Seismic Inversion for Estimating Soil Material Damping for Offshore Wind Turbines

Seismic Inversion for Estimating Soil Material Damping for Offshore Wind Turbines

Ravasio, Matteo (TU Delft Civil Engineering and Geosciences)

Metrikine, A. (mentor)
van Dalen, K.N. (graduation committee)
Armstrong, M.A. (graduation committee)
Versteijlen, W.G. (graduation committee)
Verschuur, D.J. (graduation committee)

Delft University of Technology

2018-05-31

Creating accurate engineering models for predicting the response of large structures often requires geotechnical modelling of soil behaviour. Soil material damping is an important input parameter for modelling the structural response and energy dissipation, especially for embedded and lightly damped structures, as in particular for offshore wind turbines. Non-invasive in-situ measurements like the multichannel analysis of surface waves, offer cost-effective solutions and estimations over a wider volume of soil. This research aims to demonstrate that MASW technique can be used to generate a reliable in-situ estimation of the soil damping in marine environments in the depth range of 0-30m.
A unique and high-quality dataset has been made available through a collaboration with the Norwegian Geotechnical Institute (NGI). Shear wave data are rarely available for offshore site investigations and have not been used before for a damping inversion from real surface waves measurements. Moreover, also the horizontal soil response was recorded in this measurements which allow for reducing the uncertainties of the results obtained by using the Scholte wave model. Therefore, two forward waves models are implemented in Matlab to reproduce both the in-line and cross-line measurements.
A reliable shear wave velocity profile is derived via the stiffness inversion method, after which the damping inversion is performed. A modal damping inversion method is developed and it uses a novel modified random search algorithm in order to estimate the material damping profile over depth. The attenuation coefficient is chosen as the reference parameter for the damping inversion and the misfit function for the damping inversion is defined as the normalized difference per frequency of the attenuation curves. The measured attenuation coefficient is extracted based on a modified half-power bandwidth method. The modelled attenuation curve is retrieved as the imaginary part of the complex wavenumber. The wavelet compression technique is employed to reduce the number of roots used in the inversion for both the experimental and the theoretical curves. The identification requires solving an inverse problem with a global optimization method. To get a better understanding of the model and computational time, a combination of sensitivity studies, behaviours of the phase damping ratio curves and layer reduction were performed. Then, synthetic
inversions are run to verify the validity of the proposed technique. Finally, the
method is applied to the aforementioned collected data and two damping profiles (Scholte and Love models) are computed and they show good agreement in terms of both trend and magnitude.
The frequency dependence of the material damping ratio of the soil is analysed
from the use of measured surface wave data. A novel technique is proposed for retrieving this relation starting from the results of the damping inversion and it shows that the material damping ratio is independent from frequency. Then, two new alternative approaches are proposed, which are based on a simplification of the traditional dependency relation. The first confirms the frequency independence of damping while, although it seems very promising, the second one cannot be tested due to computational limitations. The demonstrated frequency independence of the material damping is only valid based on the frequency ranges available in the dataset and cannot be generalized to cover all scenarios.

MASW
Damping Estimation
Attenuation Analysis
Frequency dependence damping
Damping Inversion

http://resolver.tudelft.nl/uuid:72ccaf81-68a6-404e-93f7-e5f1f3a06c73

2021-05-31

Student theses

master thesis

© 2018 Matteo Ravasio