Topology-Based Flow Visualization, The State of the Art

Laramee, Robert S.; Hauser, Helwig; Zhao, Lingxiao; Post, Frits H.

doi:10.1007/978-3-540-70823-0_1

Cited by 142 publications

(89 citation statements)

References 80 publications

(83 reference statements)

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“…Extracting and visualizing turbulence in vector fields has long been of interest to the visualization community [8]. Methods generally focus on either extracting specific structures (e.g., vortices) and drawing a bounding volume [15], or extracting the overall topology and visualizing it through glyphs or other proxies [18].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Extraction and Quantification of Geophysical Vortices

Williams

Petersen

Bremer

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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(a) (b) (c) Fig. 1. Three views of the tornado data set. (a) Arrows pointing along streamlines of velocity reveal the basic structure of the tornado. However, we are primarily interested in its vortex core, and specifically, where the vortex core resembles a Gaussian vortex. (b) The Q-criterion for the tornado data set, as expected, shows a vortical interior surrounded by a strain cell, where the spinning air of the tornado shears against the calmer air outside. The vortex core is brought out in the strong negative domain of Q, but an additional vortical funnel appears in the upper half of the tornado. (c) For our method, we compute the similarity of different Q thresholds to an idealized Gaussian vortex. The similarity holds well on the interior, where the flow of air resembles an idealized vortex, but quickly decays outside the core. Additionally, the funnel on top is not modeled well by Gaussian vorticity (except near the interface between the funnel and the main core), so it also has a low fit value and is excluded.Abstract-We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geometry of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real data from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.

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Section: Related Workmentioning

confidence: 99%

Adaptive Extraction and Quantification of Geophysical Vortices

Williams

Petersen

Bremer

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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“…3, which demonstrates that the particles tend to fl ow towards two zones and they cannot escape. Here, dead-zones are attracting attention according to the fl ow topology classifi cation [18] based on the eigenvalues of the rate-ofdeformation tensor [19]. Figure 3 also shows an isosurface of 50 h as LMA of the wastewater.…”

Section: Aeration Tankmentioning

confidence: 99%

Investigation of residence time distribution and local mean age of fluid to determine dead-zones in flow field

Karches¹,

Buzás²

2013

Int. J. CMEM

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Residence time and local mean age of fl uid are important parameters of the fl uid fl ow, with which we are able to detect the functioning and non-functioning zones. It is a novel approach to estimate the effect of dead-zones on chemical and biological processes. In this paper, we introduce a numerical tracer study, which is calibrated by fi eld measurements and which is applied in different cases such as settling tank and aeration tank of wastewater treatment. Using an advective-diffusive passive scalar transport equation, the age of the fl uid can be calculated. It also shows the short-circuits and dead-zones of the fl uid and this corresponds with our observations. This method helps eliminate potential hydraulic problems at the early stage of design. Keywords: CFD, dead-zone, local mean age, tracer study. INTRODUCTIONSettling tanks in the wastewater treatment have a key role in the separation of suspended solids and sludge fl ocks. The design of these tanks is based on the ATV-A131 standard [1], which takes into account only the global parameters of the fl ow, e.g. average residence time, but in many cases the inequalities inside the tank may have serious effect on effl uent quality. In this paper, we are fi rst and foremost interested in the detection of dead-zones, where the mixing is adverse due to low velocity, high residence time and fl uid age. In aeration tanks, for example, anaerobic zones may develop because microorganisms consume the oxygen. There is mass transfer between the fl ow and dead-zone because of the presence of turbulence; however, this is much less than the effect of convective transport. On the other hand, hydraulically inactive zones are redundant and these increase the cost of operation. Computational fl uid dynamics is widely applied in wastewater treatment tank design [2-4]) because with the knowledge of hydraulic phenomena, the understanding of biochemical processes could be improved. One signifi cant linking parameter between the hydraulics and biology is residence time; therefore, it is essential to know the precise value for every point. Residence time distribution (RTD) can be measured experimentally [5]. In pulse experiment, the tracer is mixed in a small volume of fl uid, which is introduced to the reactor in a very short time interval (Dirac-delta function) and the response function is measured at the outlet. The tracer should be a conservative material without any background concentration of the wastewater. The response function shows the changes in tracer concentration in the course of time, from which we can estimate the hydraulic behavior of the tank. RTD calculations can be performed numerically, and they can be compared to the results of the fi eld measurement. This paper deals with circular fl ow reactors, one aeration tank and one radial fl ow settling reactor. The fi rst is designed as a fully mixed reactor, but the RTD investigation demonstrated that there is a short-circuit, where the nutrients could fl ow through the reactor rapidly and did not have enoug...

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“…feature-based flow visualization by Post et al and Laramee et al [Laramee et al (2007);Post et al (2003)]. …”

Section: Feature-based Flow Visualizationmentioning

confidence: 99%