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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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Publication Search Results

Now showing 1 - 10 of 11

Large 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.
Mechanical Metamaterial Lattices via Direct Methods

(Georgia Institute of Technology, 2023-08-23) Gloyd, James Todd

Discrete 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.
Models for the Non-Linear Response of Tensegrity Meta-materials

(Georgia Institute of Technology, 2023-08-04) Kraus, Julie Anne

Tensegrity 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.
Physics enabled Data-driven structural analysis for mechanical components and assemblies

(Georgia Institute of Technology, 2022-05-20) Shah, Aarohi Bhavinbhai

Analyzing 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.
A Micromechanically-Informed Model of Thermal Spallation with Application to Propulsive Landing

(Georgia Institute of Technology, 2021-12-15) Hart, Kenneth Arthur

During 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.
Structural impacts of inflatable aerodynamic decelerator design

(Georgia Institute of Technology, 2020-05-17) Li, Lin

In 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.
Smart finite elements: An application of machine learning to reduced-order modeling of multi-scale problems

(Georgia Institute of Technology, 2019-05-21) Capuano, German

To design structures using state-of-the-art materials like composites and metamaterials, we need predictive tools that are capable of taking into account the phenomena occurring at different length scales. However, the upscaling of nonlinear mesoscale behavior to perform system-level predictions is intractable when using conventional modeling techniques. Other methods like multiscale finite elements are capable of solving arbitrary problems, but they tend to be computationally expensive because they rely on detailed models of the element's internal displacement field. We propose a method that utilizes machine learning to generate a direct relationship between the element's state and its forces, skipping altogether the complex and unnecessary task of finding its internal displacements. To generate our model, we choose an existing finite element formulation, extract data from an instance of that element, and feed that data to the machine learning algorithm. The result is an approximated model of the element that can be used in the same context. Unlike most data-driven techniques applied to individual elements, our method is not tied to any particular machine learning algorithm, and it does not impose any restriction on the solver of choice. In addition, we guarantee that our elements are physically accurate by enforcing frame indifference and conservation of linear and angular momentum. Our results indicate that this can considerably reduce the error of the method and the computational cost of producing and solving the model.
Topics in stress-induced instabilities and phase transitions in lattice-based solids

(Georgia Institute of Technology, 2018-11-09) Salahshoor Pirsoltan, Hossein

Mechanical response of a lattice-based solid, where the entire system is built up by a repetitive translation of a unit cell along its principal axes, manifests itself in changes either in the macro or the microstructure of the system. Depending on the loading configuration, drastic and remarkable changes may occur in the mechanical behavior of the entire lattice, often triggered by a macro or micro instability. The outcome of these instabilities varies significantly across different systems and scales, and is often reflected in phenomena such as, but not limited to, defect nucleation, shear bands, phase transitions and pattern formations. The subject of this thesis is to study stress-induced instabilities in certain groups of lattice-based solids, as well as their manifestations on mechanical properties of the whole lattice. Although we choose certain families of materials and metamaterials in our study, the approaches that we employ could be utilized to investigate instabilities of any system with translational symmetry. In chapter two, we investigate stress-induced instabilities in single crystal metals. We study the onset of symmetry breaking in four distinct metals of both FCC and BCC structure. We subject them to a combined shear and dilation, and examine the Schmid assumption, whereby we identify the onset of plasticity with the onset of instability. We perform both phonon and elastic stability analysis. We study the nature of the instability and show for the first time, to the best of our knowledge, that the short wavelength instabilities are abundant. Our results illustrate the potential pitfalls of relying on the widely used elastic stability analysis and disqualifies it as the method of choice. In chapter three, we investigate stress-induced material symmetry phase transitions in tensegrity-based metamaterials. We study material symmetries of tensegrity lattice by examining the eigenspaces of the effective elasticity tensor, obtained through a homogenization scheme. We demonstrate symmetry breaking and phase transitions, occurring solely due to pre-stressing the members of the lattice. We observe several phase transitions including cubic to tetragonal and tetragonal to orthotropic and vice-versa. We also demonstrate existence of a discrepancy between the material symmetries of a finite and infinite lattice, and show that imposing periodic boundary conditions can lead to physically incorrect results. Our results suggest new research paths for designing tensegrity-based metamaterials and tuning their properties through adjusting the pre-stretches in the cables. In chapter four, we study the mechanical response of homogeneous two dimensional tensegrity lattices. We aim to investigate the effect of lattice connectivity on the corresponding mechanical properties. We propose new designs of two dimensional lattices with very similar geometry, which only differ in the connectivity of compression members. We verify the stability of the proposed lattice, and then compare the mechanical response of two lattices, subjected to uniaxial compression, with connected and isolated compression members. We demonstrate that while local instabilities lead to global instability in the former case, the latter case remains globally stable for a vast regime of deformations. We believe our results create new avenues for investigating the complex problem of emergence of localized deformations.
A joined 3D/1D finite element method for aeroservoelastic analysis of damaged HALE aircraft wings

(Georgia Institute of Technology, 2018-04-05) Sadat Hoseini Khajuee, Seyed Mohammad Hanif

Nonlinear aeroelastic analysis of damaged High-Altitude-Long-Endurance aircraft wings is considered. The structural model consists of a full three-dimensional finite element continuum model for the damaged area, which is a small localized area of the wing, and a geometrically exact one-dimensional displacement-based finite element model for the undamaged part of the wing. The solid and the beam parts are then rigorously combined using a transformation between the joined nodes of the two models at their intersection. The transformation is derived using the recovery equations of variational asymptotic beam model and employed to eliminate the six degrees of freedom of the single joined node of the beam. The validity and efficiency of the method is demonstrated using test cases involving cracks and delaminations in the solid part. It is shown that although the accuracy remains virtually the same between the full three-dimensional model and the joined one-dimensional/three-dimensional model, the computational cost is considerably lower for the latter. Finite-state induced flow theory of Peters is exploited as the unsteady aerodynamic model to compute aerodynamic forces and moments acting on the wing. Combining the structural and aerodynamic models, a dynamic nonlinear aeroelastic element is developed for the time simulation of the dynamic responses of composite high aspect-ratio wings. The model has been used for analyzing aeroelastic instability boundaries and time simulations, as well as synthesizing an active flutter suppression control system. Numerical results verifying the validity of the method are presented and the results are discussed. The proposed joined model will enables the High-Altitude-Long-Endurance aircraft designers to tackle the problem of aeroelasticity in a computationally efficient manner, without sacrificing accuracy with regard to full three-dimensional models, hence reducing the overall time and cost of the design process.
A concurrent multiscale model for the thermomechanical response of polycrystalline materials

(Georgia Institute of Technology, 2016-12-09) Bouquet, Jean Baptiste Patrick

The presented work establishes and implements a novel concurrent multiscale framework to predict the size-dependent thermomechanical response of engineering materials. As such, it focuses on determining the interactions among length scales. More precisely, it aims at capturing the local variations on thermal conductivity at the subgrain level, the repercussion on the mesoscopic temperature field, and the consequent impact on thermal stresses. Therefore, the ultimate goal is to better understand the role of the grain size and the grain shape on heat transfer of polycrystalline materials and the influence on the underlying thermal stresses concentration, driving to the localized failure of the material. A review of current modeling approaches reveals a lack of numerical tools in the determination of the size effect on geometries presenting complex features. Indeed, current numerical methods are only capable of modeling thermomechanical processes at the macroscale. In contrast, analytical models have been developed to quantify the size effect in subgrain structures, but the applications are limited to relatively simple geometries such as thin films, nanowires or single cubic grains. To bridge that gap, a novel concurrent multiscale framework is developed herein to account for any arbitrary microstructural configuration. The proposed technique is achieved by capturing the microscale size effect on the thermal conductivity and incorporating it into the macroscale analysis. In particular, the multiscale scheme accounts for: (a) a submicron scale model for the thermal conductivity based on the Boltzmann transport equation under the relaxation time approximation, (b) a classic Fourier heat transport model at the mesoscale, and (c) a continuum model of thermomechanical deformation that explicitly resolves the microscopic geometric features of the material. The capabilities of the model are demonstrated through a series of examples, which highlight the potential of the procedure for designing materials with enhanced thermomechanical responses. Among other applications, this research uses the developed concurrent multiscale framework to analyze the influence of the microscopic features on the thermal and thermomechanical properties. In particular, the analysis of functionally graded polycrystals is performed, highlighting the influence of the grain size distribution on the temperature field and on the thermal stress distribution. From those results, further novel optimization approaches are conducted using length-dependent modeling of polycrystalline materials. More specifically, the size-dependent thermal properties obtained from the model are coupled with an adaptive topology optimization algorithm to improve the spatial grain size distribution on single-material polycrystalline systems. This technique creates a unique tool for the manufacturing of single-material systems with enhanced thermal properties. Finally, this framework provides a physically based and accurate computational tool for defining a `MFP calibrated' Kapitza resistance at the grain boundary, where both the Kapitza resistance and the intragranular thermal conductivity are size-dependent. This particular study highlights the benefits of the innovative concurrent multiscale framework for the study of thermomechanical phenomena. It notably allows to recover the accurate effective thermal conductivity of polycrystals. Additionally, it reduces spurious temperature jumps and related thermal stresses at the grain boundary, that appears to be artifacts of the current numerical approaches.