Multi-field Data Research Articles

Interactive Visual Analysis of Spatial Sensitivities.

Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications. For analyzing the spatial variation of parameter sensitivity, one needs to compute a spatial sensitivity scalar field per simulation parameter. Given n simulation parameters, we obtain multi-field data consisting of n scalar fields when considering all simulation parameters. We propose an interactive visual analytics solution to analyze the multi-field sensitivity data. It supports the investigation of how strongly and in what way individual parameters influence the simulation outcome, in which spatial regions this is happening, and what the interplay of the simulation parameters is. Its central component is an overview visualization of all sensitivity fields that avoids 3D occlusions by linearizing the data using an adapted scheme of data-driven space-filling curves. The spatial sensitivity values are visualized in a combination of a Horizon Graph and a line chart. We validate our approach by applying it to synthetic and real-world ensemble data.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

A Topological Similarity Measure Between Multi-Resolution Reeb Spaces.

Searching similarity between a pair of shapes or data is an important problem in data analysis and visualization. The problem of computing similarity measures using scalar topology has been studied extensively and proven useful in the shape and data matching. Even though multi-field or multivariate (consists of multiple scalar fields) topology reveals richer topological features, research on building tools for computing similarity measures using multi-field topology is still in its infancy. In the current article, we propose a novel similarity measure between two piecewise-linear multi-fields based on their multi-resolution Reeb spaces - a newly developed data-structure that captures the topology of a multi-field. Overall, our method consists of two steps: (i) building a multi-resolution Reeb space corresponding to each of the multi-fields and (ii) proposing a similarity measure between two multi-resolution Reeb spaces by computing a list of topologically consistent matching pairs (of nodes) and the similarity between them. We demonstrate the effectiveness of the proposed similarity measure in detecting topological features from real time-varying multi-field data in two application domains - one from computational physics and one from computational chemistry.

Evolution of interfacial formation and configuration control of bicomponent fiber during full spinning process

As a result of the limitation of bicomponent spinning in which it is difficult to obtain the interface evolution of the fiber forming process and guarantee performance stability, numerical simulation was carried out to investigate the effect of cooling, melt viscosity, and inlet flow rate on the interface throughout the process. The results showed that the position deviation of the interface only occurred inside the orifice and the interface distortion continued until after extrusion, with the maximum deformation occurring during extrusion swelling. Based on sufficient multifield data obtained from the simulation, it was demonstrated that the interfacial curvature was governed only by the viscosity difference between individual components, and the position of the interface was determined by the mechanical energy of the bicomponent melt. Moreover, the curvature and the position of the interface can be adjusted indirectly by changing the viscosity ratio, which can be regulated by the flow ratio, bringing guidance for the full process control of the bicomponent fiber preparation.

New approach for the digital reconstruction of complex mine faults and its application in mining

Visualization of complex geological structures can technically support the accurate prediction and prevention of coal mine disasters. This study proposed a new digital reconstruction method to visualize geological structures based on establishing a virtual model in the digital twin system. This methodology for the digital reconstruction of complex fault structures comprises the following four aspects: (1) collection and fidelity of multi-physical field data of the fault structures, (2) the transmission of multi-physical field data, (3) the normalization of multi-physical field data, and (4) digital model reconstruction of fault structures. The key scientific issues of this methodology to be resolved include in situ fidelity of multi-field data and normalized programming of multi-source data. In addition, according to the geological background and conditions in Da’anshan coal mine in western Beijing, China, a preliminary attempt is made to reconstruct a digital model of fault and fold structures using the methodology proposed in this study.

The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence

Abstract We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

Normalization processing and research of multi-field data in reservoir seepage investigation

Field analysis is one of the important methods to investigate the leakage of reservoir. Groundwater seepage field, groundwater temperature field, groundwater chemical field (conductivity field) and isotope field are widely used in leakage of reservoir. In this paper, the indexes of a single field are normalized according to some factors, including the relationship between field indexes and leakage sources, the relationship between field indexes and natural field background values of geological media, the variation of indexes in adjacent space, and the changes of indexes at different times. After processing, the characteristic values of the single field are obtained, including the tracer index characteristic value, the background index characteristic value, the gradient index characteristic value and the time series index characteristic value. Based on the reliability of the data and the correlation with the leakage, different weighting factors can be selected for each characteristic value to obtain a normalized field comprehensive index characteristic value. The indexes of different fields can also be superimposed according to the normalized characteristic value to realize the comparison and verification of each field. It makes full use of the limited data thus to reducing the interference information and amplifying the leakage signal. And it makes the field analysis method of groundwater leakage quantitative and systematic, which can effectively process the field data and more significantly delineate the possible groundwater leakage location.

Multi-source relational data fusion

Focusing on the problem of relational data fusion in the environment with “information isolated island”, this paper presents a multi-sources relational data fusion (MSF) framework. The framework consists of three components: schema matching, entity alignment, and entity fusion. Based on the Hungarian algorithm, we propose an alignment discovery mechanism for the attributes alignment among multi-sources relational data. By extracting the multi-dimensional features of attribute values, we efficiently realized schema matching of multi-sources relational data. To link the tuple pairs from multi-source data, we introduced the diversity sampling strategy and the entity feature extraction approach. These can effectively improve the performance of entity alignment. Finally, linked entities are fused to provide a unified view of data analysis. To verify the usefulness and efficiency of the proposed methods, we implemented a fusion system called Data Puzzle, which is verified with the real public multi-field data. Experimental results demonstrate that the proposed methods can fuse multi-source relational data efficiently with high recall and precision.

Parallel visualization of large-scale multifield scientific data

Following the recent rapid growth in supercomputer performance, many real-world problems in fields such as nuclear fusion energy and electromagnetic environments can be solved via multiphysics simulation, which outputs multifield datasets. However, current multifield visualization has difficulty handling multiphysics parallel simulation data. First, it is difficult to correctly visualize overlapping multifield data with semitransparent properties because of the complex distribution of partitioned data domains across multicore processors. Second, the interactive visualization performance of large-scale multifield data in serial processing mode on a personal computer is often slow because multiphysics simulations can produce large-scale datasets, i.e., of the order of gigabytes to terabytes. Considering the fidelity and efficiency of large-scale data visualization on supercomputer, a new parallel visualization method is required for multifield scientific data that do not change the original distribution of the mesh data generated by the multiphysics applications. This paper introduces a hybrid scheduling framework for the parallel visualization of large-scale multifield scientific data. This framework is used to overcome problems both in correct visual representation and in efficient visualization of large-scale multiphysics applications. We discuss the results of several typical multiphysics applications to verify the feasibility and reliability of our proposed framework. This framework currently supports scalable in situ visualization of up to 8.5 billion mesh cells on the 10 k cores of China’s Tianhe-2 supercomputer, which could help domain scientists understand multiphysics phenomena more clearly and accurately.

Pipeline–permafrost interaction monitoring system along the China–Russia crude oil pipeline

In-situ monitoring system is absolutely necessary for a long-distance linear pipeline infrastructure in permafrost regions due to the complicated interaction between buried pipeline and its harsh permafrost environment. To study this complex problem, a comprehensive in-situ monitoring system has been designed and established along the China–Russia Crude Oil Pipeline (CRCOP), which is one of vital energy channels for importing crude oil from Russia to China. This monitoring system can record the meteorological data, ground temperature and water content within the permafrost foundation, vertical settlement of the oil pipe, ground surface deformation on the pipeline right-of-way (on-ROW), as well as the thaw bulb around the pipeline at four sites with different engineering geological conditions. In this paper, the layout and instrumentation of the whole monitoring system and monitoring methods are described in detail, and then the thermal regime of permafrost on-ROW and off-ROW and cooling performance of the mitigative measures on foundation soils are evaluated. Results show that heat dissipated from the CRCOP lead to the rapid degradation of permafrost on-ROW and produce different sizes of thaw bulb around the oil pipelines. The mitigative measures, including insulation layer, two-phase closed thermosyphon and U-shaped air-ventilated pipes, are effective to minimize permafrost degradation and ensure the thermal stability of permafrost foundation along the CRCOP. Some recommendations are proposed for future research and for the monitoring system to be improved. This monitoring system can provide multi-field data to clarify the interaction between pipeline and permafrost degradation under different geological conditions, and guide the design and maintenance of the buried warm pipeline in permafrost regions.

Visual Analysis of Aneurysm Data using Statistical Graphics.

This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.

A Review of Geophysical Exploration Technology for Mine Water Disaster in China: Applications and Trends

Geophysical exploration can be effective in detecting and monitoring potential sources of coal mine water in-rushes and underground watercourses. Generally, in-mine seismic, DC resistivity, and transient electromagnetic methods are used for such purposes in China. However, such technologies can be influenced by many factors, such as roadways, fissures in the surrounding rocks, and various secondary conditions. Our review of current geophysical methods and tools concludes that further basic research should be carried out on geophysical field propagation in the whole space, data collection methods, and inversion methods appropriate for the special environment of coal mines. Moreover, borehole and roadway space should be designed to incorporate effective geophysical drilling, cross-hole exploration, drilling–roadway exploration, and roadway–roadway exploration. Future hydrogeophysical exploration research should focus on comprehensive geophysical methods combining multi-field synergistic observations with multi-field data integration and automatic monitoring as well as early warning systems for mine water disasters combining real-time processing and analysis of exploration equipment with Internet of Things technology.

Direct Multifield Volume Ray Casting of Fiber Surfaces.

Multifield data are common in visualization. However, reducing these data to comprehensible geometry is a challenging problem. Fiber surfaces, an analogy of isosurfaces to bivariate volume data, are a promising new mechanism for understanding multifield volumes. In this work, we explore direct ray casting of fiber surfaces from volume data without any explicit geometry extraction. We sample directly along rays in domain space, and perform geometric tests in range space where fibers are defined, using a signed distance field derived from the control polygons. Our method requires little preprocess, and enables real-time exploration of data, dynamic modification and pixel-exact rendering of fiber surfaces, and support for higher-order interpolation in domain space. We demonstrate this approach on several bivariate datasets, including analysis of multi-field combustion data.

An efficient and visually accurate multi-field visualization framework for high-resolution climate data

Recent advances in high-performance computing technologies, applied in climate science, are allowing increases in model complexity, model resolution, and the number of simulations run. The interactive exploration and analysis of these complex, multi-field climate data sets have been identified as one of the major current challenges in scientific visualization. For example, without direct 3D multi-field visualization, it is difficult to recognize the important correlative effects between vertical wind velocities and transport of the volumetric atmosphere. As such data have complicated 3D structures and are highly time-dependent, a visualization approach must handle these dynamic data in a highly interactive way. In this paper, an efficient multi-field visualization framework is proposed for Earth climate simulation data. A novel visualization pipeline is presented for on-demand data processing, enabling scalable handling of large-scale climate data sets. The hardware-accelerated multi-field visualization method used in the framework allows interactive and accurate visualization of multiple intersecting climate phenomena. An information-theoretic-based wind-field analysis method is also implemented within the visualization framework to help scientists gain a deeper understanding of the underlying multi-field climate data.

Multi-field Pattern Matching based on Sparse Feature Sampling.

We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts.

New Ajax Post Method for Multi-Field Data Including Rich Text

A new Ajax (Asynchronous JavaScript And XML) post (NAP) method for multi-field data including rich text is presented. By reasonable construction of multi-field data structure and using a new server-side analytical algrithm,the method cleverly avoids abstacles which arises in a conventional Ajax post (CAP) method.Test results show that the method is more stable and reliable than CAP method and performs much better than a traditional form post (TFP) method obviously in saving network flux and reducing response time.

A Gradient‐Based Comparison Measure for Visual analysis of Multifield Data

AbstractWe introduce a multifield comparison measure for scalar fields that helps in studying relations between them. The comparison measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. Results from the visual analysis of various data sets from climate science and combustion applications demonstrate the effective use of the measure.

Errata to "Relation-Aware Isosurface Extraction in Multifield Data"

. The sentence “The profile peaks in the temperature range 14,000-16,000 K, which is the temperature range in which hydrogen is ionized” should read as “The profile peaks at approximately 10,000K.” . The sentences “We get similar results for ionized helium ðA 1⁄4 fTCf;HeþgÞ. In fact, we get a sharper spike in the range 14,000-15,000 K (Fig. 8c)” should be replaced by the sentence “We get similar results for ionized helium ðA 1⁄4 fTCf;HeþgÞ (Fig. 8c).” REFERENCES

Physically-Based Interactive Flow Visualization Based on Schlieren and Interferometry Experimental Techniques

Understanding fluid flow is a difficult problem and of increasing importance as computational fluid dynamics (CFD) produces an abundance of simulation data. Experimental flow analysis has employed techniques such as shadowgraph, interferometry, and schlieren imaging for centuries, which allow empirical observation of inhomogeneous flows. Shadowgraphs provide an intuitive way of looking at small changes in flow dynamics through caustic effects while schlieren cutoffs introduce an intensity gradation for observing large scale directional changes in the flow. Interferometry tracks changes in phase-shift resulting in bands appearing. The combination of these shading effects provides an informative global analysis of overall fluid flow. Computational solutions for these methods have proven too complex until recently due to the fundamental physical interaction of light refracting through the flow field. In this paper, we introduce a novel method to simulate the refraction of light to generate synthetic shadowgraph, schlieren and interferometry images of time-varying scalar fields derived from computational fluid dynamics data. Our method computes physically accurate schlieren and shadowgraph images at interactive rates by utilizing a combination of GPGPU programming, acceleration methods, and data-dependent probabilistic schlieren cutoffs. Applications of our method to multifield data and custom application-dependent color filter creation are explored. Results comparing this method to previous schlieren approximations are finally presented.

Relation-Aware Isosurface Extraction in Multifield Data

We introduce a variation density function that profiles the relationship between multiple scalar fields over isosurfaces of a given scalar field. This profile serves as a valuable tool for multifield data exploration because it provides the user with cues to identify interesting isovalues of scalar fields. Existing isosurface-based techniques for scalar data exploration like Reeb graphs, contour spectra, isosurface statistics, etc., study a scalar field in isolation. We argue that the identification of interesting isovalues in a multifield data set should necessarily be based on the interaction between the different fields. We demonstrate the effectiveness of our approach by applying it to explore data from a wide variety of applications.