02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
Browse
7 results
Search Results
Item Open Access Multiscale modeling and stability analysis of soft active materials : from electro- and magneto-active elastomers to polymeric hydrogels(Stuttgart : Institute of Applied Mechanics, 2023) Polukhov, Elten; Keip, Marc-André (Prof. Dr.-Ing.)This work is dedicated to modeling and stability analysis of stimuli-responsive, soft active materials within a multiscale variational framework. In particular, composite electro- and magneto-active polymers and polymeric hydrogels are under consideration. When electro- and magneto-active polymers (EAP and MAP) are fabricated in the form of composites, they comprise at least two phases: a polymeric matrix and embedded electric or magnetic particles. As a result, the obtained composite is soft, highly stretchable, and fracture resistant like polymer and undergoes stimuli-induced deformation due to the interaction of particles. By designing the microstructure of EAP or MAP composites, a compressive or a tensile deformation can be induced under electric or magnetic fields, and also coupling response of the composite can be enhanced. Hence, these materials have found applications as sensors, actuators, energy harvesters, absorbers, and soft, programmable, smart devices in various areas of engineering. Similarly, polymeric hydrogels are also stimuli-responsive materials. They undergo large volumetric deformations due to the diffusion of a solvent into the polymer network of hydrogels. In this case, the obtained material shows the characteristic behavior of polymer and solvent. Therefore, these materials can also be considered in the form of composites to enhance the response further. Since hydrogels are biocompatible materials, they have found applications as contact lenses, wound dressings, drug encapsulators and carriers in bio-medicine, among other similar applications of electro- and magneto-active polymers. All above mentioned favorable features of these materials, as well as their application possibilities, make it necessary to develop mathematical models and numerical tools to simulate the response of them in order to design pertinent microstructures for particular applications as well as understand the observed complex patterns such as wrinkling, creasing, snapping, localization or pattern transformations, among others. These instabilities are often considered as failure points of materials. However, many recent works take advantage of instabilities for smart applications. Investigation of these instabilities and prediction of their onset and mode are some of the main goals of this work. In this sense, the thesis is organized into three main parts. The first part is devoted to the state of the art in the development, fabrication, and modeling of soft active materials as well as the continuum mechanical description of the magneto-electro-elasticity. The second part is dedicated to multiscale instabilities in electro- and magneto-active polymer composites within a minimization-type variational homogenization setting. This means that the highly heterogeneous problem is not resolved on one scale due to computational inefficiency but is replaced by an equivalent homogeneous problem. The effective response of the macroscopic homogeneous problem is determined by solving a microscopic representative volume element which includes all the geometrical and material non-linearities. To bridge these two scales, the Hill-Mandel macro-homogeneity condition is utilized. Within this framework, we investigate both macroscopic and microscopic instabilities. The former are important not only from a physical point of view but also from a computational point of view since the macroscopic stability (strong ellipticity) is necessary for the existence of minimizers at the macroscopic scale. Similarly, the investigation of the latter instabilities are also important to determine the pattern transformations at the microscale due to external action. Thereby the critical domain of homogenization is also determined for computation of accurate effective results. Both investigations are carried out for various composite microstructures and it is found that they play a crucial role in the response of the materials. Therefore, they must be considered for designing EAP and MAP composites as well as for providing reliable computations. The third part of the thesis is dedicated to polymeric hydrogels. Here, we develop a minimization-based homogenization framework to determine the response of transient periodic hydrogel systems. We demonstrate the prevailing size effect as a result of a transient microscopic problem, which has been investigated for various microstructures. Exploiting the elements of the proposed framework, we explore the material and structural instabilities in single and two-phase hydrogel systems. Here, we have observed complex experimentally observed and novel 2D pattern transformations such as diamond-plate patterns coupled with and without wrinkling of internal surfaces for perforated microstructures and 3D pattern transformations in thin reinforced hydrogel composites. The results indicate that the obtained patterns can be controlled by tuning the material and geometrical parameters of the composite.Item Open Access Physics-informed neural networks for learning dynamic, distributed and uncertain systems(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) Praditia, Timothy; Nowak, Wolfgang (Prof. Dr.-Ing.)Scientific models play an important role in many technical inventions to facilitate daily human activities. We use them to assist us in simple decision making such as deciding what type of clothing we should wear using the weather forecast model, and also in complex problems such as assessing the environmental impact of industrial wastes. Existing scientific models, however, are imperfect due to our limited understanding of complex physical systems. Due to the rapid growth in computing power in recent years, there has been an increasing interest in applying data-driven modeling to improve upon current models and to fill in the missing scientific knowledge. Traditionally, these data-driven models require a significant amount of observation data, which is often challenging to obtain, especially from a natural system. To address this issue, prior physical knowledge has been included in the model design, resulting in so-called hybrid models. Although the idea of infusing physics with data seems sound, current state-of-the-art models have not found the ideal combination of both aspects, and the application to real-world data has been lacking. To bridge this gap, three research questions are formulated: 1. How can prior physical knowledge be adopted to design a consistent and reliable hybrid model for dynamic systems? 2. How can prior physical and numerical knowledge be adopted to design a consistent and reliable hybrid model for dynamic and spatially distributed systems? 3. How can the hybrid model learn about its own total (predictive) uncertainty in a computationally effective manner, so that it is appropriate for real-world applications or could facilitate scientific hypothesis testing? The overall goal is, with these questions answered, to contribute to more consistent approaches for scientific inquiry through hybrid models. The first contribution of this thesis addresses the first research question by proposing a modeling framework for a dynamic system, in the form of a Thermochemical Energy Storage device. A Nonlinear Autoregressive Network with Exogeneous Input (NARX) model is trained recurrently with multiple time lags to capture the temporal dependency and the long-term dynamics of the system. During training, the model is penalized when it violates established physical laws, such as mass and energy conservation. As a result, the model produces accurate and physically plausible predictions compared to models that are trained without physical regularization. The second research question is addressed by the second contribution of this thesis, by designing a hybrid model that complements the Finite Volume Method (FVM) with the learning ability of Artificial Neural Networks (ANNs). The resulting model enables the learning of unknown closure/constitutive relationships in various advection-diffusion equations. This thesis shows that the proposed model outperforms state-of-the-art deep learning models by several orders of magnitude in accuracy, and it possesses excellent generalization ability. Finally, the third contribution addresses the third research question, by investigating the performance of assorted uncertainty quantification methods on the hybrid model. As a demonstration, laboratory measurement data of a groundwater contaminant transport process is employed to train the model. Since the available training data is extremely scarce and noisy, uncertainty quantification methods are essential to produce a robust and trustworthy model. It is shown that a gradient-based Markov Chain Monte Carlo (MCMC) algorithm, namely the Barker proposal is the most suitable to quantify the uncertainty of the proposed model. Additionally, the hybrid model outperforms a calibrated physical model and provides appropriate predictive uncertainty to sufficiently explain the noisy measurement data. With these contributions, this thesis proposes a robust hybrid modeling framework that is suitable for filling in missing scientific knowledge and lays the groundwork for a wider variety of complex real-world applications. Ultimately, the hope is for this work to inspire future studies that contribute to the continuous and mutual improvements of both scientific knowledge discovery and scientific model robustness.Item Open Access Development of efficient multiscale multiphysics models accounting for reversible flow at various subsurface energy storage sites(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Becker, Beatrix; Helmig, Rainer (Prof. Dr.-Ing.)Energy storage is an essential component of future energy systems with a large share of renewable energy. Apart from pumped hydro storage, large scale energy storage is mainly provided by underground energy storage systems. In this thesis we focus on chemical subsurface storage, i.e., the storage of synthetic hydrogen or synthetic natural gas in porous formations. To improve understanding of the complex and coupled processes in the underground and enable planning and risk assessment of subsurface energy storage, efficient, consistent and adequate numerical models for multiphase flow and transport are required. Simulating underground energy storage requires large domains, including local features such as fault zones and a representation of the transient saline front, and simulation times spanning the whole time of plant operation and beyond. In addition, often a large number of simulation runs need to be conducted to quantify parameter uncertainty, and efficient models are needed for data assimilation as well. Therefore, a reduction of model complexity and thus computing effort is required. Numerous simplified models that require less computational resources have been developed. In this thesis we focus on a group of multiscale models which use vertically integrated equations and implicitly include fine-scale information along the vertical direction that is reconstructed assuming vertical equilibrium (VE). Classical VE models are restricted to situations where vertical equilibrium is valid in the whole domain during most of the simulated time. This may not be the case for underground energy storage, where simulated times may be too short and locally a high degree of accuracy and complexity may be required, e.g., around the area where gas is extracted for the purpose of energy production. The three core chapters of this thesis present solutions to adapt VE models for the simulation of underground energy storage, with increasing complexity.Item Open Access A multi-scale approach for drop/porous-medium interaction(Stuttgart: Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Ackermann, Sina; Helmig, Rainer (Prof. Dr.-Ing.)Item Open Access Stochastic model comparison and refinement strategies for gas migration in the subsurface(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) Banerjee, Ishani; Nowak, Wolfgang (Prof. Dr.-Ing.)Gas migration in the subsurface, a multiphase flow in a porous-medium system, is a problem of environmental concern and is also relevant for subsurface gas storage in the context of the energy transition. It is essential to know and understand the flow paths of these gases in the subsurface for efficient monitoring, remediation or storage operations. On the one hand, laboratory gas-injection experiments help gain insights into the involved processes of these systems. On the other hand, numerical models help test the mechanisms observed and inferred from the experiments and then make useful predictions for real-world engineering applications. Both continuum and stochastic modelling techniques are used to simulate multiphase flow in porous media. In this thesis, I use a stochastic discrete growth model: the macroscopic Invasion Percolation (IP) model. IP models have the advantages of simplicity and computational inexpensiveness over complex continuum models. Local pore-scale changes dominantly affect the flow processes of gas flow in water-saturated porous media. IP models are especially favourable for these multi-scale systems because using continuum models to simulate them can be extremely computationally difficult. Despite offering a computationally inexpensive way to simulate multiphase flow in porous media, only very few studies have compared their IP model results to actual laboratory experimental image data. One reason might be the fact that IP models lack a notion of experimental time but only have an integer counter for simulation steps that imply a time order. The few existing experiments-to-model comparison studies have used perceptual similarity or spatial moments as comparison measures. On the one hand, perceptual comparison between the model and experimental images is tedious and non-objective. On the other hand, comparing spatial moments of the model and experimental images can lead to misleading results because of the loss of information from the data. In this thesis, an objective and quantitative comparison method is developed and tested that overcomes the limitations of these traditional approaches. The first step involves volume-based time-matching between real-time experimental data and IP-model outputs. This is followed by using the (Diffused) Jaccard coefficient to evaluate the quality of the fit. The fit between the images from the models and experiments can be checked across various scales by varying the extent of blurring in the images. Numerical model predictions for sparsely known systems (like the gas flow systems) suffer from high conceptual uncertainties. In literature, numerous versions of IP models, differing in their underlying hypotheses, have been used for simulating gas flow in porous media. Besides, the gas-injection experiments belong to continuous, transitional, or discontinuous gas flow regimes, depending on the gas flow rate and the porous medium's nature. Literature suggests that IP models are well suited for the discontinuous gas flow regime; other flow regimes have not been explored. Using the abovementioned method, in this thesis, four macroscopic IP model versions are compared against data from nine gas-injection experiments in transitional and continuous gas flow regimes. This model inter-comparison helps assess the potential of these models in these unexplored regimes and identify the sources of model conceptual uncertainties. Alternatively, with a focus on parameter uncertainty, Bayesian Model Selection is a standard statistical procedure for systematically and objectively comparing different model hypotheses by computing the Bayesian Model Evidence (BME) against test data. BME is the likelihood of a model producing the observed data, given the prior distribution of its parameters. Computing BME can be challenging: exact analytical solutions require strong assumptions; mathematical approximations (information criteria) are often strongly biased; assumption-free numerical methods (like Monte Carlo) are computationally impossible for large data sets. In this thesis, a BME-computation method is developed to use BME as a ranking criterion for such infeasible scenarios: The \emph{Method of Forced Probabilities} for extensive data sets and Markov-Chain models. In this method, the direction of evaluation is swapped: instead of comparing thousands of model runs on random model realizations with the observed data, the model is forced to reproduce the data in each time step, and the individual probabilities of the model following these exact transitions are recorded. This is a fast, accurate and exact method for calculating BME for IP models which exhibit the Markov chain property and for complete "atomic" data. The analysis results obtained using the methods and tools developed in this thesis help identify the strengths and weaknesses of the investigated IP model concepts. This further aids model development and refinement efforts for predicting gas migration in the subsurface. Also, the gained insights foster improved experimental methods. These tools and methods are not limited to gas flow systems in porous media but can be extended to any system involving raster outputs.Item Open Access Image-based analysis of biological network structures using machine learning and continuum mechanics(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2020) Asgharzadeh, Pouyan; Röhrle, Oliver (Prof., PhD)Item Open Access Behavior of concrete structures subjected to static and dynamic loading after fire exposure(2021) Lacković, Luka; Ožbolt, Joško (Prof. Dr.-Ing. habil.)The resistance of concrete structures exposed to extreme loading conditions such as explosion, impact, industrial accidents, tsunami, earthquake or their combination represents one of the major topics in research today. Such loading conditions are characterized with high loading rates often acting in conjunction with fire exposure. Especially vulnerable are the structures located in the seismically active areas with high level of urbanization and proximity to HAZMAT landfills, which additionally exacerbate fire conflagrations. The behavior of concrete changes significantly when exposed to elevated temperatures resulting in the decrease of its mechanical properties. Reinforced concrete (RC), when exposed to high temperature culminates in a simultaneous thermal behavior of its two constituents, steel and concrete, that should be considered in the analysis. It is also known that the resistance, crack pattern and failure mode in concrete are strongly influenced by the loading rate. The dynamic response of RC structures previously exposed to fire changes significantly when compared to initially undamaged RC structures. The main objective of the present work is to further improve the existing rate sensitive thermo-mechanical model for concrete through the following: (i) the implementation of the experimentally obtained thermal dependence of concrete fracture energy in the thermo-mechanical model, (ii) the calculation of concrete thermally dependent mechanical properties by means of nonlocal (average) temperature and (iii) to perform parametric study on fastening elements and RC frames in order to investigate the interaction between the thermally induced damage and mechanical behavior of structures. The experimental investigations in the present work indicated that the concrete fracture energy has a declining tendency with the temperature increase, measured on small and mid-sized concrete beams. This is implemented in the thermo-mechanical model and it is indicated that the decrease of fracture energy has a relatively mild influence on reaction values in terms of loading rate. However, its effect on the fracture patterns and reaction-time histories can be considered as more significant. The influence of the nonlocal temperature is validated against the experimental results carried out on RC frames which had been thermally pre-damaged and subsequently loaded with impact. Currently there are almost no models that can realistically predict the structural behavior at this level of complexity. Furthermore, a parametric study is carried out to show the influence of preloading of single-headed stud anchor and anchor group with two and four studs, on the residual concrete edge failure capacity after fire exposure. The anchors are exposed to fire and loaded in shear, perpendicular to the free edge of the concrete member up to failure, in both hot and cold state (after cooling). The influence of different geometry configurations and initial conditions such as the edge distance, embedment depth, anchor diameter and duration of fire on the load-bearing behavior of anchors is investigated. It is demonstrated that the preloading has a strong negative influence on the residual load-bearing capacity of the concrete. Finally, the numerical parametric study is performed to investigate the influence of fire duration and the loading rate on the resistance of RC frames. The response of the RC structures strongly depends on whether it was loaded in hot or residual (cold) state, i.e. after being naturally cooled down to ambient temperature. Furthermore, an extensive numerical investigation on the influence of post-earthquake fire on the residual capacity of RC frames with and without ductile detailing is conducted. The numerical investigation encompassed the validation of the thermo-mechanical model in terms of temperature distributions, thermal deflections and load-bearing capacity against the test data and subsequent parametric analysis with different levels of fire exposure ranging from 15 to 120 min.