Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage models at the highest levels of assembly. The effort to generate/verify these models against their detailed counterparts is a challenge not yet conclusively solved. Higher level organizations often place a limit on the node count for delivered models, assuming smaller models result in faster computation. The burden of producing and verifying the accuracy of reduced models is primarily on the lower-level organizations, which consumes resources to produce these models. Limiting the number of allowable nodes may prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which require more nodes than centroid based models. A methodology using Thermal Desktop was described in 2010 using: (1) finite elements and edge nodes for the conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. While the methodology was clear, the implementation would have had to be done manually; with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and be a viable approach for the generation of reduced models. The approach was implemented at NASA-Goddard for the Capture-Containment-and- Return-System (CCRS) payload, resulting in the TCYEE tool. CCRS requires delivery with node limitations through the European Space Agency to its contractors. TCYEE generated the reduced model for delivery and the predictions compared favorably to the detailed model. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements. This paper describes the methodology, implementation, and the performance of reduced models compared to their detailed counterparts.