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Completed TRL 3 (started at 1, targeting 5)
Project Objective
To develop techniques and capabilities of topology optimized cryogenic thermal isolation struts for deep space applications.
Project Description
Reducing heat transfer in cryogenic tanks in orbit currently rely on traditional materials used in thermal struts which include composites and epoxy as the primary bonded interfaces. These materials may not survive long duration missions (like Mars missions) in the radiation environments of space. Developing fully metallic struts that will extend mission life while limiting heat transfer into cryogenic components are a better option for longer duration missions. Topology optimization techniques are a great way to accomplish this design improvement.
Topology Optimization (TOp) is a design process that utilizes finite element analysis to identify optimal part designs according to any criteria we choose. Common criteria may be driven by mass, strength, density, dynamics, cost, manufacturing, etc. This technique helps with fine tuning a design to get the best possible product and opens new possibilities where we can improve and redefine the capabilities of hardware in structures, combustions devices, valves, turbomachinery, CFM, and many more.
The main goal of this project was to develop the capability and use of topology optimization and implement this process in to existing cryogenic fluid management tasks and advance the technology to other uses. By using commercially available programs like N-topology, Python, ANSYS, and Pro-Engineer CREO CAD software, we can develop the tools needed to build CAD and analysis models to tune hardware to the needs of a project in order to either exceed the current common methods or maximize material capabilities.
Project Results and Conclusions
This project successfully developed and implemented optimization algorithms for thermal struts that balance structural strength with heat transfer performance. Two optimization approaches were evaluated: Bayesian optimization using Gaussian processes (Scikit Optimize's gp_minimize) and a mixed-variable genetic algorithm (Pymoo's MixedVariableGA). The optimization framework minimizes total heat transfer while maintaining structural safety factors and manufacturing constraints with objective functions evaluated through nTop Automate and Ansys Parametric Design Language (APDL). Multiple optimized lattice-based strut designs were generated, and traditionally designed struts were machined in preparation for comparative testing.
The Gaussian (gp_minimize) algorithm demonstrated efficient convergence with relatively few function evaluations but proved susceptible to local minima inherent in automated geometry and simulation systems. This limitation was partially mitigated by isolating key variables (e.g., unit cell type) and optimizing each case independently. MVGA (MixedVariableGA) proved more robust for this application, with its higher evaluation count offset by optimized function performance. A critical lesson learned: global optimization algorithms will identify and exploit any weaknesses in problem formulation, making extremely robust objective/constraint functions essential. This includes rigorous geometry generation, simulation setup, and constraint formulation to prevent undesirable optimization results.
Hardware development revealed significant additive manufacturing limitations for complex lattice structures. Intricate lattice geometries generated prohibitively large STL files that exceeded printer limits or complicated build preparation—a challenge applicable to any project using high-volume, small-scale lattices in AM parts. An initial manufacturing test article failed due to support placement challenges, as many optimized designs require internal supports that are difficult or impossible to remove through traditional machining. Identified solutions for future work include optimizing print orientation, employing chemical milling for support removal, and imposing additional manufacturing constraints during the optimization phase.
In conclusion, adding the capability of topology optimization into structural design can lead to advancements of multiple technologies including reducing cost and schedule. Implementation of multiphysics/multidisciplinary optimizations can further enhance this benefit.
The outcome of this project has lead to the advancement of topology optimalization techniques and hardware using advanced algorithms. This optimization method is broadly applicable to projects where parameter-based optimization of complex geometries subject to simulation-based constraints can improve design performance. These methods can support structural, material, cost and other optimizations which allow for reduction in materials, schedule, cost, etc.
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