On the one hand, uncertainty in the vibration behavior of truss structures is controlled by vibration attenuation of several beams with multiple shunted piezo-elastic beam supports. On the other hand, novel (semi-)active kinematic guidance elements are developed and investigated to control uncertainty in the load distribution of kinematically connected trusses in the SFB-Demonstrator.

In sub-project C7a, piezoelectric shunt damping with RL- and RLC-shunt is investigated as a semi-active approach for vibration attenuation of a beam with circular cross section (see Figure 1).

There, the influence of epistemic data uncertainty realized by the variation of static axial loads on the vibration attenuation via piezoelectric shunt damping is investigated. The variation of static loads results in a decrease of the first resonance frequency for compressive loads as well as an increase for tensile loads, figure 2a). Furthermore, aleatoric uncertainty from the manufacturing and assembly of the piezo-elastic support as well as from the variation of shunt components and transducer parameters for RL- and RLC- shunts is quantified and investigated using a Monte-Carlo simulation, figure 2b). For both, experimental and numerical investigations, the RLC-shunt achieves a higher vibration attenuation with lower deviation at the same time.

For the two-dimensional beam truss structure in figure 3, numerical investigations showed a successful vibration attenuation with piezoelectric shunt damping if data uncertainty is present. The achieved vibration attenuation with RL- and RLC-shunt as well as the corresponding uncertainty is quantified and compared for variable static loads. A lower uncertainty and at the same time higher vibration attenuation is observed when using the RLC-shunt. The current work is focused on experimental validation of those results and (non-)robust optimization to determine the number and positions of the piezo-elastic support as well as to tune the shunt parameters.

Furthermore, load-bearing structures in mechanical engineering applications typically face the challenge of withstanding and transmitting external loads. In most cases, the load path through the load-bearing structure is predetermined by the design. However, if parts of the load-bearing structure become weak or suffer damage, e.g. due to deterioration or overload, the load capacity becomes uncertain. In sub-project C7b, the semi-active load redistribution to bypass a portion of the loading away from damaged parts of the structure is used in order to prevent the structure from failure or malfunction.

The structure to investigate semi-active load redistribution, see Figure 8, is based on a load-bearing structure developed within the SFB 805 and consists of a translational moving mass connected to a beam by a spring damper and two newly developed semi-active augmented guidance elements for load redistribution, see Figure 9. The stiffness characteristic of the beam’s supports can be adjusted to simulate structural damage. In turn, the structural damage causes i. a. misalignment of the beam, which is defined as malfunction. The proposed semi-active load redistribution provides a technological possibility to influence the load path during operation via augmenting already existing parts of the load-bearing structure with actuators.

For accurate numerical predictions of the load redistribution capability, an adequate mathematical model is indispensable. Therefore, the credibility of the load-bearing structure’s mathematical model predictions is evaluated and increased methodologically by model parameter uncertainty quantification and reduction. A Bayesian inference based calibration procedure is applied to reduce 6 and simultaneously quantify the model parameter uncertainty, Figure 10. Thus, the model is adjusted to the present conditions and the model prediction accuracy is increased.

Comparing the passive and semi-active load-bearing structure, the malfunction is reduced by up to 52% numerically and by 45% experimentally. The semi-active guidance elements achieve a redistribute of the load between the two supports of the beam. The uncertainty of the model prediction is taken into account by Monte Carlo simulations and visualized via uncertainty areas, Figure 11. The results of sub-project C7b contribute to the methodological parameter uncertainty quantification and reduction as well as the technological application of semi-active load redistribution.