Deriving numerical representation of real-life structural systems usually leads to high complexity models. In addition, the ever-increasing engineering and manufacturing demands require the treatment of intricate systems.
However, this increased complexity is usually bad news and makes things difficult to understand and handle. The goal of this project is to research into (dynamic) substructuring: A divide and conquer approach that aims to break down the system and address each component separately.
This allows an engineer to obtain a clear view on complex or high-dimensional models and understand the interaction between components while enabling even parallel working requirements on a workspace
The goal of this project is to research dynamic substructuring methodologies for the component-oriented treatment of complex, intricate structures. The derived methodology can then be used for damage localization or vibration monitoring applications.
In addition, if the student is interested in model order reduction application, the context can be adapted to deriving a physics-based reduced representation of structural systems with localized nonlinear features. The goal in this case would be to develop a divide and conquer strategy that treats every individual component separately and naturally couples the response between linear and nonlinear subdomains of the structure.
Also available here: (Theme 3)
https://chatzi.ibk.ethz.ch/education/projects---theses/master-thesis.html
**Disclaimer:** Since the project/thesis is provided from the IBK Chair on DBAUG, depending on the student's ETH department a second supervisor from their own department might be needed.
**References:**
[1] Tatsis, K. E., et al. "A general substructure-based framework for input-state estimation using limited output measurements." Mechanical Systems and Signal Processing 150 (2021): 107223.
[2] Wu, Long, et al. "A modal derivatives enhanced Rubin substructuring method for geometrically nonlinear multibody systems." Multibody system dynamics 45.1 (2019): 57-85.
[3] Vlachas, Konstantinos, et al. "On the Coupling of Reduced Order Modeling with Substructuring of Structural Systems with Component Nonlinearities." Dynamic Substructures, Volume 4. Springer, Cham, 2022. 35-43.
The goal of this project is to research dynamic substructuring methodologies for the component-oriented treatment of complex, intricate structures. The derived methodology can then be used for damage localization or vibration monitoring applications.
In addition, if the student is interested in model order reduction application, the context can be adapted to deriving a physics-based reduced representation of structural systems with localized nonlinear features. The goal in this case would be to develop a divide and conquer strategy that treats every individual component separately and naturally couples the response between linear and nonlinear subdomains of the structure.
Also available here: (Theme 3) https://chatzi.ibk.ethz.ch/education/projects---theses/master-thesis.html
**Disclaimer:** Since the project/thesis is provided from the IBK Chair on DBAUG, depending on the student's ETH department a second supervisor from their own department might be needed.
**References:** [1] Tatsis, K. E., et al. "A general substructure-based framework for input-state estimation using limited output measurements." Mechanical Systems and Signal Processing 150 (2021): 107223. [2] Wu, Long, et al. "A modal derivatives enhanced Rubin substructuring method for geometrically nonlinear multibody systems." Multibody system dynamics 45.1 (2019): 57-85. [3] Vlachas, Konstantinos, et al. "On the Coupling of Reduced Order Modeling with Substructuring of Structural Systems with Component Nonlinearities." Dynamic Substructures, Volume 4. Springer, Cham, 2022. 35-43.
The aim of this project/thesis is the assembly of a dynamic substructuring framework that deals with localized nonlinear feature inclusion.
The aim of this project/thesis is the assembly of a dynamic substructuring framework that deals with localized nonlinear feature inclusion.