Eliz-Mari Lourens

Operational Vibration-Based Response Estimation for Offshore Wind Lattice Structures

The design for fatigue for offshore wind turbine structures is characterized by uncertainty, resulting from both loading specifications and numerical modelling. At the same time, fatigue is a main design driver for this type of structures. This study presents a strategy to monitor the accumulated fatigue damage in real-time, employing a joint input-state estimation algorithm. Measuring the operational vibrations at well-chosen locations enables the estimation of strain responses at unmeasured locations. The estimation algorithm is applied to a wind turbine on a lattice support structure, for which the response estimates of the lattice members are based on measurements on the turbine tower only. This restriction follows from the difficulty to reliably and robustly measure at locations on the lattice structure. Artificial measurement data is generated with a full-order finite element model, while the strains are estimated with an erroneous reduced-order design model, after inclusion of measurement noise. The strain estimates show that the main frequency content can be captured relatively accurately, except for a small bias and some high frequency disturbance, corresponding to a weakly observable higher mode. This second aspect shows the importance of a trade-off between the accuracy of the reduced-order finite element model and the ill-conditioning of the observability matrix.

Model-Based Estimation of Hydrodynamic Forces on the Bergsoysund Bridge

Knowledge of excitation loads on bridges are important for reliable design. Load models are however prone to uncertainties. Force identification using dynamic response measured on full-scale structures can be used to reduce the uncertainty. In this contribution, numerical simulations are performed to examine the feasibility of force identification on the floating pontoon Bergsoysund Bridge. We present a practical case study in which wave excitation forces and motion induced forces are estimated using only acceleration output. The sensor network considered represents the monitoring system currently installed on the bridge. A reduced order model with 26 modes is used to represent the structure in the identification. Wave force time series are generated by Monte Carlo simulations, and the acceleration response is obtained from a frequency domain solution of the equations of motion. The generated acceleration data is polluted with noise and subsequently used for identification. The results show that a joint input-state estimation algorithm is able to adequately identify a subset of hydrodynamic forces acting on the pontoons in the presence of both measurement and model errors. The translational forces are identified with a larger accuracy than the moments. Lastly, considerations and improvements for an analysis with experimental field data are presented.

General Conditions for Full-Field Response Monitoring in Structural Systems Driven by a Set of Identified Equivalent Forces

Kalman-type filters are being increasingly used to estimate the full-field dynamic response of structures from a limited set of vibration measurements. Various coupled input-state and coupled input-state-parameter estimation algorithms have been developed in this context, ranging from an initial formulation for use with linear reduced-order structural systems to alternative filters for dealing with acceleration-only data and recently also nonlinear model descriptions. The use of these algorithms allows for response prediction to be performed in the absence of any knowledge of the excitation forces, where often a set of response-driving equivalent forces is identified from the measurements. Up to now, the success of response estimation based on the identification of equivalent forces has been related only to whether these forces satisfy the so-called controllability requirements. In this contribution, controllability is shown to be an insufficient criterion for guaranteeing the accuracy of response estimates based on equivalent loading. Instead, the need for a new criterion is advocated, which would allow for a proper assessment of the applicability of equivalent force based monitoring to various engineering problems. Concepts are illustrated using simple numerical examples where a comparison is made between the true and assumed noise statistics and the response prediction accuracy for a number of distinct cases. These include situations where (a) the applied and equivalent loads are concentrated and collocated, (b) the applied and equivalent loads are concentrated and non-collocated, and (c) modal equivalent forces are used. Results are applicable to any Kalman-type coupled input-state estimator derived using the principles of minimum-variance unbiased estimation.

Verification of joint input-state estimation by means of a full scale experiment on a footbridge

This paper presents a verification of a state-of-the-art joint input-state estimation algorithm using data obtained from in situ experiments on a footbridge. A dynamic model of the footbridge is based on a detailed finite element model that is calibrated using a set of experimental modal characteristics. The joint input-state estimation algorithm is used for the identification of two impact, harmonic, and swept sine forces applied to the bridge deck. In addition to these forces, unknown stochastic forces, such as wind loads, are acting on the structure. These forces, as well as measurement errors, give rise to uncertainty in the estimated forces and system states. Quantification of the uncertainty requires determination of the power spectral density of the unknown stochastic excitation, which is identified from the structural response under ambient loading. The verification involves comparing the estimated forces with the actual, measured forces. Although a good overall agreement is obtained between the estimated and measured forces, modeling errors prohibit a proper distinction between multiple forces applied to the structure for the case of harmonic and swept sine excitation.