arXiv subjects

Gabriele Chiogna

Publications and source records attributed to Gabriele Chiogna.

6 recordsLinked to original sources

arXiv2026

Numerical simulation and analysis of mixing enhancement due to chaotic advection using an adaptive approach for approximating the dilution index

Lagrangian particle-tracking methods are particularly suitable to study solute transport in velocity fields displaying chaotic advection. They can accurately resolve stretching and folding processes, the increase in the solute-solvent interface available for diffusion as well as Kolmogorov-Arnold-Moser (KAM) islands, non-mixing regions that limit the chaotic area in the domain and, thereby, the mixing enhancement. However, they also display limitations due to the finite number of discrete particles, particularly if we are interested in the quantification of mixing processes, which require an accurate description of the particle density or concentration gradients. In this work, we use the dilution index to quantify the temporal increase in mixing of a solute within its solvent. We introduce a new approach to select a suitable grid size for the approximation of the density function, motivated by the theory of representative elementary volumes. It preserves the central feature of the dilution index, which is monotonically increasing in time, highlighting the importance of a suitable choice for the grid size in the dilution index approximation. We use this approach to demonstrate the mixing enhancement for two chaotic injection-extraction systems that exhibit chaotic structures: a source-sink dipole and a rotated potential mixing. Using our new approach, we assess the choice of design parameters of the injection-extraction systems to effectively engineer chaotic mixing. We demonstrate the important role of diffusion in filling the KAM islands and reaching complete mixing and, consequently, the importance of avoiding numerical diffusion, which often affects Eulerian methods applied on the advection-diffusion equation.

physics.flu-dyn↗

arXiv2026

Time-varying sensitivity analysis for mixing in chaotic flows: a comparison study

Engineered injection and extraction systems that create chaotic advection are promising procedures for enhancing mixing between two species. Mixing efficiencies vary considerably, so carefully selecting the design parameters, like pumping rates, well locations, or operation times, is crucial. While numerous studies investigate the conditions required to achieve chaotic flow, sensitivity analyses addressing its impact on mixing have rarely been performed. However, selecting a suitable sensitivity analysis method depends on the underlying system and is often restricted by the computational cost, especially when considering complex, high-dimensional models. Moreover, the most appropriate metric to quantify mixing (e.g., plume area, peak concentration) can also be system-specific. We perform a time-varying sensitivity analysis on the mixing enhancement of two chaotic flow fields with different complexities. The rotated potential mixing (RPM) flow is parametrized using two or four hyperparameters, while the quadrupole flow utilizes 16 hyperparameters. We compare three global sensitivity analysis methods: Sobol indices, Morris scores, and a modification of the activity scores. We evaluate the temporal evolution of the sensitivity of the design parameters, compare the performance of the three methods, and highlight their potential in analyzing parameter interactions. The analysis of the RPM flow shows comparable sensitivities for all methods. Additionally, our numerical experiments show that Morris is the cheapest method, needing at most four times fewer model evaluations than Sobol to reach convergence. This motivates us to only use the computationally cheaper but as reliable Morris and activity scores on the 16-dimensional model, yielding again consistent results.

nlin.CD↗

arXiv2021

Impacts of peak-flow events on hyporheic denitrification potential

Subsurface flows, particularly hyporheic exchange fluxes, driven by streambed topography, permeability, channel gradient and dynamic flow conditions provide prominent ecological services such as nitrate removal from streams and aquifers. Stream flow dynamics cause strongly nonlinear and often episodic contributions of nutrient concentrations in river-aquifer systems. Using a fully coupled transient flow and reactive transport model, we investigated the denitrification potential of hyporheic zones during peak-flow events. The effects of streambed permeability, channel gradient and bedform amplitude on the spatio-temporal distribution of nitrate and dissolved organic carbon in streambeds and the associated denitrification potential were explored. Distinct peak-flow events with different intensity, duration and hydrograph shape were selected to represent a wide range of peak-flow scenarios. Our results indicated that the specific hydrodynamic characteristics of individual flow events largely determine the average positive or negative nitrate removal capacity of hyporheic zones, however the magnitude of this capacity is controlled by geomorphological settings (i.e. channel slope, streambed permeability and bedform amplitude). Specifically, events with longer duration and higher intensity were shown to promote higher nitrate removal efficiency with higher magnitude of removal efficiency in the scenarios with higher slope and permeability values. These results are essential for better assessment of the subsurface nitrate removal capacity under the influence of flow dynamics and particularly peak-flow events in order to provide tailored solutions for effective restoration of interconnected river-aquifer systems.

physics.geo-ph↗

arXiv2021

Alpine Permafrost Modeling: On the influence of topography driven lateral fluxes

Alpine permafrost environments are highly vulnerable and sensitive to changes in regional and global climate trends. Thawing and degradation of permafrost has numerous adverse environmental, economic, and societal impacts. Mathematical modeling and numerical simulations provide powerful tools for predicting the degree of degradation and evolution of subsurface permafrost as a result of global warming. A particularly significant characteristic of alpine environments is the high variability in their topography and geomorphology which drives large lateral thermal and fluid fluxes. Additionally, harsh winds, extreme weather conditions, and various degrees of saturation have to be considered. The combination of large lateral fluxes and unsaturated ground makes alpine systems markedly different from Arctic permafrost environments and general geotechnical ground freezing applications, and therefore, alpine permafrost demands its own specialized modeling approaches. In this research work, we present a multi-physics permafrost model tailored to alpine regions. In particular, we resolve the ice-water phase transitions, unsaturated conditions, and capillary actions, and account for the impact of the evolving pore volume on fluid-matrix interactions. Moreover, the approach is multi-dimensional, and therefore, inherently resolves fluxes along topographic gradients. Through numerical cases studies based on the elevation profiles of the two prominent peaks of the Zugspitze (DE) and the Matterhorn (CH), we show the strong influence of topography driven thermal and fluid fluxes on active layer dynamics and the distribution of permafrost.

physics.ao-ph↗

arXiv2019

Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces

In this article, we perform a parameter study for a recently developed karst hydrological model. The study consists of a high-dimensional Bayesian inverse problem and a global sensitivity analysis. For the first time in karst hydrology, we use the active subspace method to find directions in the parameter space that dominate the Bayesian update from the prior to the posterior distribution in order to effectively reduce the dimension of the problem and for computational efficiency. Additionally, the calculated active subspace can be exploited to construct sensitivity metrics on each of the individual parameters and be used to construct a natural model surrogate. The model consists of 21 parameters to reproduce the hydrological behavior of spring discharge in a karst aquifer located in the Kerschbaum spring recharge area at Waidhofen a.d. Ybbs in Austria. The experimental spatial and time series data for the inference process were collected by the water works in Waidhofen. We show that this case study has implicit low-dimensionality, and we run an adjusted Markov chain Monte Carlo algorithm in a low-dimensional subspace to construct samples of the posterior distribution. The results are visualized and verified by plots of the posterior's push-forward distribution displaying the uncertainty in predicting discharge values due to the experimental noise in the data. Finally, a discussion provides hydrological interpretation of these results for the Kerschbaum area.

stat.CO↗

arXiv2019

On the relation between parameters and discharge data for a lumped karst aquifer model

Hydrological models of karst aquifers are often semi-distributed, and physical processes such as infiltration and spring discharge generation are described in a lumped way. Several works have previously addressed the problems associated with the calibration of such models, highlighting in particular the issue of model parameter estimation and model equifinality. In this work, we investigate the problem of model calibration using the active subspace (AS) method, a novel tool for model parameter dimension reduction. We apply the method to a newly proposed hydrological model for karst aquifers, LuKARS, to investigate if the AS framework identifies catchment-specific characteristics or if the results only depend on the chosen model structure. Therefore, we consider four different case studies, three synthetic and one real case (Kerschbaum springshed in Waidhofen a.d. Ybbs, Austria), with varying hydrotope distributions and properties. We find that both the hydrotope area coverage and the catchment characteristics have major impacts on parameter sensitivities. While model parameters are similarly informed in scenarios with less varying catchment characteristics, we find significant differences in parameter sensitivities when the applied hydrotopes were different from each other. Our results show that the AS method can be used to investigate the relation between the model structure, the area of a hydrotope, the physical properties of a catchment and the discharge data. Finally, we successfully effectively reduce the parameter dimensions of the LuKARS model for the Kerschbaum case study using the AS method. The model with reduced parameter dimensions is able to reproduce the observed impacts of land use changes in the Kerschbaum springshed, highlighting the robustness of the hydrotope-based modeling approach of LuKARS and its applicability for land use change impact studies in karstic systems.

physics.geo-ph↗