Wind Turbine Structures Research Articles

Evaluation of the fatigue resistance of butt-welded joints in towers of wind turbines — a comparison of experimental studies with small scale and component tests as well as numerical based approaches with local concepts

Wind turbines must endure a high number of load cycles during their service lifetime. Therefore, the fatigue strength verification plays an important role. Butt-welded joints are one of the most common structural details in the tower structure of wind turbines. In general, the nominal stress method is used for their fatigue verification. The Eurocode 3 part 1–9 is the current design standard for this field of application and defines the fatigue resistance by pre-defined FAT-classes. This paper presents recent results of fatigue tests on small-scaled specimens and large components with transverse butt welds to discuss the validity of the FAT-class. In addition, results from numerical simulations for the verification with the effective notch stress and the crack propagation approach are used for comparison. For this purpose, macroscopic measurements of the weld seam geometry and a 3D scan were used for a realistic consideration of the notch effect in the simulation. Based on the consistency between the numerical results and the fatigue tests, the influence of the seam geometry on the fatigue resistance was worked out. Furthermore, a prediction of the fatigue strength of butt-welded joints with plate thicknesses up to 80 mm was carried out.

A multisensory approach to understanding bat responses to wind energy developments

Abstract Millions of bats are killed at wind energy facilities worldwide, yet the behavioural mechanisms underlying why bats are vulnerable to wind turbines remain unclear. Anthropogenic stimuli that alter perceptions of the environment, known as sensory pollution, could create ecological traps and cause bat mortality at wind farms. We review the sensory abilities of bats to evaluate potential stimuli associated with wind farms and examine the role of spatial scale on the perceptual mechanisms of sensory pollutants associated with wind energy facilities. Audition, vision, somatosensation and olfaction are sensory modalities that bats use to perceive their environment, including wind farms and turbine structures, but they will not all be useful at the same spatial scales. Bats most likely use vision to perceive wind farms on the landscape, and obstruction lighting may be the first sensory cue to attract bats to wind farms from kilometres away. Research that assesses the risks posed by specific sensory pollutants, when conducted at the appropriate scale, can help identify solutions to reduce bat mortality, such as determining the attractiveness of obstruction lighting to bats at a landscape scale.

Compressive behavior of CFDST columns: Effects of thin-walled inner steel tubes

Concrete-filled double skin steel tubular columns with large hollow ratios (LHR-CFDST), as quintessential elements within offshore wind turbine structures, have received significant attention in contemporary times. As the scale of this type of structure continues to expand, an effective strategy to reduce steel consumption involves elevating the diameter-to-thickness (D/t) ratios of the steel tubes. Presently, there is a dearth of studies that explore the performance of LHR-CFDST columns in the case of thin-walled inner steel tubes. Four LHR-CFDST columns with varying D/t ratios of the inner tubes and one double-skin hollow steel tubular column were produced and tested under axial compression. The study encompassed an investigation into failure modes, local buckling behaviors, and ductility characteristics. The local buckling performance index of specimens with varying D/t ratios differed by up to 16.9%. Furthermore, the verified finite element model was subsequently employed for mechanistic analyses pertaining to the sandwiched concrete's stress distributions, and so on. When the peak loads were attained, the maximum variation in the stress values of the concrete of the specimens with different D/t ratios reached 50.4%. Subsequently, a multi-parameter impact analysis was applied to give a reasonable limit on the D/t ratio (225) of the inner steel tube for engineering design. Finally, the prevailing ultimate strength formulas outlined in established specifications were adapted by integrating the characteristics uncovered through this investigation.

Comparison of stiffness degradation in fatigue-loaded concrete cylinders and large-scale beams

Wind and wave loads subject the tower structures of wind turbines to bending stress. The resulting initial linear strain gradients in the tower cross-section cause fatigue-induced degradation of the concrete at the outer edge of the structures. As the number of load cycles increases, stresses are gradually redistributed to the inner cross-section of the structure consequently. Most of the test results documented in the literature were obtained from small-scale concrete specimens or small-scale components. In those cases, the stiffness and stress redistributions occurring in the real component can only be insufficiently derived, and possible scale effects cannot be taken into account. Besides, available test results for individual component tests are mostly only for load cycles up to 2⋅106. However, significantly lower load levels and thus higher number of load cycles are expected to occur in wind turbines compared to usual fatigue tests. To deal with this issue, extensive fatigue tests on small cylindrical specimens as well as large-scale beams in the very-high-cycle fatigue range are carried out in this work. The test results of both cylindrical and beam specimens show the typical three-phase stiffness curves known from the literature. These curves exhibit a disproportionate decrease at the beginning of the loading, followed by a constant decrease in stiffness, and finally, a greater decrease in stiffness shortly before failure. Moreover, the stiffness curves from the tests on both specimen types show good agreement. It can be concluded that the results from small cylindrical specimen tests can be transferred to large-scale structural components.

Application of advanced signal processing techniques in vibration analysis and fault diagnosis of multi-dimensional anthropomorphic wind turbines

Abstract Wind turbine operating conditions are complex. To ensure the turbine’s safe operation, it is essential to carry out condition monitoring and fault diagnosis of its vibration. In this paper, from the structure of wind turbines, fault types, and fault formation mechanisms, a wind turbine vibration condition monitoring system is established by designing different vibration condition monitoring sensors and combining them with the Internet of Things technology. The discrete Fourier transform is employed to preprocess the time-frequency data before extracting the specific features of the vibration signal by combining the Hilbert-Huang transform after obtaining the wind turbine vibration signal. The SC-TSFN model with spatio-temporal deep fusion is established to realize the fault diagnosis of wind turbines by combining the replaceable null convolution module, BiLSTM module and the self-attention mechanism. It has been found that when the tertiary meshing frequency fluctuates around 506.98 Hz at a fault characteristic frequency of 16.14 Hz, it indicates a fault in the tertiary high-speed shaft gear. The SC-TSFN model has a fault identification time of approximately 52 days before the actual fault downtime, and the model has a 92.05% accuracy rate for wind turbine fault identification. Relying on the signal processing technology to carry out the wind turbine vibration signal analysis and then input it into the fault identification model can realize the accurate identification of the fault state of the unit and provide technical support for the stable operation of wind turbines.

Detection and monitoring of the fatigue crack growth on welds – Application-oriented use of NDT methods

Early detection of fatigue cracks and accurate measurements of the crack growth play an important role in the maintenance and repair strategies of steel structures exposed to cyclic loads during their service life. Observation of welded connections is especially of high relevance due to their higher susceptibility to fatigue damage. The aim of this contribution was to monitor fatigue crack growth in thick welded specimens during fatigue tests as holistically as possible, by implementing multiple NDT methods simultaneously in order to record the crack initiation and propagation until the final fracture. In addition to well-known methods such as strain gauges, thermography, and ultrasound, the crack luminescence method developed at the Bundesanstalt für Materialforschung und -prüfung (BAM), which makes cracks on the surface particularly visible, was also used. For data acquisition, a first data fusion concept was developed and applied in order to synchronize the data of the different methods and to evaluate them to a large extent automatically. The resulting database can ultimately also be used to access, view, and analyze the experimental data for various NDT methods. During the conducted fatigue tests, the simultaneous measurements of the same cracking process enabled a comprehensive comparison of the methods, highlighting their individual strengths and limitations. More importantly, they showed how a synergetic combination of different NDT methods can be beneficial for implementation in large-scale fatigue testing but also in monitoring and inspection programs of in-service structures - such as the support structures of offshore wind turbines.

Artificial Intelligence-Powered Digital Twins for Sustainable and Resilient Engineering Structures/KI gestützte digitale Zwillinge für nachhaltige und widerstandsfähige technische Bauwerke

Artificial Intelligence (AI) is now playing a crucial role not only in everyday life, evidenced by the booming application of Large Language Models (LLMs) such as the Generative Pretrained Transformer (GPT), but also in its potential to transform traditional industries like civil engineering. This work examines the application of novel AI tools to enable Digital Twins (DT) for engineering structures, providing a comprehensive solution for the life-cycle management. A comprehensive state-of-the-art review is conducted to explore existing advancements in sensing, inspection, and simulation that are fundamental to the development of digital twins. Building on this knowledge, a framework is proposed to define DT for Engineering (DT4ENG) based on their emphasis and data flow, including forward DT, backward DT, and DT-informed decision making. Following this, a case study on floating offshore wind turbine (FOWT) structures demonstrates the application of DT4ENG in a specific domain, with findings that have broader implications for the life-cycle management of engineering structures. The present study reveals that the AI enables digital twins to effectively identify potential structural issues, predict deterioration, and suggest timely maintenance interventions. This approach enhances the accuracy of structural health assessments, optimises resource allocation, and minimises downtime. By translating the capabilities of digital twins into actionable strategies, the research highlights their potential to significantly improve the life-cycle management of engineering infrastructure. In general, these advancements promise a new era of intelligent maintenance strategies, offering increased safety, extended service life, and cost-effectiveness. The proposed DT4ENG is set to become a standard in the traditional industry, driving a shift towards more sustainable, resilient, adaptive, and intelligent structures.

Structural Damage Classification in Offshore Structures Under Environmental Variations and Measured Noises Using Linear Discrimination Analysis

Changing environmental conditions and measured noises often affect the dynamic responses of structures and can obscure subtle changes in the vibration characteristics caused by damage. To address this issue, a new method for classifying damage in offshore structures under varying environmental conditions and measured noises is proposed using linear discrimination analysis (LDA). Two sets of data on dynamic characteristics, one from healthy structures and the other from unknown testing structures, are used to determine the optimal projection vector. This vector is perpendicular to the discriminant hyperplane and is used for damage classification. The damage‐sensitive features are extracted by projecting both sets of data onto this vector. These features are then used with the hypothesis test technique to determine the condition state of the testing structure. Numerical studies on offshore wind turbine structures and experimental validations of a deep‐sea mining system are being conducted to evaluate the effectiveness of the proposed approach. The study also examines the impact of mode combinations, measured noises and samples on the performance of the approach. The results indicate that the proposed approach can accurately assess the structural health state even in the presence of environmental changes and noise contamination, even with limited samples. The promising performance of the approach will facilitate the establishment of an online structural monitoring system to ensure the safety of offshore structures.

Impact toughness analysis of offshore wind power structures under the influence of long-period waves

Abstract In this paper, the performance of offshore wind turbine structures under long-period wave impacts is investigated, and a numerical model of long-period waves is developed to simulate the wave motion and fluid seepage in the pore medium by using the VARANS equation with the OlaFlow solver, and various turbulence models such as the model, RNG model, and the VOF method is applied to capture free surfaces, which can accurately simulate wave generation, propagation, reflection, breaking, and fluid seepage in the pore medium. These methods can accurately simulate the wave generation, propagation, reflection, breaking, and fluid seepage in the pore medium, and the accuracy of the numerical simulation is verified by comparing the results with those of the physical experiment. The results show that the wind farm exhibits good impact toughness under the influence of long period waves, and its overturning stability and slip stability are better than the safety coefficient required by the specification.

Damage Detection and Localization at the Jacket Support of an Offshore Wind Turbine Using Transformer Models

Early detection of damage in the support structure (submerged part) of an offshore wind turbine is crucial as it can help to prevent emergency shutdowns and extend the lifespan of the turbine. To this end, a promising proof-of-concept is stated, based on a transformer network, for the detection and localization of damage at the jacket-type support of an offshore wind turbine. To the best of the authors’ knowledge, this is the first time transformer-based models have been used for offshore wind turbine structural health monitoring. The proposed strategy employs a transformer-based framework for learning multivariate time series representation. The framework is based on the transformer architecture, which is a neural network architecture that has been shown to be highly effective for natural language processing tasks. A down-scaled laboratory model of an offshore wind turbine that simulates the different regions of operation of the wind turbine is employed to develop and validate the proposed methodology. The vibration signals collected from 8 accelerometers are used to analyze the dynamic behavior of the structure. The results obtained show a significant improvement compared to other approaches previously proposed in the literature. In particular, the stated methodology achieves an accuracy of 99.96% with an average training time of only 6.13 minutes due to the high parallelizability of the transformer network. In fact, as it is computationally highly efficient, it has the potential to be a useful tool for implementation in real-time monitoring systems.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Proposal and application of onshore lattice wind turbine support structure and integrated multi-scale fatigue assessment method

To accommodate the development of wind turbines in areas with weak wind speeds, an upright ultra-high lattice support structure is proposed for the large wind turbine, which is prefabricated and assembled mainly from thin-walled circular hollow steel (CHS) tube elements. As a practical example, a 180 m wind turbine support structure is presented. To evaluate the fatigue performance of the new structure type with modular assembly, a new integrated multi-scale fatigue assessment method is proposed to address the shortcomings of the equivalent fatigue load method commonly used in engineering design. For any fatigue load condition, the integrated multi-scale fatigue assessment method enables accurate and efficient calculation of the relationship curve between fatigue load and hot spot stress, and identification of the locations in the structure where fatigue accumulation damage is the greatest. Furthermore, fatigue damage accumulation and fatigue life can also be predicted accurately and efficiently by the proposed method. The results show that for the maximum fatigue cumulative damage of the proposed new structural systems, as the effect of average stress is not considered and the assumption of superposition of principal stresses, the traditional equivalent fatigue load method is too conservative compared to the proposed integrated multi-scale fatigue assessment method, detrimental to cost decreasing and benefit increasing of the wind turbine structures.

Structural Behavior of a PIP Slip Joint under Pure Bending Considering Nonlinear Buckling

This study investigates the structural behavior of a particular mechanical joint subjected to bending and perturbation and the selection of overlapping lengths for this CHS structure arrangement. We began this research by meticulously validating the methodology through a rigorous replication of a prior experimental study, establishing its reliability as a solid foundation. Subsequently, the process was applied to pile-in-pile (PIP) slip joints with varying overlapping lengths. The primary aim was to determine the optimal overlapping length, a critical parameter in this analysis, encompassing the evaluation of stiffness, bending capacities, and joint efficacy. These investigations reveal a clear correlation between increasing overlapping length and heightened joint stiffness. An optimal overlapping length that strikes a harmonious balance between stiffness, bending capacity, and joint efficiency was identified. These findings hold substantial promise for enhancing the joint design of tubular sections, particularly within the context of wind-turbine structures. Using this novel joint can be promising in increasing the efficiency and reliability of CHS structures in future construction and performance.

Fatigue tests of multiplanar concrete-filled steel tubular KK-joints with inner ring stiffeners

Wind turbine structures are subjected to prolonged cyclic loading, with fatigue issues being particularly prominent. This study aims to investigate the fatigue performance of multiplanar concrete-filled steel tubular (CFST) KK-joints with inner ring stiffeners. Fatigue tests were conducted on these joints, and comparison specimens were designed, including one with a circular hollow section (CHS) tubular joint and another with a CFST joint. The results indicated that the incorporation of inner concrete and ring stiffeners led to a significant reduction in the hot spot stress within tubular joints. The maximum hot spot stress concentration factor (hot-spot SCF) of CFST KK-joints with inner ring stiffeners decreased by 39.7% compared to CHS KK-joints and by 8.7% compared to CFST KK-joints. Furthermore, an increase in the brace thickness-to-chord thickness ratio τ from 0.94 to 1.24 for CFST KK-joints with inner ring stiffeners resulted in a corresponding 74.3% increase in hot-spot SCF. Additionally, fatigue tests were conducted to ascertain the failure modes, crack propagation patterns, and fatigue lives of specimens. These measured fatigue lives were compared with the S–N curves given by different codes. Notably, it was observed that the curve proposed by DNV/CCS exhibited the most suitable correlation with the experimental results.

Fatigue constrained topology optimization for the jacket support structure of offshore wind turbine under the dynamic load

This paper proposes a topology optimization method considering fatigue constraints for the jacket support structure of offshore wind turbine. Jacket support structure is an important component and supports the whole wind turbine weight under the dynamic environmental loads, which would induce serious fatigue problems. To obtain the optimal structural layout, the topology optimization method considering fatigue constrained is introduced for the jacket support structure under the dynamic load. The fatigue lifetime is constrained base on the Miner's cumulative damage rule. The design sensitivity of the objective and constraint functions are evaluated analytically, while the equivalent static load approach is applied. Some typical topology optimization problems are introduced to verify the effectiveness of the proposed method, and this method can obtain the appropriate layouts with stable convergence. A case study on the OC4 reference jacket indicates that the method can achieve a reasonable layout structure under the dynamic load, which meets the fatigue constraint. And the final design is reconstructed based on the topology optimization layout. Finally, a lighter jacket structure has good performance on the ultimate bearing capacity, eigenfrequency, and buckling, which is suited for long operation life.

Non-destructive detection of high-strength wind turbine bolt looseness using digital image correlation

Looseness of high-strength wind turbine bolts is one of the main types of mechanical failure that threaten the quality and safety of wind turbines, and how to non-destructively detect bolt loosening is essential to accurate assessment of operational reliability of wind turbine structures. Therefore, to address the issue of looseness detection of high-strength wind turbine bolts, this paper proposes a non-destructive detection method based on digital image correlation (DIC). Firstly, the mathematical relationships between the inplane displacement component of the bolt’s nut surface, the bolt’s preload force loss and the bolt loosening angle are both deduced theoretically. Then, experimental measurements are respectively conducted with DIC with different small bolt loosening angles. The results show that the bolt loosening angle detection method based on DIC has a detection accuracy of over 95%, and the bolt’s preload force loss evaluated by the deduced relationship has a good agreement with the empirical value. Therefore, the proposed DIC-based bolt loosening angle detection method can meet the requirements of engineering inspection, and can achieve quantitative assessment of preload forces loss of wind turbine bolt.

Numerical analysis of airfoils used in an omni-directional-guide vane structure of vertical axis wind turbine for high-rise buildings

This document introduces a novel concept involving an Omni-Directional Guided Vane (ODGV) encompassing a vertical axis wind turbine (VAWT) with the goal of improving its overall performance. Extensive three-dimensional computational analysis of the airfoils used in this novel ODGV structure is conducted to investigate the impact of various geometric parameters. Diverse geometric configurations of the ODGV are explored to analyze wind flow behavior across the turbine utilizing a well-validated computational fluid dynamics (CFD) model. The numerical investigations employ the Reynolds Averaged Navier–Stokes (RANS) modeling approach with the k-epsilon turbulence model. The steady state governing equations are solved using the validated CFD solver STAR CCM+. The study considers three distinct inlet velocities: 3, 6, and 9[Formula: see text]m/s, with the aim of improving flow behavior and velocity through the ODGV. Four different modifications of the ODGV are examined, and the accuracy of the CFD model is affirmed through comparison with NACA airfoil data. Integration of the ODGV results in an enhanced self-starting behavior of the VAWT, leading to a reduction in the cut-in speed. Validation results demonstrate a strong agreement with the data obtained from CFD simulations. These results suggest that most shape ratios, except for 0.3 and 0.4 at Tip Speed Ratio (TSR) of 1.3 and 3, contribute to enhancing power and torque coefficients. Furthermore, the findings indicate that with a Sharpe ratio of 0.56, both torque and power coefficients could be improved by up to 48% and 58%, respectively.

Material aspects of wooden towers for offshore wind turbines

Possible new innovative materials for Counter Rotating Axis Floating Tilted Turbines are studied and discussed. The 40 MW version of the Counter Rotating Axis Floating Tilted Turbine (CRAFTT) will reach as far as 80 m below sea surface and up to 400 m above. The CRAFTT is an integrated design for floating offshore wind with two turbines on the same tilted shaft where the lower turbine is mounted directly on a rotating mast integrated with floater. The upper turbine will reach altitudes of 400 m. The system is designed to be a direct drive system, eliminating need for gearbox, taking advantage of the double air-gap speed of generator. With the generator placed at lower end as ballast the incentive to reduce weight for wings, tower and blades increase. Furthermore, wood is an attractive option as it enables both low CO2 impact production and higher degree of reusability. However, fatigue properties from both mechanical and thermal cycling needs to be addressed in order to evaluate new structural materials in the context of floating wind turbines. Starting from scratch without any preconceived notions, one could consider timber as a potential option for the tower. In such a preliminary and qualitative deliberation, one can consider that the use of wood as the main load-carrying material in large structures has been proven during the last decade by the development of new high-rise wooden buildings, with even higher buildings with timber as the main structural component expected in the future, The tall wooden buildings have been made possible since wood has the advantage of having high specific mechanical properties, i.e. high strength and stiffness with respect to density in the grain direction, in addition to being renewable. Another advantage is that wood is less sensitive to fatigue than many metallic materials, since its hierarchical microstructure prevents the propagation dominant cracks when loaded in the longitudinal direction. Design against fatigue is crucial in wind turbine structures given the inevitable cyclic loading. As all materials, wood certainly has its drawbacks, the foremost being its sensitivity to moisture, which is of obvious concern in off-shore applications. Moisture has a softening effect, resulting in creep, and moisture may trigger chemical or microbial degradation. The development of barrier coatings of aluminium has shown to be very efficient in e.g. high-voltage cables and food packaging, making them impermeable to moisture and air. Such techniques should be applicable also in wood constructions. This presentation highlights the main points specific for wood as a construction material in the design of wood towers for wind turbines in offshore locations, which need to be addressed in design.

Stretch broken carbon fiber for primary wind turbine structure

Stretch broken carbon fiber (SBCF) composites offer significant benefits over conventional continuous fiber composites. These include the greatly enhanced formability of unidirectional tape form products prior to cure without any compromise on the mechanical performance after curing. The recent work performed under the US Army funded Stretch Broken Carbon Fiber for Primary Aircraft Structure program by Montana State University (MSU) is reviewed, along with the results achieved by previous efforts to develop this technology. The achievement of shorter stretch break lengths to improve formability is a principal objective of the work and a successful achievement of the recent efforts. In addition, alternate means of manufacturing SBCF prepregs are discussed, including conventional spooled fiber presentation suitable for running on current prepregging equipment, as well as a newly developed direct to prepreg approach, where the stretch breaking of the fibers is done in-line with the prepregging process. While aerospace primary structure has been the focus of current research efforts, applications in other industries such as wind turbine blades are also obvious areas of applications for SBCF technology.

Optimal vibration sensor placement for jacket support structures of offshore wind turbines based on value of information analysis

Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures.