Theses

In a thermochemical energy storage system, thermal energy is stored via a reversible chemical reaction. This enables the storage of large amounts of energy over long periods. In view of the worldwide expansion of renewable energies and the heat transition (Wärmewende) in Germany, thermochemical storage systems are therefore being examined for their suitability for many potential applications – from small short-term storages, such as in thermochemical heat pumps, to large seasonal thermal energy storage systems.
The core component of such a storage system is a fixed bed of powder containing the reactive material. Additionally, there are pipes for the supply and removal of gaseous reactants as well as for heat transport during the charging and discharging of the storage system. The performance of these systems depends significantly on the geometry and the temporal evolution of the microstructure (e.g., due to material ablation, sintering or chemical conversion). To understand these processes, high- resolution micro-CT image series (time series) were recorded, enabling non-invasive observation of structural changes. The challenge lies in the automated evaluation of these large amounts of data. The aim of this work is to develop a robust pipeline, for the analysis the image data in order to quantitatively describe the evolutionary changes of the microstructure, the reconstruction of the deformation of the bed as a whole, and finally transfer these findings into macroscopic models. Specifically, the evolution of the microstructure over time is to be characterized, e.g., via pore and grain size distributions, and the deformation field of the bed as a whole is to be reconstructed. For this purpose, various methods of image registration, segmentation and analysis for porous media, including AI based methods are to be used.

The RAINBOW project studies the fate of micropollutants (contaminants of emerging concern) during sponge-city infiltration at a legacy-contaminated urban site. Batch adsorption experiments will produce sorption data for a set of micropollutants showing cationic, anionic and neutral hydrophobic adsorption behaviour, measured as sorption kinetics, single-solute isotherms and competitive mixtures. The objective of this thesis is to use the experimental dataset to build quantitative adsorption models and to investigate whether competition between micropollutants in a mixture changes their retention at field-relevant concentrations in the soil, and whether that behaviour can be written as ordinary and partial differential equations suitable for later transport simulation.

Teams of AI agents can divide scientific work, explore alternative solutions and review each other’s results. However, their effectiveness may depend on how roles are assigned, how the team is organised and how much information agents share. Close collaboration may reduce duplicated effort, while independent exploration may uncover different solutions or errors.
This thesis will investigate these trade-offs for scientific computing tasks involving subsurface flow. The aim is to identify useful team designs for our research field and develop a small, reproducible benchmark for evaluating them.

Research question:
“Under comparable computational budgets, how do team organisation and information sharing affect the correctness, reliability and efficiency of AI agents solving subsurface flow tasks?”
The core comparison will involve a single-agent baseline and two contrasting team designs: a team with predefined roles and a coordinating agent, and a team that explores independently before selectively sharing and reviewing results. Dynamically assigned roles or human checkpoints may be investigated as an extension.

Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants in water. Experiments at VEGAS investigate their photolysis using a xenon excimer lamp emitting at 172 nm in a circulating reactor. The measured concentration time series provide a basis for studying how PFAS concentrations evolve during treatment and how the dynamics depend on experimental conditions.
The aim of this Master's thesis is to develop and evaluate a neural ordinary differential equation (neural ODE) model for these experiments. A neural network will represent the rate of change of selected PFAS concentrations as a function of concentration, time and recorded operating conditions. An ODE solver will integrate the learned dynamics, and training will compare simulated concentrations with measurements at the actual sampling times. The student will assess how well the model reproduces concentration trajectories and predicts measurements withheld from training.
A further research question is whether physically motivated constraints, such as non-negative concentrations, improve predictive reliability when measurements are sparse. The neural ODE will be compared with a simple kinetic model fitted to the same data. Where sufficient experiments are available, the study will examine transfer between initial concentrations or reactor operating conditions, such as circulation flow rate. Changes in measured parent PFAS concentrations will be distinguished from evidence of complete mineralisation.
The work will use a manageable subset of VEGAS experiments, starting with one compound under fixed reactor conditions. Clearly separated training, model-selection and evaluation data will support a reproducible assessment. Depending on progress and data availability, an optional extension may examine predictive uncertainty and identify informative sampling times or operating conditions for further experiments.

Within the scope of this Master’s thesis, the use of gamma-ray and cosmic-ray techniques for field-scale soil moisture estimation will be investigated and compared. Theoretically, the study is based on the principles of cosmic neutron interactions and gamma-ray attenuation in soils, both of which are sensitive to soil water content at the field scale. The aim is to assess the suitability of both methods for soil water content monitoring in terms of accuracy, spatial representativeness, and temporal resolution. The thesis will be focused on the analysis of available data from a long-term monitoring site operated by the Forschungszentrum Jülich GmbH. This site offers excellent conditions for method validation due to its extensive meteorological and soil hydrological monitoring infrastructure. After this initial assessment, the potential of both sensing methods for irrigation system control will be evaluated if time permits.

Underground water pipelines are the backbone of our water sup-ply. Due to aging without timely renewal, hidden leaks continu-ally occur, posing serious challenges to water and energy con-servation. Inspired by echolocating animals like bats, emitting calls and using resulting echoes to locate and identify objects, transient (hydroacoustic) waves have been successfully used for localizing leaks in water pipe networks.
Transient wave-based leak localization is achieved by optimizing leak parame-ters within hydroacoustic numerical models to match measured echos. To reduce the computational burden in this optimization, surrogate models have been de-veloped for rapid predictions of wave signals. However, existing surrogate mod-els are typically trained for a specific fixed pipe network and require substantial re-training whenever they are applied to modified or new pipe networks.
Inspired by the composability of LEGO bricks, this project aims to develop a reconfigurable sur-rogate model for water pipe networks. Instead of retraining for each different pipe network from scratch, the surrogate model will be rapidly reconfigured from "surrogate LEGO blocks" to represent any new topology. The "blocks", representing fundamental pipe segments and junc-tions, are pre-trained by advanced graph neural networks (GNN) techniques. This "build once, reuse infinitely" paradigm eliminates the need for repeated physics-based simulations across diverse pipe networks.

This project focuses on applying deep learning tools to simulate watershed processes, which are known to be dynamic, complex, and exhibit high nonlinearity. Recent studies have demonstrated that deep learning models achieved better streamflow predictions than traditional hydrological models such as conceptual or process-based models. Despite these capabilities, these data-driven models struggle to represent intermediate watershed processes (e.g., infiltration and percolation) during streamflow simulation. These limitations arise because deep learning models lack explicit embedded physical principles in their structure.
Our research examines various deep learning structures capable of integrating mass balance equations as governing physical principles. Neural Ordinary Differential Equations (Neural ODE) and Mass-Conservative Long-Short-Term Memory Networks (MCLSTM) are viable choices for this purpose. This project work will explore the application of these model types through practical case studies. Our goal extends beyond making streamflow predictions for different catchments; we also seek to evaluate the model's effectiveness in predicting streamflow in catchments lacking sufficient hydrological data or having none at all.

The department (LS3) is part of the Institute of Water and Environmental Systems Modeling (IWS). We are an international team from different fields of science. Our research focus is on hydrology (e.g. hydrological modeling, geostatistics, precipitation measurement and simulation), on resources in the natural subsurface (e.g. groundwater, natural gas, gas storage), and on systems in the environment of energy production (e.g. CO2 storage, geothermal energy, energy system planning).

As part of research projects and doctoral theses in the above-mentioned areas, we have a wide range of possible topics for theses. Current topics include precipitation measurement using opportunistic sensors, space-time geostatistics and interpretable machine learning in the field of hydrology.

If you are interested in writing a master's thesis in one of these subject areas at our department, please feel free to contact us to arrange an appointment for a personal consultation.

Drinking water supply is one of the crucial requirements of human society. The drinking water quality in supply pipe networks depends directly on the soil temperature around the pipes, which has increasing influence on the drinking water temperature with increasing residence time. However, since higher drinking water temperatures are more susceptible to bacterial growth, the question of accurate prediction of the temperature in drinking water pipe networks becomes urgent in face of climate change. The typical operational model describing the temperature along water pipes uses a simple exponential function. To address the influence of the meteorological conditions using heat transport from ground surface through the subsurface to the drinking water pipes pilot site was built in VEGAS. numerical model using the numerical simulation framework DuMuX was developed to describe the heat and moisture transport at the pilot site.

The objective of the proposed thesis is to compare both models (exponential function and process-based model). The aim of this analysis is to explore how the knowledge from the complex process-based model can be incorporated into operational temperature forecasts, e.g., used to design new drinking water networks.

For this purpose, the existing DuMuX model of the pilot site will be utilized. Depending on the candidate's preferences and qualifications, the focus of the thesis can be adjusted between real-world operational modelling and numerical implementation of physical models.