In Situ Feature Extraction and Visualization from Discontinuous Galerkin Based High-Order Methods
Abstract
The use of simulation science as a means of scientific inquiry is increasing at a tremendous rate. The process of mathematically modeling physical phenomena, estimating key modeling parameters, numerically approximating the solution, and computationally solving the resulting algorithm has inundated the scientific and engineering worlds, allowing for rapid advances in our understanding and utilization of the world around us. The efficacy of simulation science has been, in part, due to two critical components: (1) the identification and minimization of the error budget (e.g. modeling, discretization and uncertainty errors), and equally importantly, (2) evaluation mechanisms (such as visualization) by which the investigator assimilates the data produced through simulation. The latter allows for further refinement of the simulation science process (through model correction, increased numerical resolution, or algorithm debugging, etc.) and makes possible scientific statements about the physical phenomena being investigated. Tremendous effort has been exerted over many decades in the pursuit of numerical methods that are both flexible and accurate, hence providing sufficient fidelity to be employed in the numerical solution of a large number of models, and sufficient analysis of accuracy to allow researchers to focus their attention on model refinement and uncertainty quantification. High-order finite element methods (also known as spectral/hp element methods), using either the continuous Galerkin or discontinuous Galerkin formulation, have reached a level of sophistication that allows them to be commonly applied to a diverse set of real-life engineering problems in computational solid mechanics, fluid dynamics, acoustics and electromagnetics. Many of the physical problems of interest are, unfortunately, not steady-state --- leading to simulations that must run for a long time (days, weeks and in some cases months). Thus, in the absence of creative solutions, datasets can easily consume all available storage and networking resources. Examples of such simulations within fluid dynamics include all simulations in which the fluid is in transition or fully turbulent. With regards to ARO interests, problems in turbo-machinery and rotorcraft, where aspects of the geometry are rotating and/or sliding past one other, fall into this category. High-order finite element methods are now beginning to be used to simulate these physical systems due to their inherent ability to capture complex structures (such as vortices) with little numerical dissipation and dispersion. The transient nature of these simulations complicates the data handling (post processing requires the time history) and renders single snap-shots of the solution insufficient to understand the time-varying nature of the physics. Objective Our research objectives are two-fold: (1) We will generate "high-order FEM" appropriate dimensionality reduction feature extraction methods such as vortex cores which can be accomplished as part of an in situ data processing pipeline. (2) Given the exploratory nature inherent in analyzing and visualizing transient phenomena, we may specify regions of interest in an in situ fashion within a simulation field based upon the visualization objective, extract and transmit the result of working on relevant high-order FEM information to our visualization system, and then reconstruct the visualization features of interest with the cognizance of V&V.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 14, 2019
- Source ID
- W911NF1810293
Entities
People
- Robert Kirby
Organizations
- Army Contracting Command
- United States Army
- University of Utah