Mladen Gibanica

Spread in Modal Data Obtained from Wind Turbine Blade Testing

This paper presents a pre-study for an on-going research project in experimental dynamic substructuring, initiated by the SEM Substructuring Focus Group. The focus group has selected a small wind turbine, the Ampair 600W, to serve as test bed for the studies. The turbine blades are considered in this study. A total of 12 blades have been tested for modal properties in a free-free configuration. The data has been acquired and analysed by students participating in the undergraduate course “Structural Dynamics – Model Validation” at Chalmers University of Technology. Each blade was tested by different students as part of their required course work to account for spread in modal properties between the blades. A subset of the blades were tested independently multiple times to account for variability in the test setup. Furthermore, correlation analysis of test data was made with Finite Element model eigensolution data of the blade.

Redundant Information Rejection in Sensor Localisation Using System Gramians

Sensors placement is important in vibration testing. The method of effective independence, recently extended to account for triaxial sensors, is widely used for this purpose in case a finite element model of the structure is available. In this paper a criteria is added to reject redundant information that usually arises in symmetric structures or finite element models with high candidate sensor density. A sensor placement strategy is proposed in which, initially, the method of effective independence is used to select the best sensors from a candidate set by maximising the Fisher information matrix determinant. Next, the gramians of a balanced realisation is used to compare the information between systems consisting of only previously added sensors to the final set with systems of the previous and candidate sensors. Sensors with redundant information will have negligible effect on the gramian and can be rejected. The method is fast, as gramians of systems with only one or two outputs are evaluated. It is sub-optimal in the sense that all possible sensor placement combinations are not evaluated for optimality. A test case, consisting of a rectangular plate, is presented, but the method has been used on a large scale industrial model with good results.

Parameter Estimation and Uncertainty Quantification of a Subframe with Mass Loaded Bushings

In the automotive industry components are often connected through rubber bushings. The bushingsʼ material properties are usually not well known. In computational models these properties are parametrised and their spread can be considerable. A good estimate of these parameters is important in various applications, including substructuring, and for uncertainty quantification of systems with connected components. This paper deals with the calibration of an industrial size finite element model of a car subframe with parametrised bushing models. Mass loading is used on the bushings to bring local bushing modes to a lower frequency region and impose a more realistic boundary condition in component testing. The model parameters can be calibrated in different ways. In this paper two approaches are considered. They are based on two test configurations, one with and one without mass loaded boundaries. In the first case only the bushing parameters are considered for the mass loaded boundary configuration. In the second case, consisting of two steps, the configuration without mass loaded boundaries is considered in which the bushing parameters are first fixed and other model parameters considered, and in the last step a subset of all parameters is considered. The calibration, validation and uncertainty quantification, using bootstrapping, have been performed using the open-source MATLAB tool FEMcali.

Calibration, Validation and Uncertainty Quantification of Nominally Identical Car Subframes

In this paper a finite element model, with over half a million degrees-of-freedom, of a car front subframe has been calibrated and validated against experimental MIMO data of several nominally identical components. The spread between the individual components has been investigated and is reported. Sensor positioning was performed with an extended effective independence method, using system gramians to reject sensors with redundant information. The Fisher information matrix was used in the identification of the most significant model calibration parameters. Validation of the calibrated model was performed to evaluated the difference between the nominal and calibrated model, and bootstrapping used to investigate the validity of the calibrated parameters. The parameter identification, calibration, validation and bootstrapping have been performed using the open-source MATLAB tool FEMCali.

Improving Linear State-Space Models with Additional Iterations

An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of the B, C, and D matrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLAB® functions, ssest generally outperforms n4sid.

Experimental-Analytical Dynamic Substructuring of Ampair Testbed: A State-Space Approach

The Society for Experimental Mechanics (SEM) Substructuring Focus Group has initiated a research project in experimental dynamic substructuring using the Ampair 600W wind turbine as a testbed. In this paper, experimental as well as analytical models of the blades of said wind turbine are coupled to analytical models of its brackets. The focus is on a state-space based substructuring method designed specifically for experimental-analytical dynamic substructuring. It is shown (a) theoretically that the state-space method gives equivalent results to the second order methods under certain conditions, (b) that the state-space method numerically produces results equivalent to those of a well-known frequency-based substructuring technique when the same experimental models are used for the two methods and (c) that the state-space synthesis procedure can be translated to the general framework given by De Klerk et al.