TY - JOUR
T1 - The Effect of Data Transformations on Scalar Field Topological Analysis of High-Order FEM Solutions
AU - Jallepalli, Ashok
AU - Levine, Joshua A.
AU - Kirby, Robert M.
N1 - Funding Information:
The authors also wish to thank Professor Spencer Sherwin (Imperial College London, UK), Mr. Alexandre Sidot, and the Nektar++ Group for the counter-rotating vortex data and helpful discussions. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under Award Number(s) DE-SC-0019039. The authors acknowledge support from ARO W911NF-15-1-0222 (Program Manager Dr. Mike Coyle).
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - High-order finite element methods (HO-FEM) are gaining popularity in the simulation community due to their success in solving complex flow dynamics. There is an increasing need to analyze the data produced as output by these simulations. Simultaneously, topological analysis tools are emerging as powerful methods for investigating simulation data. However, most of the current approaches to topological analysis have had limited application to HO-FEM simulation data for two reasons. First, the current topological tools are designed for linear data (polynomial degree one), but the polynomial degree of the data output by these simulations is typically higher (routinely up to polynomial degree six). Second, the simulation data and derived quantities of the simulation data have discontinuities at element boundaries, and these discontinuities do not match the input requirements for the topological tools. One solution to both issues is to transform the high-order data to achieve low-order, continuous inputs for topological analysis. Nevertheless, there has been little work evaluating the possible transformation choices and their downstream effect on the topological analysis. We perform an empirical study to evaluate two commonly used data transformation methodologies along with the recently introduced L-SIAC filter for processing high-order simulation data. Our results show diverse behaviors are possible. We offer some guidance about how best to consider a pipeline of topological analysis of HO-FEM simulations with the currently available implementations of topological analysis.
AB - High-order finite element methods (HO-FEM) are gaining popularity in the simulation community due to their success in solving complex flow dynamics. There is an increasing need to analyze the data produced as output by these simulations. Simultaneously, topological analysis tools are emerging as powerful methods for investigating simulation data. However, most of the current approaches to topological analysis have had limited application to HO-FEM simulation data for two reasons. First, the current topological tools are designed for linear data (polynomial degree one), but the polynomial degree of the data output by these simulations is typically higher (routinely up to polynomial degree six). Second, the simulation data and derived quantities of the simulation data have discontinuities at element boundaries, and these discontinuities do not match the input requirements for the topological tools. One solution to both issues is to transform the high-order data to achieve low-order, continuous inputs for topological analysis. Nevertheless, there has been little work evaluating the possible transformation choices and their downstream effect on the topological analysis. We perform an empirical study to evaluate two commonly used data transformation methodologies along with the recently introduced L-SIAC filter for processing high-order simulation data. Our results show diverse behaviors are possible. We offer some guidance about how best to consider a pipeline of topological analysis of HO-FEM simulations with the currently available implementations of topological analysis.
KW - Filtering Techniques
KW - High-Order Finite Element Methods
KW - Scalar Field Visualization
KW - Topological Analysis
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U2 - 10.1109/TVCG.2019.2934338
DO - 10.1109/TVCG.2019.2934338
M3 - Article
C2 - 31425105
AN - SCOPUS:85075633758
SN - 1077-2626
VL - 26
SP - 162
EP - 172
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
M1 - 8805451
ER -