Flow Structure based 3D Streamline Placement

Salzbrunn, Tobias; Scheuermann, Gerik

doi:10.1007/978-3-540-88606-8_7

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Digital models can be used similarly. As shown in the extensive reviews of flow visualization provided by Edmunds et al [12], McLoughlin et al [13] and Salzbrunn et al [14], there are different ways to communicate the wind properties resulting from a CFD simulation. Post et al [15] present four main groups of visualizations: (i) direct visualization, where raw data are communicated most directly, showing the fluid properties by colors or objects such as arrows; (ii) texture-based visualization, which uses textures to express density or displacement; (iii) geometric visualization, where objects such as streamlines are used to show the flow trajectory and a spatial definition.…”

Section: Visualization Of Wind Simulation Resultsmentioning

confidence: 99%

“…Finally, (iv) feature-based visualization is where data with similar characteristics are assembled to highlight the information of interest for the researcher. Salzbrunn et al [14] adds a fifth group, (v) partition-based visualization, which gives an overall result of the fluid behavior and its environment. According to Laramee et al [16] (i) is intuitive and easy to use.…”

Section: Visualization Of Wind Simulation Resultsmentioning

confidence: 99%

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Towards a Sensitive Urban Wind Representation in Virtual Reality

Giraldo

Servières

Moreau

2022

IJGI

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Wind can influence people’s behavior and their way of inhabiting an architectural or urban space. Furthermore, virtual reality (VR) enables the simulation of different physical and sensitive phenomena such as the wind. We aim to analyze the effects of different wind representations in terms of perception of its properties and sense of presence in VR. We carry out two within-subject studies aiming at evaluating different wind representation suggestions (including audiovisual and tactile stimuli) to identify their effects on wind properties’ perception and sense of presence in the VR scene. Our analysis showed significant effects of tactile restitution over the visual effects used in the study, both for understanding wind properties and for increasing the sense of presence in the VR scene. The tactile condition (T) reduced the estimation error of wind direction by 27% compared to the visual condition (V). The wind force error was reduced by 9.8% using (T) with (V). (T) increased the sense of presence by 12.2% compared to (V). Our second experiment showed an overestimation of the wind force perceived compared to the reference value of the Beaufort scale. For the maximum force value evaluated, the average result was 91% higher than the reference value, while for the lower, the average answer was 77% higher than the reference value. Previous studies have evaluated wind rendering in virtual reality, and others have studied the visualization of wind simulation results. To our knowledge, our study is the first to compare the perception of these two types of representations as well as the effects of wind on elements of the context. We also compared the wind perception to a reference-based method, the Beaufort scale.

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Section: Visualization Of Wind Simulation Resultsmentioning

confidence: 99%

Section: Visualization Of Wind Simulation Resultsmentioning

confidence: 99%

Towards a Sensitive Urban Wind Representation in Virtual Reality

Giraldo

Servières

Moreau

2022

IJGI

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“…We propose an algorithm that does not rely on planarity of the domain. Thus, we form a bridge between methods for planar 2D domains and intrinsic 3D methods, as suggested by Salzbrunn and Scheuermann [18], Mattausch et al [13], Ye et al [22], and Li and Shen [10]. Outline of algorithm behavior.…”

Section: Contributionsmentioning

confidence: 99%

Dual streamline seeding

Rosanwo

Petz

Prohaska

et al. 2009

2009 IEEE Pacific Visualization Symposium

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This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.

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“…Techniques can be classified as cluster-based or integral-line based methods. Salzbrunn et al [SJWS07] provide a good overview over these approaches. Methods from both fields have in common that they need a priory information about the structures that are to be found.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi‐fields

Jänicke

Böttinger

Tricoche

et al. 2008

Computer Graphics Forum

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Current unsteady multi-field simulation data-sets consist of millions of data-points. To efficiently reduce this enormous amount of information, local statistical complexity was recently introduced as a method that identifies distinctive structures using concepts from information theory. Due to high computational costs this method was IntroductionDue to the increase in compute power, computer simulations result in inceasingly large data sets. Often, the data is 3-dimensional, time dependent and multivariate. Visualization techniques can help reduce the giant amount of information provided by the data to the important facts. An example for the importance of such techniques is aircraft design. To reduce costs large parts of the development and testing of an aircraft are performed using computer simulations. Thus, flight quality and performance can be tested in many different scenarios. In order to analyze the behavior of the aircraft, developers have to extract relevant features from the simulation and visualize them. Relevant structures are for example the wake of an airplane or vortex breakdown. Understanding the wake of an airplane is important to decide at which intervals airplanes can start and land. Planes that enter the wake of a preceding flight might experience large turbulences that can cause a crash. Similarly if the bend that a delta wing flies is to sharp the vortex that keeps it in the air might break down causing a crash. Finding such structures early in the design process is an important task. In most application domains, scientific data visualization is used to understand both the mean features as well as unusual patterns in the data.A standard technique to detect relevant structures is feature extraction. Post et al. [PVH * 03] provide a detailed examination of techniques that are available in the field of flow visualization. The methods are divided into approaches based on image processing, vector field topology, physical characteristics and selective visualization. For a detailed explanation we refer the interested reader to this publication. Opposed to feature extraction methods that extract single formations, structure-based visualization focuses on a partitioning of the entire domain. Techniques can be classified as cluster-based or integral-line based methods. Salzbrunn et al.[SJWS07] provide a good overview over these approaches. Methods from both fields have in common that they need a priory information about the structures that are to be found. Moreover, most techniques were developed for a special type of field, e.g., scalar or vector valued. Thus, a precise definition of what is to be found is needed that cannot always be given. For example, there exists no general definition of a vortex.Recently Jänicke et al.[JWSK07] adopted local statistical complexity, a concept from information theory, and used the method to automatically detect structures in an unsteady multi-field that deviate from the average behavior in the field. Due to high computational costs, the method was so far...

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Flow Structure based 3D Streamline Placement

Cited by 4 publications

References 13 publications

Towards a Sensitive Urban Wind Representation in Virtual Reality

Towards a Sensitive Urban Wind Representation in Virtual Reality

Dual streamline seeding

Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi‐fields

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