2016 IEEE Pacific Visualization Symposium (PacificVis) (2016)
April 19, 2016 to April 22, 2016
Dan Maljovec , SCI Institute, University of Utah
Bei Wang , SCI Institute, University of Utah
Paul Rosen , University of South Florida
Andrea Alfonsi , Idaho National Laboratory
Giovanni Pastore , Idaho National Laboratory
Cristian Rabiti , Idaho National Laboratory
Valerio Pascucci , SCI Institute, University of Utah
In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information - inherently lacking in visual encodings - offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to nuclear scientists. Our framework is being deployed into the multipurpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using a simulation dataset studying nuclear fuel performance.
computational topology, Sensitivity analysis, uncertainty, nuclear simulation
D. Maljovec et al., "Rethinking sensitivity analysis of nuclear simulations with topology," 2016 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Taipei, Taiwan, 2016, pp. 64-71.