Tato diplomová práce se zabývá návrhem mechanismu řízení letounu TL 4000, určení sil působících na jednotlivé součásti řízení a jejich pevnostní analýzou. V této práci jsou vypracovány všechny tři složky řízení a to tak, aby vyhovovaly stavebnímu předpisu CS 23.

<div><div><div><p>The exceptional long-term performance of concrete is a primary reason that this material represents a significant portion of the construction industry. However, a portion of this construction material is prone to premature deterioration for multi-physical durability issues such as internal frost damage, restrained shrinkage damage, and aggregate susceptibility to fracture. Since each durability issue is associated with a unique damage mechanism, this study aims at investigating the underlying physical mechanisms individually by characterizing the mechanical and thermal properties development and indicating how each unique damage mechanism may compromise the properties development over the design life of the material.</p><p>The first contribution of this work is on the characterization of thermal behavior of porous media (e.g., cement-based material) with a complex solid-fluid coupling subject to thermal cycling. By combining Young-Kelvin-Laplace equation with a computational heat transfer approach, we can calculate the contributions of (i) pore pressure development associated with solidification and melting of pore fluid, (ii) pore size distribution, and (iii) equilibrium phase diagram of multiple phase change materials, to the thermal response of porous mortar and concrete during freezing/thawing cycles. Our first finding indicates that the impact of pore size (and curvature) on freezing is relatively insignificant, while the effect of pore size is much more significant during melting. The fluid inside pores smaller than 5 nm (i.e., gel pores) has a relatively small contribution in the macroscopic freeze-thaw behavior of mortar specimens within the temperature range used in this study (i.e., +24 °C to -35 °C). Our second finding shows that porous cementitious composites containing lightweight aggregates (LWAs) impregnated with an organic phase change material (PCM) as thermal energy storage (TES) agents have the significant capability of improving the freeze-thaw performance. We also find that the phase transitions associated with the freezing/melting of PCM occur gradually over a narrow temperature range (rather than an instantaneous event). The pore size effect of LWA on freezing and melting behavior of PCM is found to be relatively small. Through validation of simulation results with lab-scale experimental data, we then employ the model to investigate the effectiveness of PCMs with various transition temperatures on reducing the impact of freeze-thaw cycling within concrete pavements located in different regions of United States.</p><div><div><div><p>The second contribution of this work is on quantification of mechanical properties development of cementitious composites across multiple length scales, and two damage mechanisms associated with aggregate fracture and restrained shrinkage cracking that lead to compromising the long-term durability of the material. The former issue is addressed by combining finite element method-based numerical tools, computational homogenization techniques, and analytical methods, where we observe a competing fracture mechanism for early- age cracking at two length scales of mortar (meso-level) and concrete (macro-level). When the tensile strength of the cement paste is lower than the tensile strength of the aggregate phase, the crack propagates across the paste. When the tensile strength of the cement paste exceeds that of the aggregate, the cracks begin to deflect and propagate through the aggregates. As such, a critical degree of hydration (associated with a particular time) exists below which the cement paste phase is weaker than the aggregate phase at the onset of hydration. This has implications on the inference of kinetic based parameters from mechanical testing (e.g., activation energy). Next, we focus on digital fabrication of a cement paste structure with controlled architecture to allow for mitigating the intrinsic damage induced by inherent shrinkage behavior followed by extrinsic damage exerted by external loading. Our findings show that the interfaces between the printed filaments tend to behave as the first layer of protection by enabling the structure to accommodate the damage by deflecting the microcrack propagation into the stable configuration of interfaces fabricated between the filaments of first and second layers. This fracture behavior promotes the damage localization within the first layer (i.e., sacrificial layer), without sacrificing the overall strength of specimen by inhibiting the microcrack advancement into the neighboring layers, promoting a novel damage localization mechanism. This study is undertaken to characterize the shrinkage-induced internal damage in 7-day 3D-printed and cast specimens qualitatively using X-ray microtomography (μCT) technique in conjunction with multiple mechanical testing, and finite element numerical modeling. As the final step, the second layer of protection is introduced by offering an enhanced damage resistance property through employing bioinspired Bouligand architectures, promoting a damage delocalization mechanism throughout the specimen. This novel integration of damage localization-delocalization mechanisms allows the material to enhance its flaw tolerant properties and long-term durability characteristics, where the reduction in the modulus of rupture (MOR) of hardened cement paste (hcp) elements with restrained shrinkage racking has been significantly improved by ~ 25% when compared to their conventionally cast hcp counterparts.</p></div></div></div></div></div></div>

Return collectors are predominant organs for rubber-tyred subways to operatesince they ensure both the track circuit shunt and the traction current return. Po- sitioned at the interface between the track and the rolling stock, they are subjected to the disruptions linked to the train movement and the track irregularities. One of the most critical steps is the crossing of a switch nose.This study aims at determining the collector position during this crossing by means of a quasi-static analysis of the system. Two approaches are investigated. The first one brings into play a rigid contact and geometrical angles. It enables to model the crossing until the contact with the crossing nose. The diving capability of the collector is also taken into account. The second one is a standard  approach of the contact. A slight penetration is considered, which allows to grasp the contact with  the crossing nose. The second advantage  is to prepare the ground for a complete dynamical analysis. Both approaches are then implemented on Matlab to solve the equations. Finally the study of the switch crossing in nominal conditionsand a parametric analysis are achieved for a specified switch.

<div>An important challenge in predicting fatigue and creep crack growth is describing crack growth rates under transient conditions. Transient conditions occur when similitude is violated at the crack tip due to the applied loads or material behavior. Crack growth models like the Paris law, valid for homogeneous materials under constant-amplitude cyclic loading or sustained loading, no longer apply. Transient crack growth rates are strongly influenced by changes in plastic deformation at the crack tip. Activation of time-dependent damage and viscoplastic deformation at high temperatures further complicates the problem.</div><div><br></div><div>This thesis advances knowledge and predictive capabilities for transient creep and fatigue crack growth in metals, with specific applications to two technologically-relevant nickel-base superalloys. Finite element computations of crack growth following overloads and in multilayered materials are conducted. Crack extension is an outcome of the boundary value problem through an irreversible cohesive zone model and its interaction with plasticity and viscoplasticity in the bulk material.</div><div><br></div><div>First, fatigue crack growth in rate-independent materials is analyzed. The plasticity formulation considers both plastic strain and gradients of plastic strain, which produce hardening beyond that predicted by classical plasticity models. The computations demonstrate that hardening due to plastic strain gradients plays a significant role in transient fatigue crack growth following overloads. Fatigue crack growth transients associated with material inhomogeneity are studied through the case of a crack growing toward interfaces between plastically dissimilar materials. Interactions between the interface strength and the yield strength mismatch are found to govern crack growth rates near the interface. Hardening due to plastic strain gradients is important for finding the critical conditions associated with crack bifurcation at an interface and penetration through an interlayer.</div><div><br></div><div>Subsequently, crack growth in rate-dependent materials is analyzed. For materials characterized by power-law viscoplasticity, fatigue crack growth rates following overloads are found to depend strongly on the material rate sensitivity. The computations predict a transition from acceleration- to retardation-dominated post-overload crack growth as the rate sensitivity decreases. The predicted post-overload crack growth rates show good agreement with high-temperature experimentally-measured trends for Alloy 617, a solid solution strengthened nickel-base superalloy proposed for use in next-generation nuclear power plants. The results demonstrate why Alloy 617 behaves in a relatively brittle manner following overloads despite being characterized as a creep-ductile material. Crack growth is also studied in materials where rate dependence is captured through time-dependent damage and dislocation storage and dynamic recovery processes. This approach is relevant for high-strength creep-brittle materials, in which the viscoplastic zone grows with the advancing crack. The computations predict crack growth retardation for several loading waveforms containing overloads. The amount of retardation depends strongly on the overload ratio and subsequent unloading ahead of the crack tip. The predicted post-overload crack extension shows good agreement with high-temperature experimentally-measured trends for Alloy 718, a precipitation-hardened nickel-base superalloy used in turbine engines and power generation applications. The results demonstrate why Alloy 718 behaves in a ductile manner following overloads, despite being characterized as a creep-brittle material.</div>

The mathematical characterization of the sound of a musical instrument still follows Schumann’s laws [1]. According to this theory, the resonances of the instrument body, “the formants”, filter the oscillations of the sound generator (e.g., strings) and produce the characteristic “timbre” of an instrument. This is a strong simplification of the actual situation. It applies to a point source and does not distinguish between a loudspeaker and a three-dimensional instrument. In this work we investigate Finite-Element-based numerical simulations of eigenfrequencies and eigenmodes of a tuning fork in order to capture the oscillation behavior of its eigenfrequencies. We model the tuning fork as an elastic solid body and solve an eigenvalue equation derived from a system of coupled equations from linear elasticity theory on an unstructured three-dimensional grid. The eigenvalue problem is solved using the preconditioned inverse iteration (PINVIT) method with an efficient geometric multigrid (GMG) preconditioner. The latter allows us to resolve the tuning fork with a high resolution grid, which is required to capture fine modes of the simulated eigenfrequencies. To verify our results, we compare them with measurement data obtained from an experimental modal analyses of a real reference tuning fork. It turns out that our model is sufficient to capture the first eight eigenmodes of a reference tuning fork, whose identification and reproduction by simulation is novel to the knowledge of the author.

<p>Composite materials have been successfully applied in various industries, such as aerospace, automobile, and wind turbines, etc. Although the material properties of composites are desirable, the behaviors of composites are complicated. Many efforts have been made to model the constitutive behavior and failure of composites, but a complete and validated methodology has not been completely achieved yet. Recently, machine learning techniques have attracted many researchers from the mechanics field, who are seeking to construct surrogate models with machine learning, such as deep neural networks (DNN), to improve the computational speed or employ machine learning to discover unknown governing laws to improve the accuracy. Currently, the majority of studies mainly focus on improving computational speed. Few works focus on applying machine learning to discover unknown governing laws from experimental data.  In this study, we will demonstrate the implementation of machine learning to discover unknown governing laws of composites. Additionally, we will also present an application of machine learning to accelerate the design optimization of a composite rotor blade.</p> <p><br></p> <p>To enable the machine learning model to discover constitutive laws directly from experimental data, we proposed a framework to couple finite element (FE) with DNN to form a fully coupled mechanics system FE-DNN. The proposed framework enables data communication between FE and DNN, which takes advantage of the powerful learning ability of DNN and the versatile problem-solving ability of FE. To implement the framework to composites, we introduced positive definite deep neural network (PDNN) to the framework to form FE-PDNN, which solves the convergence robustness issue of learning the constitutive law of a severely damaged material. In addition, the lamination theory is introduced to the FE-PDNN mechanics system to enable FE-PDNN to discover the lamina constitutive law based on the structural level responses.</p> <p><br></p> <p>We also developed a framework that combines sparse regression with compressed sensing, which leveraging advances in sparsity techniques and machine learning, to discover the failure criterion of composites from experimental data. One advantage of the proposed approach is that this framework does not need Bigdata to train the model. This feature satisfies the current failure data size constraint. Unlike the traditional curve fitting techniques, which results in a solution with nonzero coefficients in all the candidate functions. This framework can identify the most significant features that govern the dataset. Besides, we have conducted a comparison between sparse regression and DNN to show the superiority of sparse regression under limited dataset. Additionally, we used an optimization approach to enforce a constraint to the discovered criterion so that the predicted data to be more conservative than the experimental data. This modification can yield a conservative failure criterion to satisfy the design needs.</p> <p><br></p> <p>Finally, we demonstrated employing machine learning to accelerate the planform design of a composite rotor blade with strength consideration. The composite rotor blade planform design focuses on optimizing planform parameters to achieve higher performance. However, the strength of the material is rarely considered in the planform design, as the physic-based strength analysis is expensive since millions of load cases can be accumulated during the optimization. Ignoring strength analysis may result in the blade working in an unsafe or low safety factor region since composite materials are anisotropic and susceptible to failure. To reduce the computational cost of the blade cross-section strength analysis, we proposed to construct a surrogate model using the artificial neural network (ANN) for beam level failure criterion to replace the physics-based strength analysis. The surrogate model is constructed based on the Timoshenko beam model, where the mapping is between blade loads and the strength ratios of the cross-section. The results showed that the surrogate model constraint using machine learning can achieve the same accuracy as the physics-based simulation while the computing time is significantly reduced. </p>

<p>Structures with inherent shape change capabilities enable adaptive, efficient designs without the weight and complexity of external actuators and sensors. Morphing structures are found in nature: plants are able to achieve fast motion without muscular or nervous systems. For example, the Venus flytrap snaps to a closed state with spatially distributed curvatures in less than one second. In contrast, synthetic shape change has been limited by a trade-off between complexity and speed. Shape memory polymers (SMPs) can remember complex shapes, but morphing is slow and one-way. Multistability due to mechanical buckling is fast and reversible, but it has been limited to simple shapes. Furthermore, many examples of biological shape change follow logical patterns with mechanisms that selectively respond to environmental stimuli. This suggests that synthetic morphing structures may also lend themselves to alternative forms of sensing, memory, and logic.</p> <p><br></p> <p>In this research, we introduce a new method of using SMPs in combination with the hierarchical architectures of pre-strained multistable laminates to create switchable multistable structures (SMS). An SMS can remember multiple permanent shapes and reversibly snap between them. We use extrusion-based 3D printing to encode contrasting shape memory-based pre-strain fields in a bilayer. Above the SMP’s glass transition temperature, the SMS becomes compliant and remembers multiple encoded permanent shapes with fast snap-through between them. Below the transition temperature, the SMS regains its stiffness and is fixed in a single state. The geometric freedom of 3D printing enables the design and manufacture of bioinspired structures with complex pre-strain fields and deflections. The developed printing method is applied in multiple subsequent studies, including mechanical pixels, self-folding spring origami structures, and multistable structures printed with thermoset composite inks. </p> <p><br></p> <p>The highly nonlinear behavior of bistable, pre-strained structures makes their design difficult and nonintuitive. Generally, these structures are designed using a slow, iterative process with finite element analysis (FEA). We aim to solve the inverse optimization problem: start with target stable states and solve for the necessary pre-strain distributions. To this end, we develop and implement the switching tunneling method (STM) to design pre-strained,</p> <p>multistable structures. Instead of FEA, we leverage analytical solutions for gradient-based optimization. Tunneling allows for the efficient search of a design space which may contain multiple local and global minima. Switching enables us to take advantage of two different function transformations, depending on if the search is far from or close to a minimum. The STM is validated through FEA and experiments for both conventional and variable</p> <p>pre-strain bistable structures.</p> <p><br></p> <p>Structures designed to react to external conditions or events offer the opportunity to directly integrate sensing, memory, and computation into a structure. This concept is explored using metasheets composed of locally bistable unit cells, which display spatiotemporal mechanical sensing (mechanosensing) and memory. A unit cell consists of a bistable dome with a piezoresistive strip at the base; the resistance indicates the state of the dome. The mechanics of bistability offer inherent filtering and nonlinear signal amplification capabilities, tunable via geometric parameters. Metasheet arrays of these unit cells display distributed sensing capabilities, as well as hierarchical multistability.</p> <p><br></p> <p>We explore the use of time-dependent material properties combined with the mechanics of multistability to encode many unique values within a single mechanosensor unit cell, beyond binary memory. When the piezoresistive material is viscoelastic, cyclic loading causes cumulative changes in both the ground and inverted state resistances. Effectively, the metamaterial is able to count how many times an external force has been applied; this count is stored in the metamaterial’s intrinsic, measurable properties.</p> <p><br></p> <p>This work demonstrates the importance of incorporating memory concepts into structural design, which enables multistability with complex stable shapes, as well as spatiotemporal sensing and memory capabilities. Engineered systems require increasingly adaptive and responsive structures to improve efficiency. The incorporation of inherent memory and sensing enables the complex behaviors needed to interact with unstructured environments</p> <p>and biological features, a pressing issue for aerospace, soft robotics and biomedical devices. The methodology developed here to manufacture, design, and analyze multistable structures advances the state of the art and makes their implementation more practical.</p>

<p> Self-organized dislocation structures in deforming metals have a strong influence on the mechanical response of metals. However, accurate prediction of these patterns remains a challenge due to the complex dynamic and multiscale nature of the underlying process. This dissertation focuses on the development of a theoretical framework for continuum dislocation dynamics (CDD) models to predict dislocation microstructure formation at small strains, along with corresponding numerical simulation results. CDD models have the capability to incorporate plasticity physics spanning different time and length scales while capturing the dislocation motion explicitly within reasonable computational time. A typical model consists of two components: crystal mechanics, formulated as an eigenstrain problem, and dislocation dynamics, treated as a transport-reaction problem. In the first part of the thesis, a novel framework is introduced to solve the dislocation transport by decoupling the system of transport-reaction equations and enforcing the dislocation continuity constraint on individual slip systems. The results obtained from this framework demonstrate high accuracy and computational efficiency, significantly enhancing the predictive capabilities of the model. Building upon the framework, a statistical analysis of stress fluctuations in discrete dislocation dynamics (DDD) simulations is conducted to understand the relationship between coarse-grained average stress and local stress states. This analysis is motivated by the need to accurately capture dislocation reactions, such as cross-slip, which strongly depend on the local stress state, using the coarse-grained approach in CDD. The results revealed that the difference between the local and the coarse-grained states can be characterized using a Cauchy distribution. Consequently, a novel strategy is proposed to incorporate these statistical characteristics into the CDD model, yielding cross-slip rate predictions that align well with DDD results. In the final part of the study, the developed framework is applied to investigate the dislocation pattern formation during the early stages of cyclic loading. The simulation results successfully capture the formation of dislocation vein like structure and provide insights regarding the formation of labyrinth structure observed in experiments during cyclic loading at saturated state. </p>

<p>The mechanical behavior of tissues at the macroscale is tightly coupled to cellular activity at the microscale and tuned by microstructure at the mesoscale. Dermal wound healing is a prominent example of a complex system in which multiscale mechanics regulate restoration of tissue form and function. In cutaneous wound healing, a fibrin matrix is populated by fibroblasts migrating in from a surrounding tissue made mostly out of collagen. Fibroblasts both respond to mechanical cues such as fiber alignment and stiffness as well as exert active stresses needed for wound closure. </p> <p>To model wound healing mechanobiology, we first develop a multiscale model with a two-way coupling between a microscale cell adhesion model and a macroscale tissue mechanics model. Starting from the well-known model of adhesion kinetics proposed by Bell, we extend the formulation to account for nonlinear mechanics of fibrin and collagen and show how this nonlinear response naturally captures stretch-driven mechanosensing. We then embed the new nonlinear adhesion model into a custom finite element implementation of tissue mechanical equilibrium. Strains and stresses at the tissue level are coupled with the solution of the microscale adhesion model at each integration point of the finite element mesh. In addition, solution of the adhesion model is coupled with the active contractile stress of the cell population. The multiscale model successfully captures the mechanical response of biopolymer fibers and gels, contractile stresses generated by fibroblasts, and stress-strain contours observed during wound healing. We anticipate this framework will not only increase our understanding of how mechanical cues guide cellular behavior in cutaneous wound healing, but will also be helpful in the study of mechanobiology, growth, and remodeling in other tissues. </p> <p>Next, we develop another multiscale model with a bidirectional coupling between a microscale cell adhesion model and a mesoscale microstructure mechanics model. By mimicking the generation of fibrous network in experiment, we established a discrete fiber network model to simulate the microstructure of biopolymer gels. We then coupled the cell adhesion model to the discrete model to obtain the solution of microstructure equilibrium. This multiscale model was able to recover the volume loss of fibrous gels and the contraction from cells in the networks observed in experiment. We examined the influence of RVE size, stiffness of single fibers and stretch of the gels. We expect this work will help bridge the activity of cell to the microstructure and then to the tissue mechanics especially in wound healing. We hope this work will provide more rigorous understanding in the study of mechanobiology.</p> <p>At last, we established a computational model to accurately capture the mechanical response of fibrin gels which is a naturally occurring protein network that forms a temporary structure to enable remodeling during wound healing and a common tissue engineering scaffold due to the controllable structural properties. We formulated a strategy to quantify both the macroscale (1–10 mm) stress-strain response and the deformation of the mesoscale (10–1000 microns) network structure during unidirectional tensile tests. Based on the experimental data, we successfully predict the strain fields that were observed experimentally within heterogenous fibrin gels with spatial variations in material properties by developing a hyper-viscoelastic model with non-affined evolution under stretching. This model is also potential to predict the macroscale mechanics and mesoscale network organization of other heterogeneous biological tissues and matrices.</p>

<p>This Dissertation aims at establishing an integrated framework of multimodal experiments and multiphysics theory to extend the understanding of the mechanics in electrochemically active materials using organic mixed ionic-electronic conductors (OMIECs) as a model system. </p> <p>OMIECs allow the transport of both ions and electrons, which is accompanied by the (electronic, micro-) structural reorganization. The electronic structural change in OMIECs induces transforms in the electrical conductivity and optical absorbance. The change in molecular packing invites the size change and evolution of mechanical properties. The multiphysics processes render OMIECs a fascinating platform for understanding the multi-physics coupling and advancing organic electrochemical devices. </p> <p>Despite significant progress, there are urgent needs in the experimental techniques and the subsequent mechanical characterization, theoretical understanding of the multiphysics processes, and mechanics-informed design principles for high-performance devices. Specifically, (i) an accurate and straightforward experimental method is in need to better understand the mechanical behaviors and kinetics such as swelling and softening of OMIECs upon electrochemical redox reactions; (ii) a theoretical framework is missing that describes the rich coupled multiphysics processes such as large deformation, charge and mass transport, electrostatics, and phase evolution in OMIECs; (iii) the rational design of the materials and structures based on mechanics principles are required for mechanically reliable, high-performance organic electrochemical devices.</p> <p>In this Dissertation, the mechanics of OMIECs are studied systematically. The basics of OMIECs, knowledge gaps, and the outline are introduced in Chapter 1. The in-situ environmental nanoindentation apparatus and the associating characterization techniques are presented in Chapter 2. In Chapter 3, a theoretical mechanics model is presented that elucidates the interfacial mechanical degradation of thin-film electrodes and outlines the design principles for mechanically reliable electrodes. In Chapter 4, the electrochemical doping kinetics and its stress dependency on conductive polymers are studied via a designed moving front device. Chapter 5 presents a thermodynamically consistent continuum theory of two-phase OMIECs undergoing large deformation, charge and mass transport, electrostatics, and phase separation, which forms the theoretical foundation for such conductive polymer systems. The conclusion and perspectives on future work are presented in Chapter 6. </p>