  In this paper, we use graph-based techniques to investigate the use of
geometric deep learning (GDL) in the classification and down-selection of
aircraft thermal management systems (TMS). Previous work developed an
enumerative graph generation procedure using a component catalog with network
structure constraints to represent novel aircraft TMSs as graphs. However, as
with many enumerative approaches, combinatorial explosion limits its efficacy
in many real-world problems, particularly when simulations and optimization
must be performed on the many (automatically-generated) physics models.
Therefore, we present an approach that takes the directed graphs representing
aircraft TMSs and use GDL to predict the critical characteristics of the
remaining graphs. This paper's findings demonstrate that incorporating
additional graph-based features enhances performance, achieving an accuracy of
97% for determining a graph's compilability and simulatability while using only
5% of the data for training. By applying iterative classification methods, we
also successfully segmented the total set of graphs into more specific groups
with an average inclusion of 84.7 of the top 100 highest-performing graphs,
achieved by training on 45% of the data.
