@phdthesis{Kavrakov,
  author    = {Kavrakov, Igor},
  title     = {Synergistic Framework for Analysis and Model Assessment in Bridge Aerodynamics and Aeroelasticity},
  publisher = {Bauhaus-Universit{\"a}tsverlag},
  address   = {Weimar},
  isbn      = {978-3-95773-284-2},
  doi       = {10.25643/bauhaus-universitaet.4109},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200316-41099},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {314},
  abstract  = {Wind-induced vibrations often represent a major design criterion for long-span bridges. This work deals with the assessment and development of models for aerodynamic and aeroelastic analyses of long-span bridges. Computational Fluid Dynamics (CFD) and semi-analytical aerodynamic models are employed to compute the bridge response due to both turbulent and laminar free-stream. For the assessment of these models, a comparative methodology is developed that consists of two steps, a qualitative and a quantitative one. The first, qualitative, step involves an extension of an existing approach based on Category Theory and its application to the field of bridge aerodynamics. Initially, the approach is extended to consider model comparability and completeness. Then, the complexity of the CFD and twelve semi-analytical models are evaluated based on their mathematical constructions, yielding a diagrammatic representation of model quality. In the second, quantitative, step of the comparative methodology, the discrepancy of a system response quantity for time-dependent aerodynamic models is quantified using comparison metrics for time-histories. Nine metrics are established on a uniform basis to quantify the discrepancies in local and global signal features that are of interest in bridge aerodynamics. These signal features involve quantities such as phase, time-varying frequency and magnitude content, probability density, non-stationarity, and nonlinearity. The two-dimensional (2D) Vortex Particle Method is used for the discretization of the Navier-Stokes equations including a Pseudo-three dimensional (Pseudo-3D) extension within an existing CFD solver. The Pseudo-3D Vortex Method considers the 3D structural behavior for aeroelastic analyses by positioning 2D fluid strips along a line-like structure. A novel turbulent Pseudo-3D Vortex Method is developed by combining the laminar Pseudo-3D VPM and a previously developed 2D method for the generation of free-stream turbulence. Using analytical derivations, it is shown that the fluid velocity correlation is maintained between the CFD strips. Furthermore, a new method is presented for the determination of the complex aerodynamic admittance under deterministic sinusoidal gusts using the Vortex Particle Method. The sinusoidal gusts are simulated by modeling the wakes of flapping airfoils in the CFD domain with inflow vortex particles. Positioning a section downstream yields sinusoidal forces that are used for determining all six components of the complex aerodynamic admittance. A closed-form analytical relation is derived, based on an existing analytical model. With this relation, the inflow particles' strength can be related with the target gust amplitudes a priori. The developed methodologies are combined in a synergistic framework, which is applied to both fundamental examples and practical case studies. Where possible, the results are verified and validated. The outcome of this work is intended to shed some light on the complex wind-bridge interaction and suggest appropriate modeling strategies for an enhanced design.},
  subject      = {Br{\"u}cke},
  language  = {en}
}
@phdthesis{Oucif,
  author    = {Oucif, Chahmi},
  title     = {Analytical Modeling of Self-Healing and Super Healing in Cementitious Materials},
  doi       = {10.25643/bauhaus-universitaet.4229},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200831-42296},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {208},
  abstract  = {Self-healing materials have recently become more popular due to their capability to autonomously and autogenously repair the damage in cementitious materials. The concept of self-healing gives the damaged material the ability to recover its stiffness. This gives a difference in comparing with a material that is not subjected to healing. Once this material is damaged, it cannot sustain loading due to the stiffness degradation. Numerical modeling of self-healing materials is still in its infancy. Multiple experimental researches were conducted in literature to describe the behavior of self-healing of cementitious materials. However, few numerical investigations were undertaken. The thesis presents an analytical framework of self-healing and super healing materials based on continuum damage-healing mechanics. Through this framework, we aim to describe the recovery and strengthening of material stiffness and strength. A simple damage healing law is proposed and applied on concrete material. The proposed damage-healing law is based on a new time-dependent healing variable. The damage-healing model is applied on isotropic concrete material at the macroscale under tensile load. Both autonomous and autogenous self-healing mechanisms are simulated under different loading conditions. These two mechanisms are denoted in the present work by coupled and uncoupled self-healing mechanisms, respectively. We assume in the coupled self-healing that the healing occurs at the same time with damage evolution, while we assume in the uncoupled self-healing that the healing occurs when the material is deformed and subjected to a rest period (damage is constant). In order to describe both coupled and uncoupled healing mechanisms, a one-dimensional element is subjected to different types of loading history. In the same context, derivation of nonlinear self-healing theory is given, and comparison of linear and nonlinear damage-healing models is carried out using both coupled and uncoupled self-healing mechanisms. The nonlinear healing theory includes generalized nonlinear and quadratic healing models. The healing efficiency is studied by varying the values of the healing rest period and the parameter describing the material characteristics. In addition, theoretical formulation of different self-healing variables is presented for both isotropic and anisotropic maerials. The healing variables are defined based on the recovery in elastic modulus, shear modulus, Poisson's ratio, and bulk modulus. The evolution of the healing variable calculated based on cross-section as function of the healing variable calculated based on elastic stiffness is presented in both hypotheses of elastic strain equivalence and elastic energy equivalence. The components of the fourth-rank healing tensor are also obtained in the case of isotropic elasticity, plane stress and plane strain. Recent research revealed that self-healing presents a crucial solution also for the strengthening of the materials. This new concept has been termed ``Super Healing``. Once the stiffness of the material is recovered, further healing can result as a strengthening material. In the present thesis, new theory of super healing materials is defined in isotropic and anisotropic cases using sound mathematical and mechanical principles which are applied in linear and nonlinear super healing theories. Additionally, the link of the proposed theory with the theory of undamageable materials is outlined. In order to describe the super healing efficiency in linear and nonlinear theories, the ratio of effective stress to nominal stress is calculated as function of the super healing variable. In addition, the hypotheses of elastic strain and elastic energy equivalence are applied. In the same context, new super healing matrix in plane strain is proposed based on continuum damage-healing mechanics. In the present work, we also focus on numerical modeling of impact behavior of reinforced concrete slabs using the commercial finite element package Abaqus/Explicit. Plain and reinforced concrete slabs of unconfined compressive strength 41 MPa are simulated under impact of ogive-nosed hard projectile. The constitutive material modeling of the concrete and steel reinforcement bars is performed using the Johnson-Holmquist-2 damage and the Johnson-Cook plasticity material models, respectively. Damage diameters and residual velocities obtained by the numerical model are compared with the experimental results and effect of steel reinforcement and projectile diameter is studied.},
  subject      = {Schaden},
  language  = {en}
}
@phdthesis{RadmardRahmani,
  author    = {Radmard Rahmani, Hamid},
  title     = {Artificial Intelligence Approach for Seismic Control of Structures},
  doi       = {10.25643/bauhaus-universitaet.4135},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200417-41359},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties.},
  subject      = {Erdbeben},
  language  = {en}
}
@phdthesis{Rabizadeh,
  author    = {Rabizadeh, Ehsan},
  title     = {Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems},
  doi       = {10.25643/bauhaus-universitaet.4286},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201113-42864},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {In recent years, substantial attention has been devoted to thermoelastic multifield problems and their numerical analysis. Thermoelasticity is one of the important categories of multifield problems which deals with the effect of mechanical and thermal disturbances on an elastic body. In other words, thermoelasticity encompasses the phenomena that describe the elastic and thermal behavior of solids and their interactions under thermo-mechanical loadings. Since providing an analytical solution for general coupled thermoelasticity problems is mathematically complicated, the development of alternative numerical solution techniques seems essential. Due to the nature of numerical analysis methods, presence of error in results is inevitable, therefore in any numerical simulation, the main concern is the accuracy of the approximation. There are different error estimation (EE) methods to assess the overall quality of numerical approximation. In many real-life numerical simulations, not only the overall error, but also the local error or error in a particular quantity of interest is of main interest. The error estimation techniques which are developed to evaluate the error in the quantity of interest are known as "goal-oriented" error estimation (GOEE) methods. This project, for the first time, investigates the classical a posteriori error estimation and goal-oriented a posteriori error estimation in 2D/3D thermoelasticity problems. Generally, the a posteriori error estimation techniques can be categorized into two major branches of recovery-based and residual-based error estimators. In this research, application of both recovery- and residual-based error estimators in thermoelasticity are studied. Moreover, in order to reduce the error in the quantity of interest efficiently and optimally in 2D and 3D thermoelastic problems, goal-oriented adaptive mesh refinement is performed. As the first application category, the error estimation in classical Thermoelasticity (CTE) is investigated. In the first step, a rh-adaptive thermo-mechanical formulation based on goal-oriented error estimation is proposed.The developed goal-oriented error estimation relies on different stress recovery techniques, i.e., the superconvergent patch recovery (SPR), L2-projection patch recovery (L2-PR), and weighted superconvergent patch recovery (WSPR). Moreover, a new adaptive refinement strategy (ARS) is presented that minimizes the error in a quantity of interest and refines the discretization such that the error is equally distributed in the refined mesh. The method is validated by numerous numerical examples where an analytical solution or reference solution is available. After investigating error estimation in classical thermoelasticity and evaluating the quality of presented error estimators, we extended the application of the developed goal-oriented error estimation and the associated adaptive refinement technique to the classical fully coupled dynamic thermoelasticity. In this part, we present an adaptive method for coupled dynamic thermoelasticity problems based on goal-oriented error estimation. We use dimensionless variables in the finite element formulation and for the time integration we employ the acceleration-based Newmark-_ method. In this part, the SPR, L2-PR, and WSPR recovery methods are exploited to estimate the error in the quantity of interest (QoI). By using adaptive refinement in space, the error in the quantity of interest is minimized. Therefore, the discretization is refined such that the error is equally distributed in the refined mesh. We demonstrate the efficiency of this method by numerous numerical examples. After studying the recovery-based error estimators, we investigated the residual-based error estimation in thermoelasticity. In the last part of this research, we present a 3D adaptive method for thermoelastic problems based on goal-oriented error estimation where the error is measured with respect to a pointwise quantity of interest. We developed a method for a posteriori error estimation and mesh adaptation based on dual weighted residual (DWR) method relying on the duality principles and consisting of an adjoint problem solution. Here, we consider the application of the derived estimator and mesh refinement to two-/three-dimensional (2D/3D) thermo-mechanical multifield problems. In this study, the goal is considered to be given by singular pointwise functions, such as the point value or point value derivative at a specific point of interest (PoI). An adaptive algorithm has been adopted to refine the mesh to minimize the goal in the quantity of interest. The mesh adaptivity procedure based on the DWR method is performed by adaptive local h-refinement/coarsening with allowed hanging nodes. According to the proposed DWR method, the error contribution of each element is evaluated. In the refinement process, the contribution of each element to the goal error is considered as the mesh refinement criterion. In this study, we substantiate the accuracy and performance of this method by several numerical examples with available analytical solutions. Here, 2D and 3D problems under thermo-mechanical loadings are considered as benchmark problems. To show how accurately the derived estimator captures the exact error in the evaluation of the pointwise quantity of interest, in all examples, considering the analytical solutions, the goal error effectivity index as a standard measure of the quality of an estimator is calculated. Moreover, in order to demonstrate the efficiency of the proposed method and show the optimal behavior of the employed refinement method, the results of different conventional error estimators and refinement techniques (e.g., global uniform refinement, Kelly, and weighted Kelly techniques) are used for comparison.},
  subject      = {Mesh Refinement},
  language  = {en}
}
@phdthesis{Liu,
  author    = {Liu, Bokai},
  title     = {Stochastic multiscale modeling of polymeric nanocomposites using Data-driven techniques},
  doi       = {10.25643/bauhaus-universitaet.4637},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220503-46379},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {134},
  abstract  = {In recent years, lightweight materials, such as polymer composite materials (PNCs) have been studied and developed due to their excellent physical and chemical properties. Structures composed of these composite materials are widely used in aerospace engineering structures, automotive components, and electrical devices. The excellent and outstanding mechanical, thermal, and electrical properties of Carbon nanotube (CNT) make it an ideal filler to strengthen polymer materials' comparable properties. The heat transfer of composite materials has very promising engineering applications in many fields, especially in electronic devices and energy storage equipment. It is essential in high-energy density systems since electronic components need heat dissipation functionality. Or in other words, in electronic devices the generated heat should ideally be dissipated by light and small heat sinks. Polymeric composites consist of fillers embedded in a polymer matrix, the first ones will significantly affect the overall (macroscopic) performance of the material. There are many common carbon-based fillers such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), carbon nanobuds (CNB), fullerene, and graphene. Additives inside the matrix have become a popular subject for researchers. Some extraordinary characters, such as high-performance load, lightweight design, excellent chemical resistance, easy processing, and heat transfer, make the design of polymeric nanotube composites (PNCs) flexible. Due to the reinforcing effects with different fillers on composite materials, it has a higher degree of freedom and can be designed for the structure according to specific applications' needs. As already stated, our research focus will be on SWCNT enhanced PNCs. Since experiments are timeconsuming, sometimes expensive and cannot shed light into phenomena taking place for instance at the interfaces/interphases of composites, they are often complemented through theoretical and computational analysis. While most studies are based on deterministic approaches, there is a comparatively lower number of stochastic methods accounting for uncertainties in the input parameters. In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. However, uncertainties in the input parameters such as aspect ratio, volume fraction, thermal properties of fiber and matrix need to be taken into account for reliable predictions. In this research, a stochastic multiscale method is provided to study the influence of numerous uncertain input parameters on the thermal conductivity of the composite. Therefore, a hierarchical multi-scale method based on computational homogenization is presented in to predict the macroscopic thermal conductivity based on the fine-scale structure. In order to study the inner mechanism, we use the finite element method and employ surrogate models to conduct a Global Sensitivity Analysis (GSA). The SA is performed in order to quantify the influence of the conductivity of the fiber, matrix, Kapitza resistance, volume fraction and aspect ratio on the macroscopic conductivity. Therefore, we compute first-order and total-effect sensitivity indices with different surrogate models. As stochastic multiscale models are computational expensive, surrogate approaches are commonly exploited. With the emergence of high performance computing and artificial intelligence, machine learning has become a popular modeling tool for numerous applications. Machine learning (ML) is commonly used in regression and maps data through specific rules with algorithms to build input and output models. They are particularly useful for nonlinear input-output relationships when sufficient data is available. ML has also been used in the design of new materials and multiscale analysis. For instance, Artificial neural networks and integrated learning seem to be ideally for such a task. They can theoretically simulate any non-linear relationship through the connection of neurons. Mapping relationships are employed to carry out data-driven simulations of inputs and outputs in stochastic modeling. This research aims to develop a stochastic multi-scale computational models of PNCs in heat transfer. Multi-scale stochastic modeling with uncertainty analysis and machine learning methods consist of the following components: -Uncertainty Analysis. A surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the 'macroscopic' conductivity of the composite is systematically studied. All selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS). -Hybrid Machine Learning Algorithms. A combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. The thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio are selected as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks. -Stochastic Integrated Machine Learning. A stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in PNCs is developed. Seven types of machine learning models are exploited in this research, namely Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling to construct the relationship between the variable of the inputs' uncertainty and the macroscopic thermal conductivity of PNCs. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. The advantages and disadvantages of various methods are also analyzed in terms of computing time and model complexity to finally give a recommendation for the applicability of different models.},
  subject      = {Polymere},
  language  = {en}
}
@phdthesis{Habtemariam,
  author    = {Habtemariam, Abinet Kifle},
  title     = {Generalized Beam Theory for the analysis of thin-walled circular pipe members},
  doi       = {10.25643/bauhaus-universitaet.4572},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220127-45723},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {188},
  abstract  = {The detailed structural analysis of thin-walled circular pipe members often requires the use of a shell or solid-based finite element method. Although these methods provide a very good approximation of the deformations, they require a higher degree of discretization which causes high computational costs. On the other hand, the analysis of thin-walled circular pipe members based on classical beam theories is easy to implement and needs much less computation time, however, they are limited in their ability to approximate the deformations as they cannot consider the deformation of the cross-section. This dissertation focuses on the study of the Generalized Beam Theory (GBT) which is both accurate and efficient in analyzing thin-walled members. This theory is based on the separation of variables in which the displacement field is expressed as a combination of predetermined deformation modes related to the cross-section, and unknown amplitude functions defined on the beam's longitudinal axis. Although the GBT was initially developed for long straight members, through the consideration of complementary deformation modes, which amend the null transverse and shear membrane strain assumptions of the classical GBT, problems involving short members, pipe bends, and geometrical nonlinearity can also be analyzed using GBT. In this dissertation, the GBT formulation for the analysis of these problems is developed and the application and capabilities of the method are illustrated using several numerical examples. Furthermore, the displacement and stress field results of these examples are verified using an equivalent refined shell-based finite element model. The developed static and dynamic GBT formulations for curved thin-walled circular pipes are based on the linear kinematic description of the curved shell theory. In these formulations, the complex problem in pipe bends due to the strong coupling effect of the longitudinal bending, warping and the cross-sectional ovalization is handled precisely through the derivation of the coupling tensors between the considered GBT deformation modes. Similarly, the geometrically nonlinear GBT analysis is formulated for thin-walled circular pipes based on the nonlinear membrane kinematic equations. Here, the initial linear and quadratic stress and displacement tangent stiffness matrices are built using the third and fourth-order GBT deformation mode coupling tensors. Longitudinally, the formulation of the coupled GBT element stiffness and mass matrices are presented using a beam-based finite element formulation. Furthermore, the formulated GBT elements are tested for shear and membrane locking problems and the limitations of the formulations regarding the membrane locking problem are discussed.},
  subject      = {Finite-Elemente-Methode},
  language  = {en}
}