Conditional Analysis of DNS Combustion Data Using Local and Global Shape Characteristics

Feature-based conditional statistical methods are essential for the analysis of complex, large-scale data. We introduce two shape-based conditional analysis algorithms that can be deployed in complementary settings: local methods are required when the phenomena under study comprise many small intermittent features, while global shape methods are required to study large-scale structures. We present the algorithms in context with their motivating combustion science case studies, but we note that the methods are applicable to a broad class of physics-based phenomena.