In a 2023 Modelica Conference paper, we proposed a novel method for the modular structural analysis of DAE systems, in which the structural analysis is not performed on flattened models, but rather at the class level. A new notion of structural interface was proposed, in which classes are enriched with context information. That paper developed our approach based on a few illustrative examples.

In this paper, we provide the details of our algorithm. Its performance depends on the system architecture: the analysis of models having a small number of classes (possi- bly instantiated many times), with a low treewidth system architecture, scales up very efficiently with this approach. We then present additional benchmarks, among which a urban heating network, a representative real-life example on which a near-logarithmic scaling up is shown.