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M. Sc. Merten Stender

Bio: Merten received his Bachelor of Science in Mechanical Engineering from Hamburg University of Technology (TUHH) in 2013. During his Master degree (Theoretical Mechanical Engineering) at TUHH he focused on computational methods for engineering (FEM, CFD) and energy systems. In 2014 he joined the Nonlinear Dynamics Summer School hosted by Sandia Labs at University of New Mexico, Albuquerque, NM, USA. During this 6-week project he collaborated with several international researchers on optimized joint damping. For the Master Thesis Merten joined the VUTC at Imperial College London to work on fundamental research in experimental tribology, digital image correlation and nonlinear dynamics. Merten was nominated as 'GAMM Junior' by the Gesellschaft für Angewandte Mathematik und Mechanik for the years 2021-2023.


Research: In October 2016 Merten started his PhD at the Dynamics Group at TUHH. He successfully defended his thesis in October 2020. His research focuses on nonlinear vibrations in complex dynamical systems and machine learning techniques. He particularly interested in hybrid methods coupling classical physics-based simulations with data-driven methods. Explainable Machine Learning techniques complement Merten's research interests. Earlier application studies concentrated on friction-affected and friction-excited systems, such as automotive brake systems. Recently, application cases range from fault detection for artificial hip joints to data-driven ice material modeling and wind farm optimization through reinforcement learning. Major parts of his research are sponsored by the German Research Foundation (Deutsche Forschungsgesellschaft DFG) within the Priority Program 1897 'calm, smooth, smart'.

 

Collaborations and student projects: Merten is always eager to collaborate on digital twins and data-driven techniques for dynamical systems! Students who want to write their final thesis in one of the related topics should be able to present profound coding skills in Matlab, Python or any other relevant language. Optimally, students can showcase knowledge in dynamical systems and also in small machine learning projects.

 

Professional

PhD Candidate
Dynamics Group
Hamburg University of Technology

 

E-Mail

ResearchGate

GoogleScholar

Peer-reviewed journal publications

 

  1. Kellner, L.; Stender, M.; von Bock und Plach, F.; Ehlers, S.; Analyzing the complexity of ice with explainable machine learning models. npj Computational Materials (submitted 01/11/2020)
  2. Di Bartolomeo, M.; Lazzari, A.; Stender, M.; Berthier, Y.; Saulot, A.; Massi, F. (2020). Experimental observation of thermally-driven frictional instabilities on C/C materials. Tribology International, 106724. DOI:10.1016/j.triboint.2020.106724
  3. Martin, Richard; Stender, Merten; Oberst, Sebastian: Numerical analysis of dynamic hysteresis in tape springs for space applications. In: Vibration Engineering for a Sustainable Future 2020 (in press).
  4. Stender, Merten; Jahn, Martin; Hoffmann, Norbert; Wallaschek, Jörg (2020): Hyperchaos co-existing with periodic orbits in a frictional oscillator. In: Journal of Sound and Vibration 472, S. 115–203. DOI: 10.1016/j.jsv.2020.115203.
  5. Stender, Merten, Tiedemann, Merten, Spieler, David, Schoepflin, Daniel, Hoffmann, Norbert, & Oberst, Sebastian: Deep learning for brake squeal: Brake noise detection, characterization and prediction. Mechanical Systems and Signal Processing, 149, 107181. DOI: 10.1016/j.ymssp.2020.107181
  6. Block und Polach, Rüdiger U. Franz von; Gralher, Silke; Ettema, Robert; Kellner, Leon; Stender, Merten (2019): The non-linear behavior of aqueous model ice in downward flexure. In: Cold Regions Science and Technology. DOI: 10.1016/j.coldregions.2019.05.001.
  7. Didonna, Marco; Stender, Merten; Papangelo, Antonio; Fontanela, Filipe; Ciavarella, Michele; Hoffmann, Norbert (2019): Reconstruction of Governing Equations from Vibration Measurements for Geometrically Nonlinear Systems. In: Lubricants 7 (8), S. 64. DOI: 10.3390/lubricants7080064.
  8. Gnanasambandham, C.; Stender, M.; Hoffmann, N.; Eberhard, P. (2019): Multi-scale dynamics of particle dampers using wavelets: Extracting particle activity metrics from ring down experiments. In: Journal of Sound and Vibration 454, S. 1–13. DOI: 10.1016/j.jsv.2019.04.009.
  9. Jahn, Martin; Stender, Merten; Tatzko, Sebastian; Hoffmann, Norbert; Grolet, Aurélien; Wallaschek, Jörg (2019): The extended periodic motion concept for fast limit cycle detection of self-excited systems. In: Computers & Structures, S. 106–13 DOI: 10.1016/j.compstruc.2019.106139.
  10. Kellner, Leon; Stender, Merten; Bock und Polach, Rüdiger U. Franz von; Herrnring, Hauke; Ehlers, Sören; Hoffmann, Norbert; Høyland, Knut V. (2019): Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior. In: Cold Regions Science and Technology 162, S. 56–73. DOI: 1016/j.coldregions.2019.02.007.
  11. Stender, Merten; Di Bartolomeo, Mariano; Massi, Francesco; Hoffmann, Norbert (2019): Revealing transitions in friction-excited vibrations by nonlinear time-series analysis. In: Nonlinear Dyn 47 (7), S. 209. DOI: 10.1007/s11071-019-04987-7.
  12. Stender, Merten; Oberst, Sebastian; Hoffmann, Norbert (2019): Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction. In: Vibration 2 (1), S. 25–46. DOI: 10.3390/vibration2010002.
  13. Stender, Merten; Oberst, Sebastian; Tiedemann, Merten; Hoffmann, Norbert (2019): Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data. In: Nonlinear Dyn 267 (1), S. 105. DOI: 10.1007/s11071-019-05143-x.
  14. Stender, Merten; Tiedemann, Merten; Hoffmann, Lando; Hoffmann, Norbert (2019): Determining growth rates of instabilities from time-series vibration data: Methods and applications for brake squeal. In: Mechanical Systems and Signal Processing 129, S. 250–264. DOI: 10.1016/j.ymssp.2019.04.009.
  15. Stender, Merten; Tiedemann, Merten; Hoffmann, Norbert (2019): Energy harvesting below the onset of flutter. In: Journal of Sound and Vibration 458, S. 17–21. DOI: 10.1016/j.jsv.2019.06.0
  16. Papangelo, A.; Hoffmann, N.; Grolet, A.; Stender, M.; Ciavarella, M. (2018): Multiple spatially localized dynamical states in friction-excited oscillator chains. In: Journal of Sound and Vibration 417, S. 56–64. DOI: 10.1016/j.jsv.2017.11.056.
  17. Stender, Merten; Tiedemann, Merten; Hoffmann, Norbert; Oberst, Sebastian (2018): Impact of an irregular friction formulation on dynamics of a minimal model for brake squeal. In: Mechanical Systems and Signal Processing 107, S. 439–451. DOI: 10.1016/j.ymssp.2018.01.032.
  18. Pesaresi, L.; Stender, M.; Ruffini, V.; Schwingshackl, C. W. (2017): DIC Measurement of the Kinematics of a Friction Damper for Turbine Applications. In: Matthew S. Allen, Randall L. Mayes und Daniel Jean Rixen (Hg.): Dynamics of Coupled Structures, Volume 4: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017. Cham: Springer International Publishing, S. 93–101. Online verfügbar unter dx.doi.org/10.1007/978-3-319-54930-9_9.
  19. Stender, Merten; Tiedemann, Merten; Hoffmann, Norbert (2017): Characterization of complex states for friction-excited systems. In: Proc. Appl. Math. Mech. 17 (1), S. 45–46. DOI: 10.1002/pamm.201710013.
  20. Stender, Merten; Papangelo, Antonio; Allen, Matt; Brake, M.; Schwingshackl, C.; Tiedemann, Merten (2016): Structural Design with Joints for Maximum Dissipation. In: Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics, Volume 9: Springer, S. 179–187.
  21. Tiedemann, Merten; Stender, Merten; Hoffmann, Norbert (2015): On vibrations in non-linear, forced, friction-excited systems. In: Proc. Appl. Math. Mech. 15 (1), S. 267–268. DOI: 10.1002/pamm.201510124.

Current projects

Automotive Disc Brake Squeal [Vibro-acoustics, Friction-induced vibrations]

Physics-informed Learning

- Selected Topics in Advanced Vibrations

- Nonlinear Dynamics

Talks and Presentations

 

(2020) International Conference on Noise and Vibration Engineering (ISMA): Deep learning for predicting brake squeal. Leuven / Netherlands

... to be completed soon!