Universität Stuttgart
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Item Open Access Porosity and permeability alterations in processes of biomineralization in porous media - microfluidic investigations and their interpretation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Weinhardt, Felix; Class, Holger (apl. Prof. Dr.-Ing)Motivation: Biomineralization refers to microbially induced processes resulting in mineral formations. In addition to complex biomineral structures frequently formed by marine organisms, like corals or mussels, microbial activities may also indirectly induce mineralization. A famous example is the formation of stromatolites, which result from biofilm activities that locally alter the chemical and physical properties of the environment in favor of carbonate precipitation. Recently, biomineralization gained attention as an engineering application. Especially with the background of global warming and the objective to reduce CO2 emissions, biomineralization offers an innovative and sustainable alternative to the usage of conventional Portland cement, whose production currently contributes significantly to global CO2 emissions. The most widely used method of biomineralization in engineering applications, is ureolytic calcium carbonate precipitation, which relies on the hydrolysis of urea and the subsequent precipitation of calcium carbonate. The hydrolysis of urea at moderate temperatures is relatively slow and therefore needs to be catalyzed by the enzyme urease to be practical for applications. Urease can be extracted from plants, for example from ground jack beans, and the process is consequently referred to as enzyme-induced calcium carbonate precipitation (ECIP). Another method is microbially induced calcium carbonate precipitation (MICP), which uses ureolytic bacteria that produce the enzyme in situ. EICP and MICP applications allow for producing various construction materials, stabilizing soils, or creating hydraulic barriers in the subsurface. The latter can be used, for example, to remediate leakages at the top layer of gas storage reservoirs, or to contain contaminant plumes in aquifers. Especially when remediating leakages in the subsurface, the most crucial parameter to be controlled is its intrinsic permeability. A valuable tool for predicting and planning field applications is the use of numerical simulation at the scale of representative elementary volumes (REV). For that, the considered domain is subdivided into several REV’s, which do not resolve the pore space in detail, but represent it by averaged parameters, such as the porosity and permeability. The porosity describes the ratio of the pore space to the considered bulk volume, and the permeability quantifies the ease of fluid flow through a porous medium. A change in porosity generally also affects permeability. Therefore, for REV-scale simulations, constitutive relationships are utilized to describe permeability as a function of porosity. There are several porosity-permeability relationships in the literature, such as the Kozeny-Carman relationship, Verma-Pruess, or simple power-law relationships. These constitutive relationships can describe individual states but usually do not include the underlying processes. Different boundary conditions during biomineralization may influence the course of porosity-permeability relationships. However, these relationships have not yet been adequately addressed. Pore-scale simulations are, in principle, very well suited to investigate pore space changes and their effects on permeability systematically. However, these simulations also rely on simplifications and assumptions. Therefore, it is essential to conduct experimental studies to investigate the complex processes during calcium carbonate precipitation in detail at the pore scale. Recent studies have shown that microfluidic methods are particularly suitable for this purpose. However, previous microfluidic studies have not explicitly addressed the impact of biomineralization on hydraulic effects. Therefore, this work aims to identify relevant phenomena at the pore scale to conclude on the REV-scale parameters, porosity and permeability, and their relationship. Contributions: This work comprises three publications. First, a suitable microfluidic setup and workflow were developed in Weinhardt et al. [2021a] to study pore space changes and the associated hydraulic effects reliably. This paper illustrated the benefits and insights of combining optical microscopy and micro X-ray computed tomography (micro XRCT) with hydraulic measurements in microfluidic chips. The elaborated workflow allowed for quantitative analysis of the evolution of calcium carbonate precipitates in terms of their size, shape, and spatial distribution. At the same time, their influence on differential pressure could be observed as a measure of flow resistance. Consequently, porosity and permeability changes could be determined. Along with this paper, we published two data sets [Weinhardt et al., 2021b, Vahid Dastjerdi et al., 2021] and set the basis for two other publications. In the second publication [von Wolff et al., 2021], the simulation results of a pore-scale numerical model, developed by Lars von Wolff, were compared to the experimental data of the first paper [Weinhardt et al., 2021b]. We observed a good agreement between the experimental data and the model results. The numerical studies complemented the experimental observations in allowing for accurate analysis of crystal growth as a function of local velocity profiles. In particular, we observed that crystal aggregates tend to grow toward the upstream side, where the supply of reaction products is higher than on the downstream side. Crystal growth during biomineralization under continuous inflow is thus strongly dependent on the locally varying velocities in a porous medium. In the third publication [Weinhardt et al., 2022a], we conducted further microfluidic experiments based on the experimental setup and workflow of the first contribution and published another data set [Weinhardt et al., 2022b]. We used microfluidic cells with a different, more realistic pore structure and investigated the influence of different injection strategies. We found that the development of preferential flow paths during EICP application may depend on the given boundary conditions. Constant inflow rates can lead to the development of preferential flow paths and keep them open. Gradually reduced inflow rates can mitigate this effect. In addition, we concluded that the coexistence of multiple calcium carbonate polymorphs and their transformations could influence the temporal evolution of porosity-permeability relationships.Item Open Access Investigations on functional relationships between cohesive sediment erosion and sediment characteristics(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Beckers, Felix; Wieprecht, Silke (Prof. Dr.-Ing.)Item Open Access Bayesian inversion and model selection of heterogeneities in geostatistical subsurface modeling(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Reuschen, Sebastian; Nowak, Wolfgang (Prof. Dr.-Ing.)Item Open Access Advanced experimental methods for investigating flow-biofilm-sediment interactions(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Koca, Kaan; Wieprecht, Silke (Prof. Dr.-Ing.)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 Spatial aspects of hydrological extremes : description and simulation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2024) Anwar, Faizan; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)This thesis deals with the development of measures that can identify arbitrary multivariate dependence in space-time. A few new multivariate time series generators are also introduced. The aim was to use these measures and methods to simulate large scale heavy precipitation and river discharge more accurately.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 Integrating transient flow conditions into groundwater well protection(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2020) Rodríguez Pretelín, Abelardo; Nowak, Wolfgang (Prof. Dr.-Ing.)Item Open Access Coupled free-flow-porous media flow processes including drop formation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) Veyskarami, Maziar; Helmig, Rainer (Prof. Dr.-Ing.)Behavior of a coupled free-flow-porous medium system is determined by the interface between the two domains. Formation of droplets at the interface governs transport processes in the whole system by enormously affecting the exchange of mass, momentum, and energy between the free flow and the porous medium. A droplet that forms at the interface might grow or shrink due to the flow from the porous medium and evaporation from its surface into the free flow. It also might be detached from the interface by the free flow. An example of such phenomena in nature is formation of sweat droplets on the skin by perspiration and the resulted cooling effect through their evaporation into the surrounding air. Water management in fuel cells, cooling systems, and inkjet printing are just a few technical applications in which droplet formation at the interface between a free flow and a porous medium appears. In this work, we developed a novel model to describe the formation, growth and detachment as well as evaporation of droplets at the interface between a coupled free-flow-porous medium system. Pore-network modeling is used as a tool to capture pore-scale phenomena occurring in porous media. New coupling concepts between the free flow and the porous medium are developed, which include storing mass, momentum and energy in the droplet. The formation and growth of a droplet is described and a new approach is developed to include the impact of the growing droplet on the free-flow field. Description of the forces acting in the system is given and accordingly the droplet detachment is predicted. A clear description of the droplet evaporation is provided and the impact of free-flow and porous medium properties on the droplet evaporation have been analyzed.Item Open Access Spatial extent of precipitation extremes in hydrology(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) El Hachem, Abbas; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)Precipitation extremes are a space-time phenomenon that influences many engineering design decisions. The occurrence of precipitation extremes is, however, rare and with values that can deviate notably from ”normal” observations. For design purposes, an estimate of areal rainfall depth for a corresponding return period is needed. Traditionally, point rainfall extreme value statistics are transferred to areal statistics using the concept of area reduction factors. These are, in general, based on simple assumptions without considering the effects of climate change. Area Depth Duration Frequency (ADDF) curves are a mathematical function relating the area of a location to the depth and frequency of a rainfall event for a certain temporal duration and return period. The calculation of the ADDF curves is, however, not straightforward, as, in contrast to point precipitation, areal precipitation is not measured but must be estimated. This work considers precipitation as a spatial phenomenon, without purely point statistics, and aims to assess areal precipitation extremes for the present and future time periods along their expected change with climate change.