Universität Stuttgart

Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1

Browse

Filters

2019 - 201912020 - 202514

Settings

Institute: search.filters.UBSInstitut.Stuttgarter Zentrum für Simulationswissenschaften (SC SimTech)Subject: search.filters.subject.550

Search Results

Now showing 1 - 10 of 15
  • Thumbnail Image
    ItemOpen Access
  • Thumbnail Image
    ItemOpen Access
  • Thumbnail Image
    ItemOpen Access
    Pressure management via brine extraction in geological CO2 storage : adaptive optimization strategies under poorly characterized reservoir conditions
    (2019) González-Nicolás, Ana; Cihan, Abdullah; Petrusak, Robin; Zhou, Quanlin; Trautz, Robert; Godec, Michael; Birkholzer, Jens T.
    Industrial-scale injection of CO2 into the subsurface increases the fluid pressure in the reservoir, which if not properly controlled can potentially lead to geomechanical damage (i.e., fracturing of the caprock or reactivation of faults) and subsequent CO2 leakage. Brine extraction is one approach for managing formation pressure, effective stress, and plume movement in response to CO2 injection. The management of the extracted brine can be expensive (i.e., due to transportation, treatment, disposal, or re-injection), with added cost to the carbon capture and sequestration (CCS); thus, minimizing the volume of extraction brine is of great importance to ensure that the economics of CCS are favorable. The main objective of this study is to demonstrate the use of adaptive optimization methods in the planning of brine extraction and to investigate how the quality of initial site characterization data and the use of newly acquired monitoring data (e.g. pressure at observation wells) impact the optimization performance. We apply an adaptive management approach that integrates monitoring, calibration, and optimization of brine extraction rates to achieve pre-defined pressure constraints. Our results show that reservoir pressure management can be extremely benefited by early and high frequency pressure monitoring during early injection times, especially for poor initial reservoir characterization. Low frequencies of model calibration and optimization with monitoring data may lead to optimization problems, because either pressure buildup constraints are violated or excessively high extraction rates are proposed. The adaptive pressure management approach may constitute an effective tool to manage pressure buildup under uncertain reservoir conditions by minimizing the volumes of extracted brine while controlling pressure buildup.
  • Thumbnail Image
    ItemOpen Access
    Multivariate motion patterns and applications to rainfall radar data
    (2023) Fischer, Svenja; Oesting, Marco; Schnurr, Alexander
    The classification of movement in space is one of the key tasks in environmental science. Various geospatial data such as rainfall or other weather data, data on animal movement or landslide data require a quantitative analysis of the probable movement in space to obtain information on potential risks, ecological developments or changes in future. Usually, machine-learning tools are applied for this task, as these approaches are able to classify large amounts of data. Yet, machine-learning approaches also have some drawbacks, e.g. the often required large training sets and the fact that the algorithms are often hard to interpret. We propose a classification approach for spatial data based on ordinal patterns. Ordinal patterns have the advantage that they are easily applicable, even to small data sets, are robust in the presence of certain changes in the time series and deliver interpretative results. They therefore do not only offer an alternative to machine-learning in the case of small data sets but might also be used in pre-processing for a meaningful feature selection. In this work, we introduce the basic concept of multivariate ordinal patterns and the corresponding limit theorem. A simulation study based on bootstrap demonstrates the validity of the results. The approach is then applied to two real-life data sets, namely rainfall radar data and the movement of a leopard. Both applications emphasize the meaningfulness of the approach. Clearly, certain patterns related to the atmosphere and environment occur significantly often, indicating a strong dependence of the movement on the environment.
  • Thumbnail Image
    ItemOpen Access
    Diagnosing similarities in probabilistic multi-model ensembles : an application to soil-plant-growth-modeling
    (2022) Schäfer Rodrigues Silva, Aline; Weber, Tobias K. D.; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo
    There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.
  • Thumbnail Image
    ItemOpen Access
    Implications of modeling seasonal differences in the extremal dependence of rainfall maxima
    (2022) Jurado, Oscar E.; Oesting, Marco; Rust, Henning W.
    For modeling extreme rainfall, the widely used Brown-Resnick max-stable model extends the concept of the variogram to suit block maxima, allowing the explicit modeling of the extremal dependence shown by the spatial data. This extremal dependence stems from the geometrical characteristics of the observed rainfall, which is associated with different meteorological processes and is usually considered to be constant when designing the model for a study. However, depending on the region, this dependence can change throughout the year, as the prevailing meteorological conditions that drive the rainfall generation process change with the season. Therefore, this study analyzes the impact of the seasonal change in extremal dependence for the modeling of annual block maxima in the Berlin-Brandenburg region. For this study, two seasons were considered as proxies for different dominant meteorological conditions: summer for convective rainfall and winter for frontal/stratiform rainfall. Using maxima from both seasons, we compared the skill of a linear model with spatial covariates (that assumed spatial independence) with the skill of a Brown-Resnick max-stable model. This comparison showed a considerable difference between seasons, with the isotropic Brown-Resnick model showing considerable loss of skill for the winter maxima. We conclude that the assumptions commonly made when using the Brown-Resnick model are appropriate for modeling summer (i.e., convective) events, but further work should be done for modeling other types of precipitation regimes.
  • Thumbnail Image
    ItemOpen Access
    Strategies for simplifying reactive transport models : a Bayesian model comparison
    (2020) Schäfer Rodrigues Silva, Aline; Guthke, Anneli; Höge, Marvin; Cirpka, Olaf A.; Nowak, Wolfgang
    For simulating reactive transport on aquifer scale, various modeling approaches have been proposed. They vary considerably in their computational demands and in the amount of data needed for their calibration. Typically, the more complex a model is, the more data are required to sufficiently constrain its parameters. In this study, we assess a set of five models that simulate aerobic respiration and denitrification in a heterogeneous aquifer at quasi steady state. In a probabilistic framework, we test whether simplified approaches can be used as alternatives to the most detailed model. The simplifications are achieved by neglecting processes such as dispersion or biomass dynamics, or by replacing spatial discretization with travel‐time‐based coordinates. We use the model justifiability analysis proposed by Schöniger, Illman, et al. (2015, https://doi.org/10.1016/j.jhydrol.2015.07.047) to determine how similar the simplified models are to the reference model. This analysis rests on the principles of Bayesian model selection and performs a tradeoff between goodness‐of‐fit to reference data and model complexity, which is important for the reliability of predictions. Results show that, in principle, the simplified models are able to reproduce the predictions of the reference model in the considered scenario. Yet, it became evident that it can be challenging to define appropriate ranges for effective parameters of simplified models. This issue can lead to overly wide predictive distributions, which counteract the apparent simplicity of the models. We found that performing the justifiability analysis on the case of model simplification is an objective and comprehensive approach to assess the suitability of candidate models with different levels of detail.
  • Thumbnail Image
    ItemOpen Access
    Diagnosing hydro-mechanical effects in subsurface fluid flow through fractures
    (2023) Schmidt, Patrick; Steeb, Holger; Renner, Jörg
    Hydro-mechanically induced transient changes in fracture volume elude an analysis of pressure and flow rate transients by conventional diffusion-based models. We used a previously developed fully coupled, inherently non-linear numerical simulation model to demonstrate that harmonic hydraulic excitation of fractures leads to systematic overtones in the response spectrum that can thus be used as a diagnostic criterion for hydro-mechanical interaction. The examination of response spectra, obtained from harmonic testing at four different field sites, for the occurrence of overtones confirmed their potential for the hydro-mechanical characterization of tested reservoirs. A non-dimensional analysis identified relative aperture change as the critical system parameter.
  • Thumbnail Image
    ItemOpen Access
    Bayesian calibration and validation of a large‐scale and time‐demanding sediment transport model
    (2020) Beckers, Felix; Heredia, Andrés; Noack, Markus; Nowak, Wolfgang; Wieprecht, Silke; Oladyshkin, Sergey
    This study suggests a stochastic Bayesian approach for calibrating and validating morphodynamic sediment transport models and for quantifying parametric uncertainties in order to alleviate limitations of conventional (manual, deterministic) calibration procedures. The applicability of our method is shown for a large‐scale (11.0 km) and time‐demanding (9.14 hr for the period 2002-2013) 2‐D morphodynamic sediment transport model of the Lower River Salzach and for three most sensitive input parameters (critical Shields parameter, grain roughness, and grain size distribution). Since Bayesian methods require a significant number of simulation runs, this work proposes to construct a surrogate model, here with the arbitrary polynomial chaos technique. The surrogate model is constructed from a limited set of runs (n=20) of the full complex sediment transport model. Then, Monte Carlo‐based techniques for Bayesian calibration are used with the surrogate model (105 realizations in 4 hr). The results demonstrate that following Bayesian principles and iterative Bayesian updating of the surrogate model (10 iterations) enables to identify the most probable ranges of the three calibration parameters. Model verification based on the maximum a posteriori parameter combination indicates that the surrogate model accurately replicates the morphodynamic behavior of the sediment transport model for both calibration (RMSE = 0.31 m) and validation (RMSE = 0.42 m). Furthermore, it is shown that the surrogate model is highly effective in lowering the total computational time for Bayesian calibration, validation, and uncertainty analysis. As a whole, this provides more realistic calibration and validation of morphodynamic sediment transport models with quantified uncertainty in less time compared to conventional calibration procedures.
  • Thumbnail Image
    ItemOpen Access
    Characterization of export regimes in concentration-discharge plots via an advanced time-series model and event-based sampling strategies
    (2021) González-Nicolás, Ana; Schwientek, Marc; Sinsbeck, Michael; Nowak, Wolfgang
    Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the re-gression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an ex-plicit transition parameter that can morph the model between chemostatic behavior and che-modynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concen-trations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regres-sion approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs.