Browsing by Author "Xiao, Sinan"

Now showing 1 - 4 of 4

Open Access
Experimental evaluation and uncertainty quantification for a fractional viscoelastic model of salt concrete
(2022) Hinze, Matthias; Xiao, Sinan; Schmidt, André; Nowak, Wolfgang
This study evaluates and analyzes creep testing results on salt concrete of type M2. The concrete is a candidate material for long-lasting structures for sealing underground radioactive waste repository sites. Predicting operational lifetime and security aspects for these structures requires specific constitutive equations to describe the material behavior. Thus, we analyze whether a fractional viscoelastic constitutive law is capable of representing the long-term creep and relaxation processes for M2 concrete. We conduct a creep test to identify the parameters of the fractional model. Moreover, we use the Bayesian inversion method to evaluate the identifiability of the model parameters and the suitability of the experimental setup to yield a reliable prediction of the concrete behavior. Particularly, this Bayesian analysis allows to incorporate expert knowledge as prior information, to account for limited experimental precision and finally to rigorously quantify the post-calibration uncertainty.
Open Access
Optimal design of experiments to improve the characterisation of atrazine degradation pathways in soil
(2021) Chavez Rodriguez, Luciana; González‐Nicolás, Ana; Ingalls, Brian; Streck, Thilo; Nowak, Wolfgang; Xiao, Sinan; Pagel, Holger
Contamination of soils with pesticides and their metabolites is a global environmental threat. Deciphering the complex process chains involved in pesticide degradation is a prerequisite for finding effective solution strategies. This study applies prospective optimal design (OD) of experiments to identify laboratory sampling strategies that allow model‐based discrimination of atrazine (AT) degradation pathways. We simulated virtual AT degradation experiments with a first‐order model that reflects a simple reaction chain of complete AT degradation. We added a set of Monod‐based model variants that consider more complex AT degradation pathways. Then, we applied an extended constraint‐based parameter search algorithm that produces Monte‐Carlo ensembles of realistic model outputs, in line with published experimental data. Differences between‐model ensembles were quantified with Bayesian model analysis using an energy distance metric. AT degradation pathways following first‐order reaction chains could be clearly distinguished from those predicted with Monod‐based models. As expected, including measurements of specific bacterial guilds improved model discrimination further. However, experimental designs considering measurements of AT metabolites were most informative, highlighting that environmental fate studies should prioritise measuring metabolites for elucidating active AT degradation pathways in soils. Our results suggest that applying model‐based prospective OD will maximise knowledge gains on soil systems from laboratory and field experiments.
Open Access
Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces
(2020) Erdal, Daniel; Xiao, Sinan; Nowak, Wolfgang; Cirpka, Olaf A.
Ensemble-based uncertainty quantification and global sensitivity analysis of environmental models requires generating large ensembles of parameter-sets. This can already be difficult when analyzing moderately complex models based on partial differential equations because many parameter combinations cause an implausible model behavior even though the individual parameters are within plausible ranges. In this work, we apply Gaussian Process Emulators (GPE) as surrogate models in a sampling scheme. In an active-training phase of the surrogate model, we target the behavioral boundary of the parameter space before sampling this behavioral part of the parameter space more evenly by passive sampling. Active learning increases the subsequent sampling efficiency, but its additional costs pay off only for a sufficiently large sample size. We exemplify our idea with a catchment-scale subsurface flow model with uncertain material properties, boundary conditions, and geometric descriptors of the geological structure. We then perform a global-sensitivity analysis of the resulting behavioral dataset using the active-subspace method, which requires approximating the local sensitivities of the target quantity with respect to all parameters at all sampled locations in parameter space. The Gaussian Process Emulator implicitly provides an analytical expression for this gradient, thus improving the accuracy of the active-subspace construction. When applying the GPE-based preselection, 70-90% of the samples were confirmed to be behavioral by running the full model, whereas only 0.5% of the samples were behavioral in standard Monte-Carlo sampling without preselection. The GPE method also provided local sensitivities at minimal additional costs.
Open Access
Towards a community-wide effort for benchmarking in subsurface hydrological inversion : benchmarking cases, high-fidelity reference solutions, procedure, and first comparison
(2024) Xu, Teng; Xiao, Sinan; Reuschen, Sebastian; Wildt, Nils; Hendricks Franssen, Harrie-Jan; Nowak, Wolfgang
Inversion in subsurface hydrology refers to estimating spatial distributions of (typically hydraulic) properties often associated with quantified uncertainty. Many methods are available, each characterized by a set of assumptions, approximations, and numerical implementations. Only a few intercomparison studies have been performed (in the remote past) amongst different approaches (e.g., Zimmerman et al., 1998; Hendricks Franssen et al., 2009). These intercomparisons guarantee broad participation to push forward research efforts of the entire subsurface hydrological inversion community. However, from past studies until now, comparisons have been made among approximate methods without firm reference solutions. Note that the reference solutions are the best possible solutions with the best estimate and posterior standard deviation and so forth. Without reference solutions, one can only compare competing best estimates and their associated uncertainties in an intercomparison sense, and absolute statements on accuracy are unreachable. Our current initiative defines benchmarking scenarios for groundwater model inversion. These are targeted for community-wide use as test cases in intercomparison scenarios. Here, we develop five synthetic, open-source benchmarking scenarios for the inversion of hydraulic conductivity from pressure data. We also provide highly accurate reference solutions produced with massive high-performance computing efforts and with a high-fidelity Markov chain Monte Carlo (MCMC)-type solution algorithm. Our high-end reference solutions are publicly available along with the benchmarking scenarios, the reference algorithm, and the suggested benchmarking metrics. Thus, in comparison studies, one can test against high-fidelity reference solutions rather than discussing different approximations. To demonstrate how to use these benchmarking scenarios, reference solutions, and suggested metrics, we provide a blueprint comparison of a specific ensemble Kalman filter (EnKF) version. We invite the community to use our benchmarking scenarios and reference solutions now and into the far future in a community-wide effort towards clean and conclusive benchmarking. For now, we aim at an article collection in an appropriate journal, where such clean comparison studies can be submitted together with an editorial summary that provides an overview.