02 Fakultät Bau- und Umweltingenieurwissenschaften

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3

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Institute: search.filters.UBSInstitut.Institut für Mechanik (Bauwesen)Subject: search.filters.subject.670

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    The high cycle fatigue testing of High‐Performance Concretes using high frequency excitation
    (2023) Madadi, Hamid; Steeb, Holger
    The effect of fatigue failure in brittle materials like (ultra) High Performance Concrete (UHPC) due to cyclic loading causes unexpected failure that consequently results in heavy costs in marine and civil structures. To characterize the effect of fatigue, cyclic loading tests are performed, and “the number of cycles to failure” are experimentally determined. One problem with these kinds of tests is that such experimental investigations are potentially expensive, i.e., time‐consuming process since the number of loading cycles could be extremely high. Further, within the different damage phases of the cycling tests, one has no access to the small‐scale, i.e., microscopical evolution of (micro‐)cracks. Additionally, a full characterization of the small‐strain stiffness evolution of the material is challenging. The goal of the research investigation is to combine a (large amplitude) High Cycle Fatigue experiment with a (low amplitude) Dynamic Mechanical Analysis (DMA). Using a setup based on the piezoelectric actuator, the (rate‐dependent) mechanical properties of the material in tangential space, and the failure modes of the material will be examined accurately. The excitation frequency is between 0.01 Hz to 1000 Hz which allows for reducing the experimental investigation time to failure. Further, it allows investigating the effect of frequency on the number of cycles to failure. Firstly, experimental results for HPC and berea sandstone samples will be presented. Harmonic experimental data include (direct) strain measurements in axial and circumferential directions as well as forces in axial directions. In addition, the resulting complex Young's modulus and evolving damage‐like “history” of HPC and berea sandstone specimens will be shown.
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    Hydraulically induced fracturing in heterogeneous porous media using a TPM‐phase‐field model and geostatistics
    (2023) Wagner, Arndt; Sonntag, Alixa; Reuschen, Sebastian; Nowak, Wolfgang; Ehlers, Wolfgang
    Hydraulically induced fracturing is widely used in practice for several exploitation techniques. The chosen macroscopic model combines a phase‐field approach to fractures with the Theory of Porous Media (TPM) to describe dynamic hydraulic fracturing processes in fully‐saturated porous materials. In this regard, the solid's state of damage shows a diffuse transition zone between the broken and unbroken domain. Rocks or soils in grown nature are generally inhomogeneous with material imperfections on the microscale, such that modelling homogeneous porous material may oversimplify the behaviour of the solid and fluid phases in the fracturing process. Therefore, material imperfections and inhomogeneities in the porous structure are considered through the definition of location‐dependent material parameters. In this contribution, a deterministic approach to account for predefined imperfection areas as well as statistical fields of geomechanical properties is proposed. Representative numerical simulations show the impact of solid skeleton heterogeneities in porous media on the fracturing characteristics, e. g. the crack path.
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    DLP 4D printing of multi‐responsive bilayered structures
    (2023) Mainik, Philipp; Hsu, Li‐Yun; Zimmer, Claudius W.; Fauser, Dominik; Steeb, Holger; Blasco, Eva
    Advances in soft robotics strongly rely on the development and manufacturing of new responsive soft materials. In particular, light‐based 3D printing techniques, and especially, digital light processing (DLP), offer a versatile platform for the fast manufacturing of complex 3D/4D structures with a high spatial resolution. In this work, DLP all‐printed bilayered structures exhibiting reversible and multi‐responsive behavior are presented for the first time. For this purpose, liquid crystal elastomers (LCEs) are used as active layers and combined with a printable non‐responsive elastomer acting as a passive layer. Furthermore, selective light response is incorporated by embedding various organic dyes absorbing light at different regimes in the active layers. An in‐depth characterization of the single materials and printed bilayers demonstrates a reversible and selective response. Last, the versatility of the approach is shown by DLP printing a bilayered complex 3D structure consisting of four different materials (a passive and three different LCE active materials), which exhibit different actuation patterns when irradiated with different wavelengths of light.
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    Benchmark simulations of dense suspensions flow using computational fluid dynamics
    (2022) Haustein, Martin A.; Eslami Pirharati, Mahmoud; Fataei, Shirin; Ivanov, Dimitri; Jara Heredia, Daniel; Kijanski, Nadine; Lowke, Dirk; Mechtcherine, Viktor; Rostan, Daniel; Schäfer, Thorsten; Schilde, Carsten; Steeb, Holger; Schwarze, Rüdiger
    The modeling of fresh concrete flow is still very challenging. Nevertheless, it is of highest relevance to simulate these industrially important materials with sufficient accuracy. Often, fresh concrete is assumed to show a Bingham-behavior. In numerical simulations, regularization must be used to prevent singularities. Two different regularization models, namely the 1) Bi-viscous, and 2) Bingham-Papanastasiou are investigated. Those models can be applied to complex flows with common simulation methods, such as the Finite Volume Method (FVM), Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH). Within the scope of this investigation, two common software packages from the field of FVM, namely Ansys Fluent and OpenFOAM, COMSOL Multiphysics (COMSOL) from FEM side, and HOOMD-blue.sph from the field of SPH are used to model a reference experiment and to evaluate the modeling quality. According to the results, a good agreement of data with respect to the velocity profiles for all software packages is achieved, but on the other side there are remarkable difficulties in the viscosity calculation especially in the shear- to plug-flow transition zone. Also, a minor influence of the regularization model on the velocity profile is observed.
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    Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials
    (2021) Fernández, Mauricio; Fritzen, Felix; Weeger, Oliver
    Mechanical metamaterials such as open‐ and closed‐cell lattice structures, foams, composites, and so forth can often be parametrized in terms of their microstructural properties, for example, relative densities, aspect ratios, material, shape, or topological parameters. To model the effective constitutive behavior and facilitate efficient multiscale simulation, design, and optimization of such parametric metamaterials in the finite deformation regime, a machine learning‐based constitutive model is presented in this work. The approach is demonstrated in application to elastic beam lattices with cubic anisotropy, which exhibit highly nonlinear effective behaviors due to microstructural instabilities and topology variations. Based on microstructure simulations, the relevant material and topology parameters of selected cubic lattice cells are determined and training data with homogenized stress‐deformation responses is generated for varying parameters. Then, a parametric, hyperelastic, anisotropic constitutive model is formulated as an artificial neural network, extending a recent work of the author extending a recent work of the author, Comput Mech., 2021;67(2):653‐677. The machine learning model is calibrated with the simulation data of the parametric unit cell. The authors offer public access to the simulation data through the GitHub repository https://github.com/CPShub/sim‐data. For the calibration of the model, a dedicated sample weighting strategy is developed to equally consider compliant and stiff cells and deformation scenarios in the objective function. It is demonstrated that this machine learning model is able to represent and predict the effective constitutive behavior of parametric lattices well across several orders of magnitude. Furthermore, the usability of the approach is showcased by two examples for material and topology optimization of the parametric lattice cell.
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    Many‐scale finite strain computational homogenization via Concentric Interpolation
    (2020) Kunc, Oliver; Fritzen, Felix
    A method for efficient computational homogenization of hyperelastic materials under finite strains is proposed. Multiple spatial scales are homogenized in a recursive procedure: starting on the smallest scale, few high fidelity FE computations are performed. The resulting fields of deformation gradient fluctuations are processed by a snapshot POD resulting in a reduced basis (RB) model. By means of the computationally efficient RB model, a large set of samples of the homogenized material response is created. This data set serves as the support for the Concentric Interpolation (CI) scheme, interpolating the effective stress and stiffness. Then, the same procedure is invoked on the next larger scale with this CI surrogating the homogenized material law. A three‐scale homogenization process is completed within few hours on a standard workstation. The resulting model is evaluated within minutes on a laptop computer in order to generate fourth‐scale results. Open source code is provided.