TY - CHAP A1 - Markgraf, Klaus A1 - Dietrich, Benjamin A1 - Müller, Katja A1 - Flassig, Robert A1 - Flassig, Peter T1 - FINEconcepts - Wissenstransfer und Energiesystemoptimierung mithilfe des digitalen Zwillings T2 - NWK, HS Harz, 2023 N2 - Climate change, but also geopolitical circumstances, are moving topics such as energy efficiency and renewable energies more and more into the focus of the population, economy , and politics. As a result, the will to optimize new and existing energy systems extends from private individuals to companies and even entire communities. This work describes the development and usage of a new software called FINEconcepts which creates a digital twin of an energy system. This virtual model can then be used to optimize the energy system based on annual costs, CO2 emissions or other relevant criteria such as self-sufficiency. Because all system components, which include renewable technologies as well, can be added as a building block with chosen but changeable parameters, the software allows the user to explore and awaken interest and understanding of technologies that were previously considered too costly, irrelevant, or unrealistic. Implemented projects in small and large companies as well as in residential areas did prove, that the usage of FINEconcepts leads not only to more efficient energy systems by increasing the use of renewable energy, but also increased knowledge and understanding in terms of energy. Besides economics, ecology and security, understanding is an equally important factor in achieving a sustainable energy supply. Y1 - 2023 UR - https://www.hs-harz.de/dokumente/extern/Forschung/NWK2023/Beitraege/FINEconcepts_-_Wissenstransfer_und_Energiesystemoptimierung_mithilfe_des_digitalen_Zwillings.pdf SP - 428 EP - 435 PB - HS Harz ER - TY - CHAP A1 - Müller, Katja A1 - Flassig, Peter A1 - Flassig, Robert T1 - Numerical Simulations and Sensitivity Analysis of Ice Formation on Fan Blades T2 - ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, London, 2024 N2 - In-flight icing, the formation of ice during flight, poses risks to the safety and reliability of aircraft. Due to environmental conditions, ice accumulation occurs on the low-pressure compressor blades of an engine, diminishing aerodynamic performance and potentially causing damage to the engine. Numerical simulations of ice accretion are conducted on the blades of the NASA Rotor 67 utilizing the Computational Fluid Dynamics (CFD) software ANSYS CFX and the in-flight icing software FENSAP-ICE. One-dimensional and two-dimensional sensitivity studies aim to analyze the influences of temperature, droplet diameter and liquid water content (LWC) on the resulting ice build-up on the blade. The analyses reveal that ice accumulates predominantly at the leading edge of the blade, where collection efficiency is maximal. Additionally, an ice layer forms at the blade root on the pressure side. While LWC and temperature exerts a significant influence on the ice mass, only a marginal impact on droplet diameter is observed. Y1 - 2024 SN - 978-0-7918-8806-3 U6 - https://doi.org/10.1115/GT2024-125715 PB - ASME ER - TY - CHAP A1 - Müller, Katja A1 - Markgraf, Klaus A1 - Vogel, Andreas A1 - Flassig, Peter A1 - Flassig, Robert T1 - Numerical Study of Ice Accretion on Fan Blades: Implications for the Design of Blade Geometries T2 - ASME Turbo Expo 2025: Turbomachinery Technical Conference and Exposition, Memphis, Tennessee, USA, 2025 N2 - Ice formation on aircraft components due to the impact of supercooled droplets poses a severe safety risk. In particular, the formation of ice on the fan blades can lead to vibrations that affect the entire engine. While numerous studies have examined the effects of environmental conditions on ice accumulation, the influence of blade geometry has received little attention. This study investigates how variations in blade geometry affect ice accretion in a low-pressure compressor using a numerical approach. A Design of Experiments (DoE) is conducted on the NASA Rotor67, focusing on the sensitivity of ice formation to geometric modifications. The workflow includes geometry generation (ParaBlade), flow simulation (ANSYS CFX), and ice accretion modeling (ANSYS FENSAP-ICE) under rime ice conditions. The results reveal a strong correlation between the inlet metal angle and both accreted ice mass and maximum ice thickness. Furthermore, designs with good aerodynamic performance tend to exhibit higher ice accumulation. These findings enhance the understanding of icing behavior in low-pressure compressors and offer valuable insights for optimizing blade design in adverse environmental conditions. Y1 - 2025 SN - 978-0-7918-8887-2 U6 - https://doi.org/10.1115/GT2025-152704 PB - ASME ER - TY - INPR A1 - Markgraf, Klaus A1 - Müller, Katja A1 - Henkel, Clara A1 - Flassig, Robert A1 - Janke, Christian A1 - Flassig, Peter T1 - Application of Explainable Artificial Intelligence (XAI) in Combination with Bootstrap to Improve Processes in Model-Based Aero-Engine Development N2 - In aerospace engineering, data-driven surrogate models are increasingly employed to mitigate the computational and temporal costs of simulations, numerical analyses, and experiments. Two major challenges accompany this trend. First, the training of surrogate models often requires a sufficient amount of data, the determination of which is inherently difficult. Second, these models often exhibit high complexity, limiting both the traceability of their outputs and the extraction of useful insights. Explainable Artificial Intelligence (XAI) methods have therefore emerged as promising tools to enhance the interpretability, explainability, and transparency of such models. In this work, a combination of the established Shapley Additive Explanations (SHAP) approach with a bootstrap-based method is investigated. The proposed framework provides insights into the contribution of individual features and enables an assessment of data sufficiency with respect to surrogate model performance. Building upon these findings, the Bootstrap-Informed Feature Importance (BIFI) method is proposed. BIFI offers a model-agnostic, robust identification of relevant features.The method is analyzed in the context of Design of Experiments (DOE) processes used for surrogate model construction. Evaluation on four synthetic datasets of increasing complexity, as well as a dataset from aero-engine development, demonstrates that BIFI-based DOEs can improve surrogate model quality measured in terms of R and MSE by up to 90%. Consequently, the proposed method enables more efficient utilization of simulations, computations, and experiments while reducing the required number of samples. Y1 - 2026 U6 - https://doi.org/10.21203/rs.3.rs-8590595/v1 SP - 1 EP - 23 ER -