Abstract
The rapid growth of offshore wind energy has driven the need for advanced foundation designs
to support larger turbines in challenging marine environments. This study evaluates the performance of two
key shallow foundation types for offshore wind turbines—gravity-based foundations (GBFs) and monopod
suction buckets (MSBs)—using finite element analysis (FEA) in ABAQUS. Conducted at a site in the
Dorood Oil Field in the Persian Gulf, the analysis compares soil stress, foundation settlement, lateral
displacement, and rotation under gravitational and environmental loads. Eight GBF configurations with
varying height-to-diameter ratios and ten MSB configurations with different skirt length to diameter ratios
were examined. Results show that MSB foundations generally exhibit lower settlement and comparable
lateral stability compared to GBFs, particularly for larger configurations, due to effective load transfer to
deeper soil layers. However, GBFs demonstrate lower rotation angles at higher h/D ratios. Optimal
configurations, GBF-1 and MSB-1, were identified as balanced designs offering reliable performance. These
findings provide valuable insights for optimizing foundation design in offshore wind turbine projects,
emphasizing the critical role of foundation geometry and soil-structure interaction.
Key Words
finite element analysis; gravity-based foundations; marine Geotechnics; monopod
suction buckets; offshore foundations; offshore wind turbines
Address
Soheyl Hosseinzadeh and Behrouz Gatmiri: School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract
Buoys are a crucial structure used offshore, and the data acquired from them is essential for
marine navigation, offshore engineering, coastal management, weather forecasting and wave energy
research. To optimize wave energy extraction and guarantee the dependability of ocean-based structures,
accurate heave displacement forecasting is essential. In order to enhance the estimation of heave
displacement, a unique hybrid model that combines a Long Short-Term Memory (LSTM) network with the
Frequency Enhanced Decomposition Transformer (FEDformer) is implemented. The proposed FEDformer
– LSTM hybrid model competently captures long-range dependencies and non-linear temporal patterns in
wave data by employing the frequency-domain decomposition powers of FEDformer and the temporal
learning advantages of LSTM. Experimental data are retrieved from the buoy data of the National Institute
of Ocean Technology (NIOT), which includes wave height, wind speed, and other data from key maritime
areas. The hybrid model beats state-of-the-art forcasting algorithms and independent deep-learning
techniques in terms of correlation metrics, Mean Absolute Error (MAE) and Root Meam Square Error
(RMSE), affording to proportional tests carried out using real-world buoy datasets. The findings indicate that
the FEDformer-LSTM model is more appropriate prediction model for the proposed application.
Key Words
deep learning; FEDformer; ocean-based structures; wave energy conversion
Address
N. Santhosh: Department of Mechanical Engineering, Easwari Engineering College, Chennai, India
.M. Vinu Kumar: Department of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore, India
R. Sundar: Scientist – E, Ocean Observation Systems, National Institute of Ocean Technology, Chennai, India
V. Vadivelvivek: Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IndiaC. Dineshbabu: Department of Mechanical Engineering, Kongunadu College of Engineering and Technology, Trichy, India
Abstract
Through-the-thickness stress distribution in a tubular member has a profound effect on the
fatigue behavior of tubular joints commonly found in steel offshore structures. Such stress distribution can
be characterized by the degree of bending (DoB). Although tubular T-joints with concrete-filled chords are
commonly used in offshore tubular structures and the concrete fill can have a significant effect on the DoB
values at the brace-to-chord intersection, no investigation has been reported on the DoB in tubular T-joints
with concrete-filled chords due to the complexity of the problem and high cost involved. In the present
research, data extracted from 162 stress analyses conducted on 81 finite element (FE) models subjected to
brace tension and compression, verified based on available experimental data and parametric equations, was
used to study the effects of geometrical parameters on the DoB values in tubular T-joints with concrete-filled
chords. Parametric FE study was followed by a set of nonlinear regression analyses to develop four new
DoB parametric equations for the fatigue analysis and design of axially loaded tubular T-joints with
concrete-filled chords.
Key Words
degree of bending (DoB); fatigue; offshore jacket structure; tubular T-joint with
concrete-filled chord
Address
Hamid Ahmadi: National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College (AMC),
University of Tasmania, Launceston, TAS 7248, Australia
Mahdi Ghorbani: Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran
Abstract
The influence of the second and third order wave loads on the TLP FOWT responses are
investigated in this study. The second order wave loads are calculated by commercial wave
diffraction/radiation analysis software tool. The third order wave loads calculation methods are developed in
this study based on the well-known FNV formulation. The third order wave force is applied on the platform
in dynamic simulation models as external force. It is observed that the third order wave force is cubic
proportional to the wave heights. The sum frequency third order wave force has periods about 1/3 of the first
order wave periods. With the same wave heights, the third order wave forces on the surface piercing
structure are higher in shallower waters. The 2nd order wave loads have higher influences on the FOWT
responses than the 3rd order wave loads in both fatigue and extreme load cases. The 3rd order wave force
influence is negligible in fatigue load cases. The high order wave loads have more impact on the tower and
mooring system responses than platform motion. On a TLP FOWT, the tower and mooring system usually
feature high frequency resonance susceptible to excitation from sum frequency 2nd and 3rd order wave
loads.
Key Words
FNV formulation; high order hydrodynamic load; TLP FOWT
Address
Chunhui Song, Shenglei Fu, Yuming Zhang and
Yunlong Su: CNOOC, LTD, China
Jim Li and Tuanjie Liu: OffshoreTech China, LLC, China
Abstract
Wind turbines often exhibit component failures before completion of their typical 20-year
design life. Unexpected part failures increase the associated costs of their Operations & Maintenance
(O&M). In turn, this raises the associated Levelized Cost of Electricity (LCOE), making this method of
power generation less competitive compared to traditional methods. One of the components of particular
concern is the slew or "pitch" bearing connecting the root of the blades to the rotor hub. The Drivetrain
Reliability Collaborative (DRC) of the National Renewable Energy Lab (NREL) has begun investigations
into pitch bearing reliability. One outcome of this is a collection campaign on the 1.5MW Wind Turbine at
the NREL Flatirons Campus to observe variations in pitch bearing strains during real operation. This
entailed outfitting the turbine with additional instrumentation such as strain gauges in the rotor hub. The
present study intends to extend the applicability of the DRC1.5 field tests by relating the strain signals to
standard operational output. Machine Learning (ML) techniques include supervised learning by Artificial
Neural Networks (ANN) and Long-Short-Term Memory (LSTM), as well as Principal Component Analysis
(PCA). The same DRC test data sets were applied to ANN and LSTM and their results are compared.
Discussions of results describe which generalize best for the purpose of sensor reduction, and which of the
operational signals are most indicative of bearing strain. The results showed that both ANN and LSTM
predicted future (or nonfunctional) sensor signals well with slightly higher accuracy by LSTM. Post
processing of time series predictions can then track the progression of fatigue damage without additional
sensors. Examining the prediction results details the model performance and highlights the relevance for
wind turbine condition monitoring. Incorporation of learning techniques is presented as a systematic
approach that can be replicated to simplify and optimize real monitoring strategies.
