A Comparative Study of the Perceptual Sensitivity of Topological Visualizations to Feature Variations

Color maps are a commonly used visualization technique in which data are mapped to optical properties, e.g., color or opacity. Color maps, however, do not explicitly convey structures (e.g., positions and scale of features) within data. Topology-based visualizations reveal and explicitly communicate structures underlying data. Although we have a good understanding of what types of features are captured by topological visualizations, our understanding of people's perception of those features is not. This paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and persistence diagram visualizations compared to a reference color map visualization for synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In particular, we built and ran a human-subject study that evaluated the perception of data features characterized by Gaussian signals and measured how effectively each visualization technique portrays variations of data features arising from the position and amplitude variation of a mixture of Gaussians. For positional feature variations, the results showed that only the Reeb graph visualization had high sensitivity. For amplitude feature variations, persistence diagrams and color maps demonstrated the highest sensitivity, whereas isocontours showed only weak sensitivity. These results take an important step toward understanding which topology-based tools are best for various data and task scenarios and their effectiveness in conveying topological variations as compared to conventional color mapping

Streamline Integration using MPI-Hybrid Parallelism on a Large Multi-Core Architecture

Streamline computation in a very large vector field data set represents a significant challenge due to the non-local and datadependentnature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programmingand execution as applied to streamline integration on a large, multicore platform. With multi-core processors now prevalent in clustersand supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice.We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize-over-seeds and parallelize-overblocks,and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing betweencores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication andI/O bandwidth than a traditional, non-hybrid distributed implementation

Integrated Radiation Transport and Nuclear Fuel Performance for Assembly-Level Simulations

The Advanced Multi-Physics (AMP) Nuclear Fuel Performance code (AMPFuel) is focused on predicting the temperature and strain within a nuclear fuel assembly to evaluate the performance and safety of existing and advanced nuclear fuel bundles within existing and advanced nuclear reactors. AMPFuel was extended to include an integrated nuclear fuel assembly capability for (one-way) coupled radiation transport and nuclear fuel assembly thermo-mechanics. This capability is the initial step toward incorporating an improved predictive nuclear fuel assembly modeling capability to accurately account for source-terms and boundary conditions of traditional (single-pin) nuclear fuel performance simulation, such as the neutron flux distribution, coolant conditions, and assembly mechanical stresses. A novel scheme is introduced for transferring the power distribution from the Scale/Denovo (Denovo) radiation transport code (structured, Cartesian mesh with smeared materials within each cell) to AMPFuel (unstructured, hexagonal mesh with a single material within each cell), allowing the use of a relatively coarse spatial mesh (10 million elements) for the radiation transport and a fine spatial mesh (3.3 billion elements) for thermo-mechanics with very little loss of accuracy. In addition, a new nuclear fuel-specific preconditioner was developed to account for the high aspect ratio of each fuel pin (12 feet axially, but 1 4 inches in diameter) with many individual fuel regions (pellets). With this novel capability, AMPFuel was used to model an entire 17 17 pressurized water reactor fuel assembly with many of the features resolved in three dimensions (for thermo-mechanics and/or neutronics), including the fuel, gap, and cladding of each of the 264 fuel pins; the 25 guide tubes; the top and bottom structural regions; and the upper and lower (neutron) reflector regions. The final, full assembly calculation was executed on Jaguar using 40,000 cores in under 10 hours to model over 162 billion degrees of freedom for 10 loading steps. The single radiation transport calculation required about 50% of the time required to solve the thermo-mechanics with a single loading step, which demonstrates that it is feasible to incorporate, in a single code, a high-fidelity radiation transport capability with a high-fidelity nuclear fuel thermo-mechanics capability and anticipate acceptable computational requirements. The results of the full assembly simulation clearly show the axial, radial, and azimuthal variation of the neutron flux, power, temperature, and deformation of the assembly, highlighting behavior that is neglected in traditional axisymmetric fuel performance codes that do not account for assembly features, such as guide tubes and control rods

Seeing the Unseeable

The SciDAC Visualization and Analytics Center for Enabling Technologies (VACET) isa highly productive effort combining the forces of leading visualization researchersfrom five different institutions to solve some of the most challenging dataunderstanding problems in modern science. The VACET technology portfolio isdiverse, spanning all typical visual data analysis use models and effectivelybalancing forward-looking research with focused software architecture andengineering resulting in a production-quality software infrastructure. One of the keyelements in VACET's success is a rich set of projects that are collaborations withscience stakeholders: these efforts focus on identifying and overcoming obstacles toscientific knowledge discovery in modern, large, and complex scientific datasets

Scalable computation of streamlines on very large datasets

Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Stream-lines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline compu-tation, and requires careful balancing of computational de-mands placed on I/O, memory, communication, and proces-sors. In this paper we review two parallelization approaches based on established parallelization paradigms (static de-composition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. Our algorithm is aimed at good scalability and performance across the widely varying computational characteristics of streamline-based problems. We perform performance and scalability studies of all three algorithms on a number of prototypi-cal application problems and demonstrate that our hybrid scheme is able to perform well in different settings. ∗(c) 2009 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. SC09 Novembe

Integrated Radiation Transport and Nuclear Fuel Performance for Assembly-Level Simulations

The Advanced Multi-Physics (AMP) Nuclear Fuel Performance code (AMPFuel) is focused on predicting the temperature and strain within a nuclear fuel assembly to evaluate the performance and safety of existing and advanced nuclear fuel bundles within existing and advanced nuclear reactors. AMPFuel was extended to include an integrated nuclear fuel assembly capability for (one-way) coupled radiation transport and nuclear fuel assembly thermo-mechanics. This capability is the initial step toward incorporating an improved predictive nuclear fuel assembly modeling capability to accurately account for source-terms and boundary conditions of traditional (single-pin) nuclear fuel performance simulation, such as the neutron flux distribution, coolant conditions, and assembly mechanical stresses. A novel scheme is introduced for transferring the power distribution from the Scale/Denovo (Denovo) radiation transport code (structured, Cartesian mesh with smeared materials within each cell) to AMPFuel (unstructured, hexagonal mesh with a single material within each cell), allowing the use of a relatively coarse spatial mesh (10 million elements) for the radiation transport and a fine spatial mesh (3.3 billion elements) for thermo-mechanics with very little loss of accuracy. In addition, a new nuclear fuel-specific preconditioner was developed to account for the high aspect ratio of each fuel pin (12 feet axially, but 1 4 inches in diameter) with many individual fuel regions (pellets). With this novel capability, AMPFuel was used to model an entire 17 17 pressurized water reactor fuel assembly with many of the features resolved in three dimensions (for thermo-mechanics and/or neutronics), including the fuel, gap, and cladding of each of the 264 fuel pins; the 25 guide tubes; the top and bottom structural regions; and the upper and lower (neutron) reflector regions. The final, full assembly calculation was executed on Jaguar using 40,000 cores in under 10 hours to model over 162 billion degrees of freedom for 10 loading steps. The single radiation transport calculation required about 50% of the time required to solve the thermo-mechanics with a single loading step, which demonstrates that it is feasible to incorporate, in a single code, a high-fidelity radiation transport capability with a high-fidelity nuclear fuel thermo-mechanics capability and anticipate acceptable computational requirements. The results of the full assembly simulation clearly show the axial, radial, and azimuthal variation of the neutron flux, power, temperature, and deformation of the assembly, highlighting behavior that is neglected in traditional axisymmetric fuel performance codes that do not account for assembly features, such as guide tubes and control rods

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.Comment: 112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issu