Identification and synthesis of components for uncertainty propagation
Doctoral thesis, 2020

For automotive structures, built-up of hundreds of components with property spread, knowing the effects of component variability and its propagation through the system assembly is important in order to mitigate noise and vibration problems. To increase the understanding of how the spread propagates into variability in built-up structures, both experimental and computational aspects are considered in this thesis.

In the first part of the thesis, methods to identify models from experimental data are developed. Physical insight is often required for accurate experimental models. To this end, two-phase state-space system identification algorithms are developed where physically motivated residual states are included and physically motivated constraints are enforced. The developed identification algorithms are used together with finite element model updating to investigate the variability in dynamical properties between nominally identical components. Furthermore, the accurate and physical experimental models are used in synthesis with the updated finite element models. It is shown that experimental-analytical synthesis of complex and modally dense structures is possible, and that the component variability can be predicted in such assemblies.

In the second part of the thesis, methods to reduce the computational cost of variability analysis are developed. An efficient multifidelity interface reduction method is developed for component synthesis. It is also shown that modal truncation augmentation vectors can be computed efficiently from the multifidelity interface reduction basis. Lastly, an efficient uncertainty propagation method is developed, based on a second-order modal model. Utilising several approximations, it is shown that industrial-sized models can be handled with small loss in accuracy compared to a purely Monte Carlo based approach.


system identification

interface reduction

uncertainty quantification

experimental methods

uncertainty propagation

Monte Carlo method

state-space models

surrogate modelling

EC, Hörsalsvägen 11, Göteborg
Opponent: Prof. Etienne Balmès, Arts et Métiers ParisTech, Paris, France


Mladen Gibanica

Chalmers, Mechanics and Maritime Sciences, Dynamics

Model updating of multiple nominally identical car components

Experimental Techniques,; Vol. 44(2020)p. 391-407

Journal article

State-space system identification with physically motivated residual states and throughput rank constraint

Mechanical Systems and Signal Processing,; Vol. 142(2020)

Journal article

Multifidelity component interface reduction and modal truncation augmentation

International Journal for Numerical Methods in Engineering,; Vol. 120(2019)p. 105-124

Journal article

Identification of physically realistic state-space models for accurate component synthesis

Mechanical Systems and Signal Processing,; Vol. 145(2020)

Journal article

M. Gibanica and T. J. S. Abrahamsson, Data-Driven Modal Surrogate Model for Frequency Response Uncertainty Propagation

Subject Categories

Applied Mechanics

Computational Mathematics

Control Engineering

Signal Processing



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4707


Chalmers University of Technology

EC, Hörsalsvägen 11, Göteborg

Opponent: Prof. Etienne Balmès, Arts et Métiers ParisTech, Paris, France

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Latest update

1/9/2021 1