@misc{BeirowNakosStecklinaetal., author = {Beirow, Bernd and Nakos, Alex and Stecklina, Caroline and Noack, Martin and Firl, Matthias and Sasakaros, Marios}, title = {Implementation of intentional mistuning by means of finite element based shape optimization}, series = {Journal of engineering for gas turbines and power}, journal = {Journal of engineering for gas turbines and power}, publisher = {ASME International}, address = {New York}, issn = {0742-4795}, doi = {10.1115/1.4069624}, pages = {1 -- 15}, abstract = {Intentional Mistuning has turned out to be an effective measure to alleviate the maximum forced response of bladed wheels in the framework of numerous studies in the past. In particular solutions based on two different blade designs, following e.g. alternating or AABB patterns, have proved to be promising in this regard and moreover robust against the impact of unavoidable random mistuning. Thus, for example, a 40 percent reduction of the first blade bending maximum forced response has been proved experimentally for a turbine impeller of a turbo charger application. Despite this success, the technical implementation of the frequency based mistuning pattern followed an academic solution based on locally removing material at the leading edge tip, which is not suited for the use in serial wheels since it may disturb the flow channel. In addition, the forced response of other blade modes may be affected in a negative manner. In order to overcome these problems, an alternative way of implementing Intentional Mistuning is suggested by applying a marginal geometric modification of the blade thickness distribution to adjust the natural frequency of the first bending mode. Finite element based shape optimization is utilized to this end. Secondary conditions are ensuring that only the target frequency of the first bending mode is adjusted whereas natural frequencies of other modes are kept almost unchanged.}, language = {en} } @misc{BeirowNakosStecklinaetal., author = {Beirow, Bernd and Nakos, Alex and Stecklina, Caroline and Noack, Martin and Firl, Matthias and Sasakaros, Marios}, title = {Implementation of intentional mistuning by means of finite element based shape optimization}, series = {Proceedings of the ASME Turbo Expo 2025, Memphis, June 16-20, 2025}, journal = {Proceedings of the ASME Turbo Expo 2025, Memphis, June 16-20, 2025}, publisher = {The American Society of Mechanical Engineers}, address = {New York, NY}, isbn = {978-0-7918-8885-8}, doi = {10.1115/GT2025-151617}, pages = {11}, abstract = {Intentional Mistuning has turned out to be an effective measure to alleviate the maximum forced response of bladed wheels in the framework of numerous studies in the past. In particular solutions based on two different blade designs, following e.g. alternating or AABB patterns, have proved to be promising in this regard and moreover robust against the impact of unavoidable random mistuning. Thus, for example, a 40 percent reduction of the first blade bending maximum forced response has been proved experimentally for a turbine impeller of a turbo charger application. Despite this success, the technical implementation of the frequency based mistuning pattern followed an academic solution based on locally removing material at the leading edge tip, which is not suited for the use in serial wheels since it may disturb the flow channel. In addition, the forced response of other blade modes may be affected in a negative manner. In order to overcome these problems, an alternative way of implementing Intentional Mistuning is suggested by applying a marginal geometric modification of the blade thickness distribution to adjust the natural frequency of the first bending mode. Finite element based shape optimization is utilized to this end. Secondary conditions are ensuring that only the target frequency of the first bending mode is adjusted whereas natural frequencies of other modes are kept almost unchanged.}, language = {en} } @techreport{KlattPfeifferBendigetal., author = {Klatt, Matthias and Pfeiffer, Klaus and Bendig, Martin and Platta, Kristian and Matzekat, David}, title = {Betrachtungen zur Mindesterzeugung von Braunkohlekraftwerken im Kontext des Netzbetriebs}, publisher = {Brandenburgische Technische Universit{\"a}t, Fachgebiet Dezentrale Energiesysteme [u.a.]}, address = {Cottbus ; Senftenberg}, language = {de} } @misc{SchmittSowoidnichGosswamietal., author = {Schmitt, Ingo and Sowoidnich, Kay and Gosswami, Tapashi and Sumpf, Bernd and Maiwald, Martin and Wolff, Matthias}, title = {PCA-based peak feature selection for classification of spectroscopic datasets}, series = {Journal of chemometrics}, volume = {39}, journal = {Journal of chemometrics}, number = {11}, publisher = {John Wiley \& Sons Ltd.}, address = {New York, NY}, issn = {0886-9383}, doi = {10.1002/cem.70074}, pages = {1 -- 14}, abstract = {Reducing feature dimensionality in spectroscopic data is crucial for efficient analysis and classification. Using all available features for classification typically results in an unacceptably high runtime and poor accuracy. Popular feature extraction methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and autoencoders, reduce feature dimensionality by extracting latent features that can be challenging to interpret. To enable better human interpretation of the classification model, we avoid extraction methods and instead propose applying feature selection methods. In this work, we develop an innovative PCA-based feature selection method for spectroscopic data, providing an essential subset of the original features. As an important advantage, no prior knowledge about the characteristic signals of the respective target substance is required. In this proof-of-concept study, the proposed method is initially characterized using simulated Raman and infrared absorption datasets. From the top five PCA eigenvectors of spectroscopic data, we identify a set of three top peaks each at specific wavenumbers (features). The compact set of selected features is then used for classification tasks applying a decision tree. Based on two well-defined spectroscopic datasets, our study demonstrates that our new method of PCA-based peak finding outperforms selected other approaches with regard to interpretability and accuracy. For both investigated datasets, accuracies greater than 97\% are achieved. Our approach shows large potential for accurate classification combined with interpretability in further scenarios involving spectroscopic datasets.}, language = {en} }