UROP Project

3D-Variational Data Assimilation for Geothermal Applications



Daniel Holder

Program Director UROP


+49 241 80-90695


Key Info

Basic Information

Project Offer-Number:
UROP International
Organisation unit:
Aachen Institute for Advanced Study in Computational Engineering Science
Language Skills:
Computer Skills:
Python, C++


Climate change and other environmental changes make a responsible and sustainable usage of the earth's resources, such as groundwater and energy, increasingly important. Numerical Simulations provide a great tool to understand the complex nature in space, time, and parameter space of geophysical problems, and can thereby help to optimize the allocation of these resources in the future. With this project, we aim to improve the evaluation of the geothermal thermal potential in Europe to provide a clean energy source for the future. Therefore, we want to improve the determination of the lower thermal boundary condition of steady-state conductive heat transfer problems by using 3D variational data assimilation. The 3D-VAR method defines the assimilated solution as the minimizer of a cost functional that penalizes both deviations from the data and from an a priori best-knowledge model. A bottleneck is the computation of this minimizer, which we overcome by using reduced order models.


In order to ensure the usability of the presented concept for a broad range of applications, it needs to be transferred to an open-source, high-performance library environment. Therefore, we rely on the MOOSE framework, primarily developed by the Idaho National Laboratory. In the first stage of this project, an existing 3D-VAR Python implementation should be transferred into a C++ based MOOSE application. This requires good prior programming experience with the programming language C++. In the second stage of the project, the students apply the developed software package to a variety of geophysical applications with an increasing degree of complexity.


For this project programming experience is essential.