TY - RPRT A1 - Sterner, Michael A1 - Thema, Martin A1 - Eckert, Fabian A1 - Moser, Albert A1 - Schäfer, Andreas A1 - Drees, Tim A1 - Christian Rehtanz, A1 - Ulf Häger, A1 - Kays, Jan A1 - Seack, André A1 - Dirk Uwe Sauer, A1 - Matthias Leuthold, A1 - Philipp Stöcker, T1 - Stromspeicher in der Energiewende - Untersuchung zum Bedarf an neuen Stromspeichern in Deutschland für den Erzeugungsausgleich, Systemdienstleistungen und im Verteilnetz BT - Studie N2 - Wie groß ist der Speicherbedarf in Deutschland in der weiteren Umsetzung der Energiewende? Welche Rolle spielen Batteriespeicher, Pumpspeicher, Power-to-Gas etc. im Kontext anderer Flexibilitätsoptionen auf den verschiedenen Netzebenen? Wie entwickelt sich der Markt für Batterien und Wasserstoff? In unserer Agora-Speicherstudie haben wir auch erstmalig den Begriff Power-to-X definiert und damit die bis dato entstandenen Begriffe Power-to-Gas, Power-to-Liquids, Power-to-Products, Power-to-Chemicals etc. zusammengefasst. Y1 - 2014 U6 - https://doi.org/10.13140/RG.2.2.31804.56964 PB - Agora Energiewende CY - Berlin ER - TY - GEN A1 - Lehrer, Tobias A1 - Stocker, Philipp A1 - Duddeck, Fabian A1 - Wagner, Marcus T1 - Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment T2 - Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023 N2 - Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development. To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment. This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts. We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous. Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-64772 PB - International Centre for Numerical Methods in Engineering ER -