Organizational Unit:
Daniel Guggenheim School of Aerospace Engineering

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Now showing 1 - 2 of 2
  • Item
    Design and manufacturing of conformal ablative heatshields
    (Georgia Institute of Technology, 2019-06-10) Sidor, Adam Thomas
    Conformal ablators, first introduced in the early 2000s under the NASA Hypersonics Project, are a type of rigid ablative thermal protection system that uses flexible, rather than rigid, fibrous substrates. These materials are impregnated with resin in a mold to yield a part that is close to the final geometry and requires little post-process machining (a near net shape part). The lack of fiber connectivity through the thickness enables the TPS to tolerate larger strains than comparable rigid substrate ablators facilitating larger tiles and installation on most aeroshells without strain isolation. Reduced part count and simplified integration drive reductions in labor, cost and complexity –advancements which are enabling for planetary and human missions. Conformal ablators are currently fabricated using an open liquid impregnation process adapted from a technique developed for Lightweight Ceramic Ablators, such as Phenolic Impregnated Carbon Ablator, which leads to design and manufacturing inefficiencies. This work advanced a new manufacturing technique for conformal ablators, vacuum infusion processing, that reduces resin consumption and streamlines clean up. The closed process also eliminates an expensive atmosphere-controlled oven or vacuum chamber. A design methodology, centered around a simulation of the mold filling process, was developed to tailor a conformal ablative heatshield to vacuum infusion processing. A constitutive model, combining properties of individual components, was formulated to estimate the properties of the composite TPS material. The methodology leverages this model, integrated with material selection, tile layout, and the mold filling simulation, to automate a conceptual conformal heatshield design. The approach allows rapid iteration on TPS composition and manufacturing constraints.
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    Multi-physics high resolution topology optimization for aerospace structures
    (Georgia Institute of Technology, 2019-03-28) Chin, Ting Wei
    Advancements in multimaterial additive manufacturing have the potential to enable the creation of topology optimized structures with both shape and material tailoring. These are extremely useful in creating designs for multi-physics applications where engineering experience may be lacking. These include designing aerospace structures that are subjected to elevated temperature environment, where mechanical and thermal loads are present or designing structures for strength and avoiding low natural frequency resonance. Multi-physics analysis and multimaterial design parametrization present additional complexity and technical challenges to overcome for large-scale designs. Design and analysis using large-scale uniform meshes is computationally expensive due to the large number of degrees of freedom (DOFs). The same mesh resolution can be created through adaptive mesh refinement such that it has fewer DOFs. However, due to the complexity in creating these adaptive meshes, especially for higher order 3D designs, they are mostly confined to 2D topology optimization. Large-scale multimaterial design through Discrete Material Optimization (DMO) also results in numerous partition of unity constraints and a multimaterial design space that has more local minima than an equivalent single material design space. This work presents new techniques for obtaining large-scale 3D multimaterial, multi-physics designs. Adaptive mesh refinement and higher order design parametrization are introduced to obtain smooth features. The multi-physics capabilities of the method are demonstration in the form of thermoelastic topology optimization. Multimaterial designs using adaptive mesh refinement as well as higher order design parametrization with steady-state thermoelastic topology optimization are presented. Novel technique to accelerate large-scale natural frequency-constrained topology optimization design is also presented.