of the canonical correlation relationships with a particular tripartite graph and evaluate the redundancy and stability of these relationships with multiple auxiliary views. For multiple working conditions, we design different visual comparison strategies to comprehensively compare the many-to-many canonical correlation patterns from local to global. Experiments on real industrial control datasets and feedback from industry experts demonstrate the effectiveness of CAPVis.

Graphical abstract

The atmospheric visibility is not only related to environmental quality and public health, but also has a significantly impact on industries such as navigation and aviation. The conventional Numerical weather prediction (NWP) method is run by a supercomputer with high computational cost. On the other hand, due to the inhomogeneity of the visibility distribution, most of machine learning models always analyze and predict visibility on a seasonal basis. To address these issues, we propose a visibility prediction model called BP-LGBM Combination Method (BLCM), which combines the Back Propagation (BP) neural network and the Light Gradient Boosting Machine (LGBM) classifier. By leveraging the advantages of regression and classification algorithms, this model achieves high accuracy predictions of visibility values while significantly reducing computation costs. Meanwhile, in order to resolve the seasonal issue, the data decision filtering process was proposed. It can output different categories of visibility prediction in any season, which expands the applicability of visibility forecasting to any period throughout the year. We also designed a visual analysis system for domain scientists to interactively explore the prediction results and their causes. Finally, the effectiveness of the proposed method has been demonstrated through several ablation experiments, contrast experiments and case studies.

Graphical abstract

Abstract

Recently, implicit neural representations (INRs) have demonstrated significant capabilities in handling 3D volume data, especially in the context of data compression. However, the majority of research has predominantly focused on structured grids, which are not commonly found in scientific domains, particularly in physics. To address this limitation, we propose an unstructured grid reduction method via implicit neural representation (UGINR). UGINR employs a divide-and-conquer approach; specifically, we segment the large-scale data into pieces based on values. Subsequently, we employ an INR network for each piece to learn its distinctive features. Finally, we integrate these individual networks to achieve the compression goal. To ensure compatibility with established research methods, we sample only the vertices of each cell in the unstructured grid. Through weight quantization, our model can achieve a high compression ratio. To illustrate the effectiveness of the proposed method, we conduct experiments on various datasets, demonstrating our approach’s robustness in scientific visualization and large-scale data compression.

Graphical abstract

Abstract

Deep learning techniques have been applied to the graph drawing of node-link diagrams to help figure out user preference of layout in recent research. However, when revisiting existing studies, only stress model and dimensional reduction methods are utilized in the unsupervised learning of graph drawing tasks since their gradient descent conditions can be easily constructed, and few works have explored their scalability on large graphs. In this paper, we propose a framework that can adapt most of the graph layout methods to a form of loss function and develop an implementation DeepFD, which takes the force-directed algorithm as the prototype to design the loss function. Our model is built with the graph-LSTM as encoder and multilayer perceptron as decoder and trained with dataset split from huge graphs with millions of nodes by Louvain. We design a set of qualitative and quantitative experiments to evaluate our method and compare with classical layout techniques such as F-R and K-K algorithms, while deep-learning based models with various architecture or loss function are adopted to perform ablation experiments. The results indicate that our developed approach can generate a high-quality layout of large graph within a low time cost, and the model we propose shows strong robustness and high efficiency.

Graphical abstract

The surface pressure field generated by a supersonic impinging jet on a vertical flat plate was measured using a pressure-sensitive paint (PSP) and a double-pulsed laser. The Mach number of the jet was (M_textrm{j}) = 1.23 and the position of the flat plate was h/D = 4.5. A significant peak at St = 0.41 (15.2 kHz) was observed in the spectra measured by a microphone, and unsteady pressure transducers. The feedback loop involved in the acoustic loading occurred at this frequency. Coherent structures of the flow were extracted by applying the azimuthal Fourier decomposition and the dynamic mode decomposition (DMD) to PSP images on the impingement plate. Axisymmetric modes expanding outward from the impingement point of the jet were observed for the azimuthal mode m = 0. Two helical modes related to the feedback loop were identified for |m| = 1. It was confirmed that the RMS values of the amplitudes of these DMD modes were larger than the other modes. The frequencies of these DMD modes were St = 0.39 (14.3 kHz) and St = 0.37 (13.7 kHz), respectively. Coherent structures associated with phenomena faster than 10 kHz were successfully extracted from PSP measurement data.

Graphical abstract