Investigation of a slip joint connection between the monopile and tower of an offshore wind turbine (poster)

Hydraulic EngineeringCivil Engineering and Geoscience

On the use of equivalent forces for structural health monitoring based on joint input-state estimation algorithms

For the monitoring of large structures where the loading is typically characterized by large uncertainties in temporal and/or spatial evolution, algorithms capable of estimating a set of response driving equivalent forces are strongly desired. In this context, several Kalman-type coupled input-state and coupled input-state-parameter filters have recently been developed, allowing for an estimation of the full-field dynamic response of a structure from only a limited number of vibration 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 to assess the applicability of equivalent force based monitoring to various engineering problems. Concepts are illustrated by comparing true and assumed noise statistics as well as the response prediction accuracy for different numerical examples, 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.Offshore Engineerin

Investigation of a slip joint connection between the monopile and tower of an offshore wind turbine

To circumvent current industry problems related to the settling of grouted connections, a steel-to-steel or slip joint connection is proposed for fitting a transition piece onto an installed monopile foundation. In the first part of this contribution, a simplified dynamic analysis of the installation of such a joint is considered. Assuming velocity dependent Coulomb friction, slip-stick equations of motion are derived for a simplified 1-D model of the joint, and the slip distance under self weight and resulting overlap lengths are calculated for different initial cone angles and friction coefficients. It is concluded that even for small initial cone angles, small angle differences between the top and bottom cone, and low friction coefficients, the tangential displacement caused by the self weight is insufficient to reach the desired contact overlap. In the second part of this contribution, the static capacities (axial and bending) of the joint in the in-place situation are determined by means of a FE model. Contact analyses are performed based on the ideal situation in which the two parts of the joint is initially in full contact. Of the parameters varied in these analyses, small cone angles and large overlaps are identified as most conducive to a succesfull transferral of the loads from the transition piece to the monopile. Given the uncertainty on the friction coefficient, it is then also recommended to use a cone angle of 1? and preferably an overlap > 1.5D.Hydraulic EngineeringCivil Engineering and Geoscience

A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response

Quasi-periodic loading resulting from waves and a rotationally sampled wind field often leads to fatiguedriven designs for offshore wind turbine support structures. The uncertainty on wind and wave loading, together with large modelling uncertainties, lead to large discrepancies between the observed and predicted dynamic behaviour of these structures. Among many recent-developed techniques for monitoring of true fatigue damage development, two promising Kalman-type filters are compared, namely the recently proposed Dual Kalman filter (DKF) and the Gillijns and De Moor filter (GDF). The filters are applied to synthetic vibration data in order to predict the global response of a lattice support structure assuming large modelling uncertainties and no knowledge of the input forces. A critical assessment of both filterswith regard to requirements on the available data and tuning of the filter parameters is presented.Offshore Engineerin

Full-field response monitoring in structural systems driven by a set of identified equivalent forces

Kalman-type filters for coupled input-state estimation can be used to estimate the full-field dynamic response of structures from only a limited set of vibration measurements. The use of these coupled estimators allows for response prediction to be performed in the absence of any knowledge of both the dynamic evolution and spatial distribution of the excitation forces, where often a set of response-driving equivalent forces is identified from the measurements. In this contribution, a rigorous analysis of the concept of equivalent force based response monitoring is performed, with the aim to clearly establish its limitations and ranges of applicability. It is shown that, unlike commonly assumed, the success of this type of response monitoring cannot be related solely to whether the chosen set of equivalent forces satisfy the controllability requirements, but will depend on the bandwith of the excitation forces in combination with the extent/characteristics of the sensor network. Arguments are instantiated using simple numerical examples where a comparison is made between the theoretical assumptions used to derive the filters and the physical situation. Included in the analyses are situations where (a) the applied and equivalent loads are concentrated and collocated, (b) the applied and equivalent loads are concentrated and non-collocated, (c) modal equivalent loads are used to represent concentrated non-moving forces, and (d) modal equivalent loads are used to represent concentrated moving forces. Results are applicable to any Kalman-type coupled input-state estimator derived using the principles of minimum-variance unbiased estimation.Offshore Engineerin

Wind load estimation and virtual sensing in long-span suspension bridges using physics-informed Gaussian process latent force models

Wind loading is an essential aspect in the design and assessment of long-span bridges, but it is often not well-known and cannot be measured directly. Most structural health monitoring systems can easily measure structural responses at discrete locations using accelerometers. This data can be combined with reduced-order modal models in Kalman filter-based algorithms for an inverse estimation of wind loads and system states. As a further development, this work investigates the incorporation of Gaussian process latent force models (GP-LFMs), which can characterize the evolution of the wind loading. The Hardanger Bridge, a 1310 m long suspension bridge instrumented with a monitoring system for wind and vibrations, is used as a case study. It is shown how the LFMs can be enriched with physical information about the stochastic wind loads using monitoring anemometer data and aerodynamic coefficients from wind tunnel tests. It is found that the estimates of the modal wind loads and modal states obtained from a Kalman filter and Rauch–Tung–Striebel smoother are stable for acceleration output only, thus avoiding the accumulation of errors. The proposed approach demonstrates how physical or environmental data can be injected as valuable information for global monitoring strategies and virtual sensing in bridges.Dynamics of StructuresOffshore Engineerin

Nonlinear model parameter identification for ice-induced vibrations

The problem of level ice interacting with compliant structures is addressed, where the ice loads can depend on the dynamical behavior of the structures. We are interested in a special type of ice-induced vibration, known as frequency lock-in, and characterized by having the dominant frequency of the ice forces near a natural frequency of the structure. It is shown that accurate estimates of the model parameters for the well-known Määttänen's model for ice-induced vibrations can be obtained from measurements of the structural vibrations and the ice velocity. Määttänen's model uses a state-dependent piecewise nonlinear function for the ice crushing strength, which leads to nonlinear negative damping in the equations of motion of the considered structure. The identification is achieved by means of an Unscented Kalman Filter using simulated noisy measurements of the structural behavior.Offshore EngineeringApplied Mechanic

Vibration-based Identification of Hydrodynamic Loads and System Parameters for Offshore Wind Turbine Support Structures

For reliable structural health monitoring and possible lifetime extension of offshore wind turbine support structures, accurate predictions of the response of these structures at all critical locations are required. Response predictions in offshore wind applications are, however, affected by large uncertainties on environmental (wind/wave/soil) as well as system parameters (eigenfrequencies/damping). As a first step towards robust health monitoring in the presence of these uncertainties, a methodology for simultaneous estimation of a response equivalent hydrodynamic loading and a system parameter from measured vibration signals is proposed. Use is made of a recently proposed coupled input-state-parameter estimation technique based on the Extended Kalman filter. The identification process is driven by a limited set of artificially generated vibration response data in combination with an approximate reduced-order model of the support structure. The results show that the proposed method is capable of tracking both the response equivalent hydrodynamic loading and a parameter that is related to the stiffness of the substructure.Offshore Engineerin

Investigation of dynamic wind loads on a long-span suspension bridge identified from measured acceleration data

The traditional wind load assessment for long-span bridges relies on assumed models for the wind field and aerodynamic coefficients from wind tunnel tests, which usually introduce some uncertainties. Recent studies have shown that large deviations can exist between the predicted and observed wind-induced dynamic response of suspension bridges. In studies of the dynamical behavior of bridges, inverse force identification methods can therefore be an interesting tool in the assessment of possible uncertainties involved in the modeling of wind loads. This paper presents a novel case study of the identification of the dynamic wind loads on the 1310 ​m long Hardanger bridge, a suspension bridge equipped with a monitoring system for wind and vibrations. The modal wind loads are identified from acceleration data using an algorithm for model-based joint input and state estimation. Several data sets with different wind conditions are presented. The wind loads are studied in the time and frequency domains and are compared to the mean velocity and turbulence characteristics of the wind.Offshore EngineeringDynamics of Structure

Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models

Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force model (GPLFM) is a generalized Bayesian virtual sensing technique which combines a physics-driven model of the structure with a data-driven model of latent variables of the system to extrapolate unmeasured strain states. In the GPLFM, unknown sources of excitation are modeled as a Gaussian process (GP) and endowed with a structured covariance relationship with response states, using properties of the GP covariance kernel as well as correlation information supplied by the mechanical model. It is shown that posterior inference of the latent inputs and states is performed by Gaussian process regression of measured accelerations, computed efficiently using Kalman filtering and Rauch–Tung–Striebel smoothing in an augmented state-space model. While the GPLFM has been previously demonstrated in numerical studies to improve upon other virtual sensing techniques in terms of accuracy, robustness, and numerical stability, this work provides one of the first cases of in-situ validation of the GPLFM. The predicted strain response by the GPLFM is compared to subsoil strain data collected from an operating offshore wind turbine in the Westermeerwind Park in the Netherlands. A number of test cases are conducted, where the performance of the GPLFM is evaluated for its sensitivity to varying operational and environmental conditions, to the instrumentation scheme of the turbine, and to the fidelity of the mechanical model. In particular, this paper discusses the capacity of the GPLFM to achieve relatively robust strain predictions under high model uncertainty in the soil-foundation system of the offshore wind turbine by attributing sources of model error to the estimated stochastic input.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Dynamics of StructuresOffshore EngineeringMechanics and Physics of Structure