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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemLarge Deflection Effects on the ERR and Mode Partitioning of the Single and Double Cantilever Beam Sandwich Debond Configurations(Georgia Institute of Technology, 2023-12-05) Okegbu, Daniel O.The goal of this study is to investigate the effects of large deflections in the energy release rate and mode partitioning of face/core debonds for the Single and Double Cantilever Beam Sandwich Composite testing configurations, which are loaded with an applied shear force and/or bending moment. Studies on this topic have been done by employing geometrically linear theories (either Euler-Bernoulli or Timoshenko beam theory). This assumes that the deflection at the tip of the loaded debonded part is small, which is not always the case. To address this effect, we employ the elastica theory, which is a non-linear theory, for the debonded part. An elastic foundation analysis and the linear Euler-Bernoulli theory are employed for the "joined" section where a series of springs is employed to represent the interfacial bond between the face and the substrate (core and bottom face). The derivation/solution is done for a general asymmetric sandwich construction. A $J$-integral approach is subsequently used to derive a closed-form expression for the energy release rate. Furthermore, in the context of this Elastic Foundation model, a mode partitioning measure is defined based on the transverse and axial displacements at the beginning of the elastic foundation. The results are compared with finite element results for a range of core materials and show very good agreement. Specifically, the results show that large deflection effects reduce the energy release rate but do not have a noteworthy effect on the mode partitioning. Conversely, a small deflection assumption can significantly overestimate the energy release rate.
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ItemMechanical Metamaterial Lattices via Direct Methods(Georgia Institute of Technology, 2023-08-23) Gloyd, James ToddDiscrete lattices use individually manufactured unit cell building blocks, which are then assembled to form large lattice structures, the quality of which is not significantly influenced by the scale of the structure. Furthermore, the size of the final structure is not bounded by the footprint of the manufacturing equipment. Tuning the elastic behavior of materials and structures provides the possibility for significant improvements to overall performance, as demonstrated by structural and topology optimization studies. Similar improvements can be made in discrete lattice applications, as shown by the Coded Structures Laboratory at NASA. Improvements made to performance of discrete lattice structures are, so far, limited by the lack of a systematic, direct method to dictate the behavior---that is, prescribe the deformation---of the final structure. Here we present a direct method of prescribed structural behavior integrating structural and topology optimization, for both discrete lattice structures and general structures. Also presented are formulas and methods for calculating the determinant and inverse of a linear combination of matrices, which originally stemmed from the development of prescribed behavior methods however, while applicable to prescribed deformation problems, are much more useful in other situations. The direct methods of prescribed deformation presented here automatically produce dictated behavior from the candidate structure when possible and produce an approximation when the desired behavior is impossible. These methods are shown to move towards a minimizer with quadratic convergence, with improved results in situations with fewer limits on the prescribed behavior. Additionally, the presented formula for calculation of the determinant of a linear combination of matrices provides exact results in as little as one tenth of the time of traditional approximation methods, and the exact inverse of the linear combination is calculated in as little as one quarter of the time of traditional exact methods. We show these formulas provide significant computational and conceptual improvement to current methods and provide unmatched performance in parallel computing settings.
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ItemModels for the Non-Linear Response of Tensegrity Meta-materials(Georgia Institute of Technology, 2023-08-04) Kraus, Julie AnneTensegrity structures are composed of bars and tethers that have a state where they can be stability stressed without outside constraints. This makes them pliable, controllable, and capable of large deformations. In order to use tensegrities in actual applications it is necessary to have the capability to model them. Current computational modeling methods involve finite element modeling (FEM). FEM is a very powerful tool that can analyze most systems given enough time and computational power. However, for certain applications related to tensegrity structures, the required computational power can become a limiting factor. In this work we develop computational models to further improve our understanding of tensegrity structures. For the case of pin-jointed tensegrity structures, we use the Elastica theory to derive an analytical model of deformation in imperfect beams. Our model outperforms existing ones and is more accurate over a large range of deformations. We also then utilize finite element simulations to study the behavior of 3D-printed tensegrity meta-materials and demonstrate that the same type of response observed in pin-jointed structures is also held in the frame cases, and that tensegrities have strong potential as structures that can handle high strain prior to failure due to their ability to delocalize failure. Finally, by observing that these models are accurate but computationally expensive, we develop a machine learning based reduced order model to predict the response of tensegrity structures. Our algorithm is based on a frame-indifferent neural network architecture, allowing for the ability to predict the effects of large-scale deformation on tensegrity structures.
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ItemPhysics enabled Data-driven structural analysis for mechanical components and assemblies(Georgia Institute of Technology, 2022-05-20) Shah, Aarohi BhavinbhaiAnalyzing structures that exhibit nonlinear and history-dependent behaviors is crucial for many engineering applications such as structural health monitoring, wave management/isolation, and geometric optimization to name a few. However, current approaches for modeling such structural components and assemblies rely on detailed finite element formulations of each component. While finite element method serves to be versatile and well-established for nonlinear and history-dependent problems, it tends to be inefficient. Consequently, their computational cost, becomes prohibitive for many applications when time-sensitive predictions are needed. In the present work, we introduce a framework to develop data-driven dimensionally-reduced surrogate models at the component level, which we call smart parts (SPs), to establish a direct relationship between the input–output parameters of the component. Our method utilizes advanced machine learning techniques to develop SPs such that all the information pertaining to history and nonlinearities is preserved. Unlike other data-driven approaches, our method is not limited to any particular type of nonlinearity and it does not impose restrictions on the type of analysis to be performed. This renders its application straightforward for a diverse set of engineering problems, as we show through multiple case studies. We also propose a novel meta learning based approach to enable an extension of this approach to dynamic problems. In addition, we present several ways to enhance this approach in terms of precision and efficiency. Thus, the present work provides an approach that can dramatically boost the computational efficiency and simplicity to analyze large structures without sacrificing accuracy.
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ItemA Micromechanically-Informed Model of Thermal Spallation with Application to Propulsive Landing(Georgia Institute of Technology, 2021-12-15) Hart, Kenneth ArthurDuring the propulsive landing of spacecraft, the retrorocket exhaust plume introduces the landing site surface to significant pressure and heating. Landing site materials include concrete on Earth and bedrock on other bodies, two highly brittle materials. During a landing event, defects and voids in the material grow due to thermal expansion and coalesce, causing the surface to disaggregate or spall. After a spall is freed from the surface, the material beneath it is exposed to the pressure and heat load until it spalls, continuing the cycle until engine shutdown. Spalls and debris entrained in the exhaust plume risk damaging the lander or nearby assets- a risk that increases for larger engines. The purpose of this work is to develop a micromechanically-informed model of thermal spallation to improve understanding of this process, in the context of propulsive landing. A preliminary simulation of landing site spallation, utilizing an empirical thermal spallation model, indicates that spallation may occur for human-scale Mars landers. This model, however, was developed for drilling through granite, which has a fundamentally different microstructure compared to typical landing sites, necessitating a more general approach. To that end, highly-detailed simulations of thermomechanical loading, applied to representative microstructures, inform a functional relationship between applied heat flux and spallation rate. These representative microstructures can be generated using an algorithm that has been validated for a wide variety of materials, including basalt from Gusev Crater, Mars.
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ItemStructural impacts of inflatable aerodynamic decelerator design(Georgia Institute of Technology, 2020-05-17) Li, LinIn order to land larger payloads to Mars, more capable decelerators are required to advance beyond the performance limitations of traditional heritage entry, descent, and landing technologies. One potential technology is an inflatable aerodynamic decelerator (IAD), a flexible aeroshell that can be folded and stowed in a rocket fairing during launch and inflated prior to entry. IADs allow for larger drag areas with minimal mass increase in comparison to traditional rigid aeroshells and, thus, enable improved deceleration performance. However, minimal insight is available regarding the impact of detailed IAD configuration design on their structural performance. Future missions involving IADs will require this structural performance information early in the design cycle in order to develop IADs that have favorable structural and mass performance and are tailorable to specific mission requirements. This thesis advances the state of the art of inflatable aerodynamic decelerator design by investigating the implications of IAD configuration on their structural and mass performance and developing data analysis techniques to assess an IAD's global dynamic response. These methodologies and results improve future IAD design efforts by enabling estimates of structural performance information in conceptual design, exploring the configurational impacts of novel decelerator designs, and providing new test methodologies to better evaluate those designs. This research, therefore, starts to explore the next phases in the IAD development process, as inflatable decelerator technology maturation transitions from early-stage concept demonstration to applications on future missions that require expanded capabilities beyond the current configurational design space. In order to inform conceptual design efforts, simplified models of traditional stacked tori and tension cone decelerators are developed that strategically eliminate complexity in the IAD design to enable rapid simulation of the structural response. These computationally efficient models are used to evaluate the entire configurational design space and enable assessments of the IAD design on their structural and mass performance. A new hybrid decelerator is also developed, leveraging the benefits of the stacked tori and tension cone designs, to provide configurations that better balance mass efficiency with reduced deflection compared to the traditional stacked tori and tension cone designs. New data analysis methodologies are also developed to extract information on an IAD’s dynamic response from photogrammetry data. These methodologies allow for visualization of the global IAD dynamic response along with an evaluation of the frequency content of motion. The analysis routines are applied to existing photogrammetry data sets to highlight fundamental characteristics of the decelerator dynamic response and fluid-structure resonance phenomena.