02 Fakultät Bau- und Umweltingenieurwissenschaften

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Author: search.filters.author.Graf, Thomas

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    Data-driven prediction and uncertainty quantification of process parameters for directed energy deposition
    (2023) Hermann, Florian; Michalowski, Andreas; Brünnette, Tim; Reimann, Peter; Vogt, Sabrina; Graf, Thomas
    Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.
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    Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste : introduction of a research initiative
    (2024) Kurgyis, Kata; Achtziger-Zupančič, Peter; Bjorge, Merle; Boxberg, Marc S.; Broggi, Matteo; Buchwald, Jörg; Ernst, Oliver G.; Flügge, Judith; Ganopolski, Andrey; Graf, Thomas; Kortenbruck, Philipp; Kowalski, Julia; Kreye, Phillip; Kukla, Peter; Mayr, Sibylle; Miro, Shorash; Nagel, Thomas; Nowak, Wolfgang; Oladyshkin, Sergey; Renz, Alexander; Rienäcker-Burschil, Julia; Röhlig, Klaus-Jürgen; Sträter, Oliver; Thiedau, Jan; Wagner, Florian; Wellmann, Florian; Wengler, Marc; Wolf, Jens; Rühaak, Wolfram
    The Federal Company for Radioactive Waste Disposal (BGE mbH) is tasked with the selection of a site for a high-level radioactive waste repository in Germany in accordance with the Repository Site Selection Act. In September 2020, 90 areas with favorable geological conditions were identified as part of step 1 in phase 1 of the Site Selection Act. Representative preliminary safety analyses are to be carried out next  to support decisions on the question, which siting regions should undergo surface-based exploration. These safety analyses are supported by numerical simulations building on geoscientific and technical data. The models that are taken into account are associated with various sources of uncertainties. Addressing these uncertainties and the robustness of the decisions pertaining to sites and design choices is a central component of the site selection process. In that context, important research objectives are associated with the question of how uncertainty should be treated through the various data collection, modeling and decision-making processes of the site selection procedure, and how the robustness of the repository system should be improved. BGE, therefore, established an interdisciplinary research cluster to identify open questions and to address the gaps in knowledge in six complementary research projects. In this paper, we introduce the overall purpose and the five thematic groups that constitute this research cluster. We discuss the specific questions addressed as well as the proposed methodologies in the context of the challenges of the site selection process in Germany. Finally, some conclusions are drawn on the potential benefits of a large method-centered research cluster in terms of simulation data management.