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Lagrangian Coherent Structures (LCS) have become a widespread and powerful method to describe dynamic motion patterns in time-dependent flow fields. The standard way to extract LCS is to compute height ridges in the Finite Time Lyapunov Exponent (FTLE) field. In this work, we present an alternative method to approximate Lagrangian features for 2D unsteady flow fields that achieves subgrid accuracy without additional particle sampling. We obtain this by a geometric reconstruction of the flow map using additional material constraints for the available samples. In comparison to the standard method, this allows for a more accurate global approximation of LCS on sparse grids and for long integration intervals. The proposed algorithm works directly on a set of given particle trajectories and without additional flow map derivatives. We demonstrate its application for a set of computational fluid dynamic examples, as well as trajectories acquired by Lagrangian methods, and discuss its
benefits and limitations.
In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spatiotemporal fields, to assess different simulations and to compare simulations with observations. A significant information reduction is possible by focusing on geometric-topological features of the fields or on derived meteorological objects. Due to the huge size of the data sets, spatial features have to be extracted in time slices and traced over time. Fields with chaotic component, i.e. without 1:1 spatiotemporal correspondences, can be compared by looking upon statistics of feature properties. Feature extraction, however, requires a clear mathematical definition of the features – which many meteorological objects still lack. Traditionally, object extractions are often heuristic, defined only by implemented algorithms, and thus are not comparable. This work surveys our framework designed for efficient development of feature tracking methods and for testing new feature definitions. The framework supports well-established visualization practices and is being used by atmospheric researchers to diagnose and compare data.
Traditionally, Lagrangian fields such as finite-time Lyapunov exponents (FTLE)
are precomputed on a discrete grid and are ray casted afterwards. This, however,
introduces both grid discretization errors and sampling errors during ray marching.
In this work, we apply a progressive, view-dependent Monte Carlo-based approach
for the visualization of such Lagrangian fields in time-dependent flows. Our ap-
proach avoids grid discretization and ray marching errors completely, is consistent,
and has a low memory consumption. The system provides noisy previews that con-
verge over time to an accurate high-quality visualization. Compared to traditional
approaches, the proposed system avoids explicitly predefined fieldline seeding
structures, and uses a Monte Carlo sampling strategy named Woodcock tracking
to distribute samples along the view ray. An acceleration of this sampling strategy
requires local upper bounds for the FTLE values, which we progressively acquire
during the rendering. Our approach is tailored for high-quality visualizations of
complex FTLE fields and is guaranteed to faithfully represent detailed ridge surface
structures as indicators for Lagrangian coherent structures (LCS). We demonstrate
the effectiveness of our approach by using a set of analytic test cases and real-world numerical simulations.
In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spatiotemporal fields, to assess different simulations and to compare simulations with observations. A significant information reduction is possible by focusing on geometric-topological features of the fields or on derived meteorological objects. Due to the huge size of the data sets, spatial features have to be extracted in time slices and traced over time. Fields with chaotic component, i.e. without 1:1 spatiotemporal correspondences, can be compared by looking upon statistics of feature properties. Feature extraction, however, requires a clear mathematical definition of the features - which many meteorological objects still lack. Traditionally, object extractions are often heuristic, defined only by implemented algorithms, and thus are not comparable. This work surveys our framework designed for efficient development of feature tracking methods and for testing new feature definitions. The framework supports well-established visualization practices and is being used by atmospheric researchers to diagnose and compare data.
To improve existing weather prediction and reanalysis capabilities, high-resolution and multi-modal climate data becomes an increasingly important topic. The advent of increasingly dense numerical simulation of atmospheric phenomena, provides new means to better understand dynamic processes and to visualize structural flow patterns that remain hidden otherwise. In the presented illustrations we demonstrate an advanced technique to visualize multiple scales of dense flow fields and Lagrangian patterns therein, simulated by state-of-the-art simulation models for each scale. They provide a deeper insight into the structural differences and patterns that occur on each scale and highlight the complexity of flow phenomena in our atmosphere.
This paper is associated with a poster winner of a 2016 APS/DFD Milton van Dyke Award for work presented at the DFD Gallery of Fluid Motion. The original poster is available from the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2016.GFM.P0030
Time-dependent vector fields are of high relevance to describe a wide range
of physical phenomena based on particle motion, including stall effects in
technical engineering, blood flow anomalies, and atmospheric mass trans-
port. The efficient analysis and fundamental understanding of intrinsic field
properties can lead to significant improvements when interacting with such
phenomena based on the available field data. So called Lagrangian methods
are a particularly established technique for this purpose. They are based on
the evaluation of time-dependent particle trajectories.
This work will present an overview of the state of the art in time-dependent
vector field analysis and visualization, with special focus on topology-oriented
and Lagrangian methods. The first core aspect of this work is the introduction
of a novel concept for a more objective and qualitative benchmark of existing
Lagrangian approaches. Based on this benchmark, this work contributes
and evaluates a set of novel concepts, that offer new perspectives towards
established approaches with respect to computational handling, quality and
efficiency. The second core aspect is the empirical application and validation
of Lagrangian methods with respect to practically relevant analysis scenarios.
Each application case includes an introduction into the underlying problem
statement and an efficient solution using Lagrangian methods. The validation
focuses on the comparison with existing flow analysis concepts and aspects
of the quantitative evaluation in each case. Together, both topics of this work
contribute towards a more consistent formalization, but also to the improved
applicability of Lagrangian methods.