Domestic research projects


Research projects (co)funded by the Slovenian Research Agency.


  • Member of University of Ljubljana: UL Faculty of Mechanical Engineering
  • Project code: N2-0144
  • Project title: Vision based reduced order modeling approach for operational parameter identification of nonlinear finite element models
  • Period: 01.01.2020 - 31.12.2023
  • Range on year: 0,97 FTE
  • Head: prof. dr. Miha Boltežar
  • Research activity: Engineering sciences and technologies 
  • Research Organisation: Link
  • Researchers: Link
  • Citations for bibliographic records: Link

Accurate dynamic identification of mechanical components is key for current and future Digital Twins of mechanical systems. However, current state-of-the-art methods do not allow non-intrusive and high-spatial density based parameter identification for components under operational conditions. The focus of this project is to develop a framework for identifying these operational parameters for detailed nonlinear finite element component models from non-contact and high spatial density optical measurements.


In order to obtain this framework, a fundamentally new approach will be developed which tackles the image measurements and efficient inverse model evaluation in a strongly integrated setting. This approach will revolve around three key contributions. First a methodology will be developed for extracting a detailed deformation field over a wide frequency range from (relatively low frequency) image data. In an original contribution, this deformation field data will be exploited for setting up parameterized reduced order models in order to circumvent the high setup cost typically associated to these approaches. The image based data and reduced order models are then combined in a parameter identification process in the frequency or time domain, depending on the component of interest. These developments will be validated on a range of experimental setups, ranging from academic to industrial complexity. 

The phases of the project and their realization:

WP 1 Vision based dynamic deformation and reduced order basis extraction  

T1.1: SEMM based 1 order of magnitude better optical modal identification of linear structures  

T1.2: Speckle pattern based modal identification for high spatial density data     


WP 2 Vision based parametric reduced order model definition    

T2.1: Parametric ROM using vision based ROBs for linear models  

T2.2: Parametric ROM using vision based ROBs for nonlinear material  models  

T2.3: Parametric ROM using vision based ROBs for large deformation models    


WP 3 (Nonlinear) Finite-element parameter identification from image data and reduced order models     

T3.1: Image based frequency-domain model parameter identification  

T3.2: Image based time-domain model parameter identification  


WP 4 Experimental validation  

T4.1  Academic experiments for concept development

T4.2 Industrial scale test-cases for validation