Universität Stuttgart
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Item Open Access Large-scale high head pico hydropower potential assessment(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Schröder, Hans Christoph; Wieprecht, Silke (Prof. Dr.-Ing.)Due to a lack of site-related information, Pico hydropower (PHP) has hardly been a projectable resource so far. This is particularly true for large area PHP potential information that could open a perspective to increase the size of development projects by aggregating individual PHP installations. The present work is extending the capabilities of GIS based hydropower potential assessment into the PHP domain through a GIS based PHP potential assessment procedure that facilitates the discrimination of areas without high head PHP potential against areas with PHP potential and against areas with so called “favorable PHP potential”. The basic unit of the spatial output is determined by the underlying PHP potential definition of this work: a standardized PHP installation and the required hydraulic source, together called standard unit, are located on an area of one square kilometer. The gradation of the output is a consequence of the verification techniques. Several large area PHP potential field assessment methods, based on contemplative analysis techniques, are developed in this work. Field assessments were conducted in Yunnan Province/China, Costa Rica, Ecuador and Sri Lanka. The aim for all field assessments is to get a comprehensive view on the PHP potential distribution of the entire country/province. Application of the GIS based PHP potential assessment procedure is aimed at the global tropical and subtropical regions.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 Wasserinfiltration in die ungesättigte Zone eines makroporösen Hanges und deren Einfluss auf die Hangstabilität(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2016) Germer, Kai; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)Hangrutschungen stellen in besiedelten Regionen eine große Gefahr dar, weil nicht selten direkt bewohnte Bereiche betroffen sind. Aber auch die Rutschungsauswirkungen auf die Infrastruktur wie Verkehrswege und Versorgungseinrichtungen können der Gesellschaft Schaden zufügen. Zum einen ergibt sich im Zusammenhang mit Hangrutschungen und allgemein Massenbewegungen das Betätigungsfeld der direkten Stabilisierung und Verhinderung von Rutschungen durch beispielsweise Tiefdrainagen und aufwendige ingenieursbauliche Maßnahmen. Zum anderen, und das ist Gegenstand dieser Arbeit, ergibt sich das Tätigkeitsfeld der grundlagenorientierten Forschung, um die Prozesse, die zu Hangrutschungen führen, besser verstehen zu können. Mit dem Heumöser Hang in Österreich (Vorarlberg), einem sich sehr langsam bewegenden Großhang (Kriechhang), liegt ein Untersuchungsobjekt vor, an dem vielfältige hydrologische Prozesse stattfinden, die schon über mehrere Jahre hinweg untersucht wurden. Die Untersuchungen resultierten in der Hypothese, dass sich am Heumöser Hang entwickelnder Makroporenfluss zu schnellen hydraulischen Veränderungen im Innern des Hangkörpers führe. Die hydraulischen Veränderungen zeigen sich insbesondere in starken Porenwasserdruckanstiegen (unter anderem in einem gespannten Grundwasserleiter), die teilweise in der Tiefe des Hanges zu Auftriebskräften führen, die den Hang destabilisieren, so dass dieser sich schubweise insgesamt etwa ein bis zwei Dezimeter im Jahr talwärts bewegt. Zum Erarbeiten des Prozessverständnisses bezüglich des Zusammenhanges zwischen Infiltration und Hangstabilität wurden große Bodenproben vom Hang im Labor untersucht und Experimente an zwei technischen Modellen durchgeführt. Mit der vorgestellten Vorgehensweise und der Separierung der Untersuchungen und Experimente konnten für den Heumöser Hang relevante hydrologische und mechanische Teilaspekte erarbeitet werden, die verknüpft die Hangbewegungshypothese in weiten Teilen bestätigen können. Insbesondere bestätigten die Messungen an den originären Bodenproben vom Heumöser Hang, dass der Makroporenfluss so dominant sein kann, dass potentiell schneller Porenwasserdruckanstieg in der Tiefe erzeugt werden kann. Dennoch kann ein Makroporenfluss generell insbesondere bei trockenen Matrixbedingungen vermindert werden. Die Verminderung des Makroporenflusses wurde anhand von Experimenten mit Sand gezeigt. Der Prozess des Wassertransfers von Makropore zu umgebender Matrix ist im Sand sehr deutlich zu sehen. Darüber hinaus konnten bei Bodensäulen- und Bodenprobenexperimenten viele methodische Herangehensweisen getestet und verglichen werden. Weil im Vorfeld der Untersuchungen schon abgeschätzt wurde, dass nur mit größeren Proben eine für den Standort bessere Repräsentativität der Ergebnisse erhalten werden kann, wurde besonders viel Wert auf die Methodik der Großprobennahme gelegt. So ist in der vorgestellten Arbeit ein neuartiger Ansatz zur Großprobennahme entwickelt worden, bei dem die Proben ungestört frei gelegt wurden und mit Haushaltsfolie, Montageschaum und Außenmodulen eingehüllt wurden. Nur die Großprobennahme garantierte ein Mindestmaß an Erfassung von Heterogenitäten und Bodenstrukturen im Dezimeterbereich, wie z.B. Makroporen und Risse. Auch die laboratorischen Versuchsaufbauten zur Anwendung der Multi-Step-Outflow- und Evaporationsmethode an den Großproben wurden einmalig größenangepasst entwickelt.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.)