02 Fakultät Bau- und Umweltingenieurwissenschaften

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Institute: search.filters.UBSInstitut.Institut für Wasser- und UmweltsystemmodellierungSubject: search.filters.subject.600

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    Multi-objective automatic calibration of hydrodynamic models - development of the concept and an application in the Mekong Delta
    (2011) Nguyen, Viet-Dung; Bárdossy, András (Prof. Dr. rer.nat. Dr.-Ing. habil.)
    Automatic and multi-objective calibration of hydrodynamic models is still underdeveloped, in particular, in comparison with other fields such as hydrological modeling. This is for several reasons: lack of appropriate data, the high degree of computational time demanded, and a suitable framework. These aspects are aggravated in large-scale applications. There are recent developments, however, that improve both the data and the computing constraints. Remote sensing, especially radar-based techniques, provide highly valuable information on flood extents, and in case high precision Digital Elevation Models (DEMs) are present, also on spatially distributed inundation depths. With regards to computation, the use of parallelization techniques brings significant performance gains. In the presented study, we build on these developments by calibrating a large-scale one-dimensional hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: We combine in-situ data from a network of river gauging stations, i.e. data with high-temporal but low-spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low-temporal but high-spatial resolution, in a multi-objective automatic calibration process. It is shown that this kind of calibration of hydrodynamic models is possible, even in an area as large-scale and complex as the Mekong Delta. Furthermore, the calibration process reveals deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would be difficult to detect by a standard manual calibration procedure. In the last part of the dissertation the established hydrodynamic model is combined with flood frequency analysis in order to assess the flood hazard in the Mekong Delta. It is now common to state that climate change can lead to a change in flood hazard. Starting from this assumption, this study develops a novel approach for flood hazard mapping in the Mekong Delta. Typically, flood frequency analysis assumes stationarity and is limited to extreme value statistics of flood peaks. Both, the stationarity assumption and the limitation to univariate frequency analysis remain doubtful in the case of the Mekong Delta, because of changes in hydrologic variability and because of the large relevance of the flood volume for the impact of flooding. Thus, besides the use of the traditional approach for flood frequency analysis, this study takes non-stationarity and bivariate behavior into account. Copula-based bivariate analysis is used to model the dependence and to generate pairs of maximum discharge and volume, by coupling their marginal distributions to gain a bivariate distribution. In addition, based on cluster analysis, groups of characteristic hydrographs are identified and synthetic flood hydrographs are generated. These hydrographs are the input for the calibrated large-scale hydrodynamic model of the Mekong Delta, resulting in flood hazard maps for the whole Mekong Delta. To account for uncertainty within the hazard assessment, a Monte Carlo framework is applied yielding probabilistic hazard maps.
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    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.
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    Improving thermochemical energy storage dynamics forecast with physics-inspired neural network architecture
    (2020) Praditia, Timothy; Walser, Thilo; Oladyshkin, Sergey; Nowak, Wolfgang
    Thermochemical Energy Storage (TCES), specifically the calcium oxide (CaO)/calcium hydroxide (Ca(OH)2) system is a promising energy storage technology with relatively high energy density and low cost. However, the existing models available to predict the system's internal states are computationally expensive. An accurate and real-time capable model is therefore still required to improve its operational control. In this work, we implement a Physics-Informed Neural Network (PINN) to predict the dynamics of the TCES internal state. Our proposed framework addresses three physical aspects to build the PINN: (1) we choose a Nonlinear Autoregressive Network with Exogeneous Inputs (NARX) with deeper recurrence to address the nonlinear latency; (2) we train the network in closed-loop to capture the long-term dynamics; and (3) we incorporate physical regularisation during its training, calculated based on discretized mole and energy balance equations. To train the network, we perform numerical simulations on an ensemble of system parameters to obtain synthetic data. Even though the suggested approach provides results with the error of 3.96 x 10^(-4) which is in the same range as the result without physical regularisation, it is superior compared to conventional Artificial Neural Network (ANN) strategies because it ensures physical plausibility of the predictions, even in a highly dynamic and nonlinear problem. Consequently, the suggested PINN can be further developed for more complicated analysis of the TCES system.