@inproceedings{JankeGollerMartinetal., author = {Janke, Christian and Goller, M. and Martin, Ivo and Gaun, Lilia and Bestle, Dieter}, title = {3D-CFD Compressor Map Computation of a Multi-Stage Axial Compressor with Off-Design Adjusted Rotor Geometries}, series = {ASME Turbo Expo 2016, Turbomachinery Technical Conference and Exposition, Volume 7A, Structures and Dynamics, Seoul, South Korea, June 13-17, 2016}, booktitle = {ASME Turbo Expo 2016, Turbomachinery Technical Conference and Exposition, Volume 7A, Structures and Dynamics, Seoul, South Korea, June 13-17, 2016}, publisher = {ASME}, address = {New York, NY [u.a.]}, isbn = {978-0-7918-4983-5}, language = {en} } @misc{MartinBestle, author = {Martin, Ivo and Bestle, Dieter}, title = {Automated eigenmode classification for airfoils in the presence of fixation uncertainties}, series = {Engineering Applications of Artificial Intelligence}, volume = {67}, journal = {Engineering Applications of Artificial Intelligence}, issn = {0952-1976}, doi = {https://doi.org/10.1016/j.engappai.2017.09.022}, pages = {187 -- 196}, abstract = {Abstract Automated structural design optimization should take into acc ount risk of failure which depends on eigenmodes, since eigenmode shap es determine failure risk by their characteristic stress concentration pattern, as well as by their specific interaction with excitations. T hus, such a process needs to be able to identify eigenmodes with low e rror rate. This is a rather challenging task, because eigenmodes depen d on the geometry of the structure which is changing during the design process, and on boundary conditions which are not clearly defined due to uncertainties in the assembly and running conditions. The present investigation aims to find a proper classification method for eigenmod es of compressor airfoils. Specific data normalization and data depend ent initialization of a neural network using principle-component direc tions as initial weight vectors have led to the development of a class ification and decision procedure enabling automatic assignment of prop er uncertainty bands to eigenfrequencies of a specific eigenmode shape . Application to compressor airfoils of a stationary gas-turbine with hammer-foot and dove-tail roots demonstrates the high performance of t he proposed procedure.}, language = {en} } @misc{FischerMarchettiDeschmannAssisetal., author = {Fischer, Thomas and Marchetti-Deschmann, Martina and Assis, Ana Cristina and Elad, Michal Levin and Algarra, Manuel and Barac, Marko and Bogdanovic Radovic, Iva and Cicconi, Flavio and Claes, Britt and Frascione, Nunzianda and George, Sony and Guedes, Alexandra and Heaton, Cameron and Heeren, Ron and Lasic, Violeta and Lerma, Jos{\´e} Luis and del Valle Martinez de Yuso Garcia, Maria and Nosko, Martin and O'Hara, John and Oshina, Ilze and Palucci, Antonio and Pawlaczyk, Aleksandra and Posp{\´i}škov{\´a}, Krist{\´y}na and de Puit, Marcel and Radodic, Ksenija and Rēpele, Māra and Ristova, Mimoza and Romolo, Francesco Saverio and Šafař{\´i}k, Ivo and Siketic, Zdravko and Spigulis, Janis and Szynkowska-Jozwik, Malgorzata Iwona and Tsiatsiuyeu, Andrei and Vella, Joanna and Dawson, Lorna and R{\"o}diger, Stefan and Francese, Simona}, title = {Profiling and imaging of forensic evidence - A pan-European forensic round robin study part 1: Document forgery}, series = {Science \& Justice}, volume = {62}, journal = {Science \& Justice}, number = {4}, issn = {1876-4452}, doi = {10.1016/j.scijus.2022.06.001}, pages = {433 -- 447}, language = {en} } @inproceedings{MartinBestle, author = {Martin, Ivo and Bestle, Dieter}, title = {Automated Mode Identification of Airfoil Geometries to be used in an Optimization Process}, series = {ASME Turbo Expo 2016, Turbomachinery Technical Conference and Exposition, Volume 7A, Structures and Dynamics, Seoul, South Korea, June 13-17, 2016}, booktitle = {ASME Turbo Expo 2016, Turbomachinery Technical Conference and Exposition, Volume 7A, Structures and Dynamics, Seoul, South Korea, June 13-17, 2016}, publisher = {ASME}, address = {New York, NY}, isbn = {978-0-7918-4983-5}, doi = {10.1115/GT2016-56987}, pages = {12}, language = {en} } @techreport{BestleLockanHartwigetal., author = {Bestle, Dieter and Lockan, Michael and Hartwig, Lennard and Martin, Ivo}, title = {Q3D Optimierung vielstufiger Verdichter in der Vorauslegung und robuste multidisziplin{\"a}re Schaufelauslegung}, publisher = {Brandenburgische Technische Universit{\"a}t Cottbus-Senftenberg, IKMZ-Universit{\"a}tsbibliothek}, address = {Cottbus ; Senftenberg}, isbn = {978-3-940471-30-7}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-42225}, pages = {33}, language = {de} } @phdthesis{Martin, author = {Martin, Ivo}, title = {Automated Process for Robust Airfoil Design-Optimization Incorporating Critical Eigenmode Identification and Production-Tolerance Evaluation}, address = {Cottbus}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-47136}, pages = {158}, abstract = {For many countries, gasturbine technology is one of the key technologies for the reduction of climate-damaging pollutant emissions. The profitability of such facilities, however, is highly dependent on the price for the utilized fossil fuel, which is why there is a constant need for increased efficiency. The potential of increasing the efficiency of the individual components is basically limited by factors which will reduce operating life. The goal of this thesis is to develop methods for improved automated structural design optimization, which shall be developed on the basis of compressor airfoils. Special attention is payed to avoid the excitation of failure critical eigenmodes by detecting them automatically. This is achieved by introducing a method based on self-organizing neural networks which enables the projection of eigenmodes of arbitrary airfoil geometries onto standard surfaces, thereby making them comparable. Another neural network is applied to identify eigenmodes which have been defined as critical for operating life. The failure rate of such classifiers is significantly reduced by introducing a newly developed initialization method based on principle components. A structural optimization is set up which shifts the eigenfrequency bands of critical modes in such a way that the risk of resonance with engine orders is minimized. In order to ensure practical relevance of optimization results, the structural optimization is coupled with an aerodynamic optimization in a combined process. Conformity between the loaded hot-geometry utilized by the aerodynamic design assessment and the unloaded cold-geometry utilized by the structural design assessment is ensured by using loaded-to-unloaded geometry transformation. Therefor an innovative method is introduced which, other than the established time-consuming iterative approach, uses negative density for a direct transformation taking only a few seconds, hence, making it applicable to optimization. Additionally, in order for the optimal designs to be robust against manufacturing variations, a method is developed which allows to assess the maximum production tolerance of a design from which onwards possible design variations are likely to violate design constraints. In contrast to the usually applied failure rate, the production tolerance is a valid requirement for suppliers w.r.t. expensive parts produced in low-quantity, and therefore is a more suitable optimization objective.}, language = {en} } @misc{MartinHartwigBestle, author = {Martin, Ivo and Hartwig, Lennard and Bestle, Dieter}, title = {A multi-objective optimization framework for robust axial compressor airfoil design}, series = {Structural and Multidisciplinary Optimization}, volume = {59}, journal = {Structural and Multidisciplinary Optimization}, number = {6}, issn = {1615-1488}, doi = {10.1007/s00158-018-2164-3}, pages = {1935 -- 1947}, language = {en} }