Interactive Exploration and Analysis of Pathlines in Flow Data

2013 IEEE Pacific Visualization Symposium (PacificVis), 2013

This paper applies Poisson-based methods to assist in interactive exploration of steady flow fields. Using data-driven deformations we obtain flow-orthogonal and flow-tangential surfaces by a fluxbased optimization. Surfaces are positioned interactively and deformed in real-time according to local flow. The deformed surfaces are particularly useful for defining seed structures. We show how the same gradient-based computational framework can be applied to obtain parametrizations of flow-aligned surfaces. This way it is easy to define nontrivial seed structures for integration-based flow visualization methods. Additionally, the flow-aligned parametrizations are employed for view-independent surface-based LIC visualizations. We apply our method to a number of data sets to show the effectiveness of our deformations and parametrization-based seed extraction methods for interactive flow exploration.

2006

Abstract The visualization of time-dependent simulation data, such as data sets from computational fluid dynamics (CFD) simulation, is still a very challenging task. In this paper, we present a new approach for the interactive visual analysis of flow simulation data, which is especially targeted at the analysis of time-dependent data. This supports the flexible specification and visualization of flow features in an interactive setup of multiple linked views.

Computer Graphics Forum, 2009

The quest for the ideal flow visualization reveals two major challenges: interactivity and accuracy. Interactivity stands for explorative capabilities and real-time control. Accuracy is a prerequisite for every professional visualization in order to provide a reliable base for analysis of a data set. Geometric flow visualization has a long tradition and comes in very different flavors. Among these, stream, path and streak lines are known to be very useful for both 2D and 3D flows. Despite their importance in practice, appropriate algorithms suited for contemporary hardware are rare. In particular, the adaptive construction of the different line types is not sufficiently studied. This work provides a profound representation and discussion of stream, path and streak lines. Two algorithms are proposed for efficiently and accurately generating these lines using modern graphics hardware. Each includes a scheme for adaptive time-stepping. The adaptivity for stream and path lines is achieved through a new processing idea we call "selective transform feedback". The adaptivity for streak lines combines adaptive timestepping and a geometric refinement of the curve itself. Our visualization is applied, amongst others, to a data set representing a simulated typhoon. The storage as a set of 3D textures requires special attention. Both algorithms explicitly support this storage, as well as the use of precomputed adaptivity information.

2000

ABSTRACT The investigation of flow data can be eased by the visualization of topological information about the flow. Especially, when empirical models or numerical results from flow simulation are investigated, often the first step of analysis is to search structural elements, like fixed points, separatrices, etc. The work presented in this paper focuses on the visualization of 3D dynamical systems (comparable to flow data) on the basis of results which are obtained by automatic analysis of the flow topology.

2012

Abstract Flow visualization is a well established branch of scientific visualization and it currently represents an invaluable resource to many fields, like automotive design, meteorology and medical imaging. Thanks to the capabilities of modern hardware, flow datasets are increasing in size and complexity, and traditional flow visualization techniques need to be updated and improved in order to deal with the upcoming challenges.

Computer Graphics Forum, 2021

Most existing unsteady flow visualization techniques concentrate on the depiction of geometric patterns in flow, assuming the geometry information provides sufficient representation of the underlying physical characteristics, which is not always the case. To address this challenge, this work proposes to analyse the time‐dependent characteristics of the physical attributes measured along pathlines which can be represented as a series of time activity curves (TAC). We demonstrate that the temporal trends of these TACs can convey the relation between pathlines and certain well‐known flow features (e.g. vortices and shearing layers), which enables us to select pathlines that can effectively represent the physical characteristics of interest and their temporal behaviour in the unsteady flow. Inspired by this observation, a new TAC‐based unsteady flow visualization and analysis framework is proposed. The centre of this framework is a new similarity measure that compares the similarity of ...

2003

Abstract Flow visualization has been a very attractive part of visualization research for a long time. Usually very large datasets need to be processed, which often consist of multivariate data with a large number of sample locations, often arranged in multiple time steps. Recently, the steadily increasing performance of computers again has become a driving factor for a reemergence in flow visualization, especially in techniques based on feature extraction, vector field clustering, and topology extraction.

2016

Flow plays a big role in the environmental sciences, but there are major differences between theoretically available and practically applied visualization techniques. This paper surveys various techniques in computational and environmental flow visualization, identifies challenges, and suggests strategic initiatives on how to bridge the gap.

2015 IEEE Pacific Visualization Symposium (PacificVis), 2015

When the spatial and temporal resolutions of a time-varying simulation become very high, it is not possible to process or store data from every time step due to the high computation and storage cost. Although using uniformly down-sampled data for visualization is a common practice, important information in the un-stored data can be lost. Currently, linear interpolation is a popular method used to approximate data between the stored time steps. For pathline computation, however, errors from the interpolated velocity in the time dimension can accumulate quickly and make the trajectories rather unreliable. To inform the scientist the error involved in the visualization, it is important to quantify and display the uncertainty, and more importantly, to reduce the error whenever possible. In this paper, we present an algorithm to model temporal interpolation error, and an error reduction scheme to improve the data accuracy for temporally down-sampled data. We show that it is possible to compute polynomial regression and measure the interpolation errors incrementally with one sequential scan of the time-varying flow field. We also show empirically that when the data sequence is fitted with least-squares regression, the errors can be approximated with a Gaussian distribution. With the end positions of particle traces stored, we show that our error modeling scheme can better estimate the intermediate particle trajectories between the stored time steps based on a maximum likelihood method that utilizes forward and backward particle traces. Scan each time step Full-resolution Time-varying Flow Field Down-sampled Flow Field Unreliable Pathlines Data Provider Client Storage End Positions of Particle Traces Analysis & Visualization Particle Tracing Sample once per n time steps Save once per n time steps

2012

Abstract Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis.