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Abstract This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection.

This paper experimentally examines the influence of hybrid excitation on the performance of vibrational piezoelectric energy harvesting systems on a bluff body with a variable cross section along its generatrix. A combination of vibrational excitation from a shaker and airflow is considered the source from which energy is harvested. Varied excitation frequencies and airflow velocities across five different masses were considered, each defining the natural frequency of the system. The system’s performance in hybrid excitation, enhancements in energy harvesting, and challenges with these was observed, helping to determine optimal operating conditions to function effectively in ambient environments. The tests identified the conditions and ranges within which maximized harvesting responses were observed. Next, computational fluid dynamic (CFD) simulations were carried out to understand the impact of circular and square cross sections controlling the nature of the airflow and representative of the wide range of cross sections that may be utilized for such purposes. The analyses helped contextualize the opportunities and limitations of the use of such cross sections and helped in understanding if a transition from one cross section to another can lead to an assimilation of the advantages observed in using each cross section independently.

There are several factors to account for marine growth including but not limited to temperature, salinity, chlorophyll-a content, existing species in the environment and predating. This paper proposes a model of biological growth for hard species on marine structures, which can be compatible with site-specific and realistic ecology while also being able to translate the results for analyses linked to lifetime hydrodynamic or structural effects via commercial software or computing. The model preserves fundamentals of ecological aspects rather than using heuristics or random sampling to data fitting on sparsely collected information. The coefficients used in the proposed model align to the real world, with location-specific values, and can be adapted to new information. The growth model is demonstrated for Mythulis Edulis (blue mussel) colonisation to assess the lifetime hydrodynamic effects for the West Coast of Ireland and the Gulf of Guinea. The model can be extended to any hard growth approach.

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited.

This study proposes the new condition monitoring concept of using features in the measured rotation, or ‘pitch’ signal, of a crossing vehicle as an indicator of the presence of foundation scour in a bridge. The concept is explored through two-dimensional vehicle–bridge interaction modelling, with a reduction in stiffness under a pier used to represent the effects of scour. A train consisting of three 10-degree-of-freedom carriages cross the model on a profiled train track, each train varying slightly in terms of mass and velocity. An analysis of the pitch of the train carriages can clearly identify when scour is present. The concept is further tested in a scaled laboratory experiment consisting of a tractor–trailer crossing a four-span simply supported bridge on piers. The foundation support is represented by four springs under each pier, which can be replaced with springs of a reduced stiffness to mimic the effect of scour. The laboratory model also consistently shows a divergence in vehicle pitch between healthy and scoured bridge states.

Abstract Modelling floating offshore wind turbines (FOWTs) is challenging due to the strong coupling between the aerodynamics of the turbine and the hydrodynamics of the floating platform. Physical testing at scale is faced with the additional challenge of the scaling mismatch between Froude number and Reynolds number due to working in the two fluid domains, air and water. In the drive for cost‐reduction of floating wind energy, designers may be seeking to move towards high‐fidelity numerical modelling as a substitute for physical testing. However, the numerical engineering tools typically used for FOWT modelling are considered as mid‐fidelity to low‐fidelity tools, and currently lack the level of accuracy required to do so. Furthermore, there is a lack of operational FOWT data available for further development and validation. High‐fidelity tools, such as CFD, have greater accuracy but are cumbersome tools and still require validation. Physical scale model testing therefore continues to play an essential role in the development of FOWTs both as a source of validation data for numerical models and as an important development step along the path to commercialization of all platform concepts. The aim of this paper is to provide an overview of both numerical modelling and physical FOWT scale model testing approaches and to provide guidance on the selection of the most appropriate approach (or combination of approaches). The current state‐of‐the‐art will be discussed along with current research trends and areas for further investigation.

Abstract Anti-phase synchronization is the spontaneous formation of 2 clusters of oscillators synchronized between themselves within a cluster but opposite in phase with the other cluster. Neuronal networks in human and animal brains, ecological networks, climactic networks, and lasers are all systems that exhibit anti-phase synchronization although the phenomenon is encountered less frequently than the celebrated in-phase synchronization. We show that this disparity in occurrence is due to fundamental limits on the size of networks that can sustain anti-phase synchronization. We study the influence of network structure and coupling conditions on anti-phase synchronization in networks composed of coupled Stuart-Landau oscillators. The dependence of probability of anti-phase synchronization on connectivity of the network, strength of interaction over distance, and symmetry of the network is illustrated. Regardless of favourable network conditions, we show that anti-phase synchronization is limited to small networks, typically smaller than 20 nodes.

The initial design phase for offshore wind farms does not require complete geotechnical mapping and individual cone penetration testing (CPT) for each expected turbine location. Instead, background information from open source studies and previous historic records for geology and seismic data are typically used at this early stage to develop a preliminary ground model. This study focuses specifically on the interpolation and extrapolation of cone penetration test (CPT) data. A detailed methodology is presented for the process of using a limited number of CPTs to characterise the geotechnical behavior of an offshore site using artificial neural networks. In the presented study, the optimised neural network achieved a predictive error of 0.067. Accuracy is greatest at depths of less than 10 m. The pitfalls of using machine learning for geospatial interpolation are explained and discussed.

This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach.

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector.

Mechanical vibrations occur in the operation of most technical systems [...]

The design of an offshore wind turbine system varies with the turbine capacity, water depth, and environmental loads. The natural frequency of the structure, considering foundation flexibility, forms an important factor in structural design, lifetime performance estimates, and cost estimates. Although nonlinear numerical analysis in the time domain is widely used in the offshore industry for detailed design, it becomes necessary for project planners to estimate the natural frequency at an earlier stage and rapidly within reasonable accuracy. This paper presents a compendium of mathematical expressions to compute the natural frequencies of offshore wind turbine (OWT) structures on various foundation types by assimilating analytical solutions for each type of OWT, obtained by a range of authors over the past decade. The calculations presented can be easily made using spreadsheets. Example calculations are also presented where the compiled solutions are compared against publicly available sources.

The data presented in this article is in relation to the research article “Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage” Cahill et al. (2018) [1]. The article provides data on the full-scale bridge testing using piezoelectric vibration energy harvesters on Pershagen Bridge, Sweden. The bridge is actively excited via a swept sinusoidal input. During the testing, the bridge remains operational and train passages continue. The test recordings include the voltage responses obtained from the vibration energy harvesters during these tests and train passages. The original dataset is made available to encourage the use of energy harvesting for Structural Health Monitoring.

Hard marine growth is an important process that affects the design and maintenance of floating offshore wind turbines. A key parameter of hard biofouling is roughness since it considerably changes the level of drag forces. Assessment of roughness from on-site inspection is required to improve updating of hydrodynamic forces. Image processing is rapidly developing as a cost effective and easy to implement tool for observing the evolution of biofouling and related hydrodynamic effects over time. Despite such popularity; there is a paucity in literature to address robust features and methods of image processing. There also remains a significant difference between synthetic images of hard biofouling and their idealized laboratory approximations in scaled wave basin testing against those observed in real sites. Consequently; there is a need for such a feature and imaging protocol to be linked to both applications to cater to the lifetime demands of performance of these structures against the hydrodynamic effects of marine growth. This paper proposes the fractal dimension as a robust feature and demonstrates it in the context of a stereoscopic imaging protocol; in terms of lighting and distance to the subject. This is tested for synthetic images; laboratory tests; and real site conditions. Performance robustness is characterized through receiver operating characteristics; while the comparison provides a basis with which a common measure and protocol can be used consistently for a wide range of conditions. The work can be used for design stage as well as for lifetime monitoring and decisions for marine structures, especially in the context of offshore wind turbines.

Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100–500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

This paper investigates damage identification metrics and their performance using a cantilever beam with a piezoelectric harvester for Structural Health Monitoring. In order to do this, the vibrations of three different beam structures are monitored in a controlled manner via two piezoelectric energy harvesters (PEH) located in two different positions. One of the beams is an undamaged structure recognized as reference structure, while the other two are beam structures with simulated damage in form of drilling holes. Subsequently, five different damage identification metrics for detecting damage localization and extent are investigated in this paper. Overall, each computational model has been designed on the basis of the modified First Order Shear Theory (FOST), considering an MFC element consisting homogenized materials in the piezoelectric fiber layer. Frequency response functions are established and five damage metrics are assessed, three of which are relevant for damage localization and the other two for damage extent. Experiments carried out on the lab stand for damage structure with control damage by using a modal hammer allowed to verify numerical results and values of particular damage metrics. In the effect, it is expected that the proposed method will be relevant for a wide range of application sectors, as well as useful for the evolving composite industry.

Offshore wind turbines are a complex, dynamically sensitive structure due to their irregular mass and stiffness distribution, and complexity of the loading conditions they need to withstand. There are other challenges in particular locations such as typhoons, hurricanes, earthquakes, sea-bed currents, and tsunami. Because offshore wind turbines have stringent Serviceability Limit State (SLS) requirements and need to be installed in variable and often complex ground conditions, their foundation design is challenging. Foundation design must be robust due to the enormous cost of retrofitting in a challenging environment should any problem occur during the design lifetime. Traditionally, engineers use conventional types of foundation systems, such as shallow gravity-based foundations (GBF), suction caissons, or slender piles or monopiles, based on prior experience with designing such foundations for the oil and gas industry. For offshore wind turbines, however, new types of foundations are being considered for which neither prior experience nor guidelines exist. One of the major challenges is to develop a method to de-risk the life cycle of offshore wind turbines in diverse metocean and geological conditions. The paper, therefore, has the following aims: (a) provide an overview of the complexities and the common SLS performance requirements for offshore wind turbine; (b) discuss the use of physical modelling for verification and validation of innovative design concepts, taking into account all possible angles to de-risk the project; and (c) provide examples of applications in scaled model tests.

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

While the potential use of energy harvesters as structural health monitors show promise, numerical models related to the design, deployment and performance of such monitors often present significant challenges. One such challenge lies in the problem of leak detection in fluid-carrying pipes. Recent advances in experimental studies on energy harvesters for such monitoring has been promising but there is a paucity in existing literature in linking relevant fluid–structure interaction models around such applications. This paper addresses the abovementioned issue by developing a numerical model with Computational Fluid Dynamics (CFD) and Finite Element (FE) tools and carries out extensive analyses to compare it with existing experiments under controlled laboratory conditions. Conventional Polyvinylidene Fluoride (PVDF) films for leak detection and monitoring of water pipes were considered in this regard. The work provides guidelines on parameter selection and modeling for experimental design and repeatability of results for these types of experiments in future, around the demands of leak monitoring. The usefulness of such models is also demonstrated through the ability to estimate the optimum distribution frequency of these sensors that will enable the detection of the smallest leak of consequence under a known or established flow condition.

Fishing net cleanliness plays a critical role for aquaculture industry as bio-fouled nets restrict the flow of water through the net leading to a build-up of toxins and reduced oxygen levels within the pen, thereby putting the fish under increased stress. In this paper, we proposed an underwater fishing Net Health State Estimation (NHSE) method, which can automatically analyze the degree of fouling on the net through underwater image analysis using remotely operated vehicles (ROV) images, and calculate a blocking percentage metric of each net opening. The level of fouling estimated through this method help the operators decide on the need of cleaning or maintenance schedule. There are mainly six modules in the proposed NHSE method, namely user interaction, distortion correction, underwater image dehazing, marine growth segmentation, net-opening structure analysis, and blocked percentage estimation. To evaluate the proposed NHSE method, we collected and labeled several underwater images in Mulroy Bay, Ireland with pixel-wise annotations. In order to verify the universality and robustness of the algorithm, we simulated and built a virtual fishing farm, and, on this basis, collected and labeled fishing net images under different environmental conditions. Seven evaluation metrics are introduced to demonstrate the effectiveness and advantages of the proposed method.

Structural performance of renewable energy device platforms is central to their power generation in a reliable and competitive manner. However, there is a gap in research in the conceptual and experimental stages of such devices at lower technological readiness levels in terms of understanding of their structural responses. Uncertainties around knowledge related to damage conditions of such structures are under-researched and experimental investigations into the monitoring of performance of such structures are significantly needed. This research addresses this need and investigates various damage conditions in a scaled catenary moored spar platform in an ocean wave basin, exposed to typical wave conditions for the west coast of Ireland. A comparison of the monitored structural responses was carried out with respect to the undamaged experimental model. It was observed that while free decay tests were not useful to distinguish between various damage levels, a characterisation of the distribution of the responses can be relevant in identifying damages or significant structural changes. The work contributes to the much-needed experimental evidence base around structural health monitoring of renewable energy device platforms.

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented demonstrating how the inspection process can be enhanced through the use of virtual data. The first case study looks at how immersive virtual underwater scenes can be created to help divers and inspectors plan and implement real-world inspections. The second case study shows an example where deep learning-based computer vision methods are trained on datasets comprised of instances of virtual damage, specifically instances of barnacle fouling on the surface of a ship hull. The trained deep models are then applied to detect real-world instances of biofouling with promising results. The final case study shows how image-based damage detection methods can be calibrated using virtual images of damage captured under various simulated levels of underwater visibility. The work emphasizes the value of virtual data in creating a more efficient, safe and informed underwater inspection campaign for a wide range of built infrastructure, potentially leading to better monitoring, inspection and lifetime performance of such underwater structures.

The Tupperwave device is a closed-circuit oscillating water column (OWC) wave energy converter that uses non-return valves and two large fixed-volume accumulator chambers to create a smooth unidirectional air flow, harnessed by a unidirectional turbine. In this paper, the relevance of the Tupperwave concept against the conventional OWC concept, that uses a self-rectifying turbine, is investigated. For this purpose, wave-to-wire numerical models of the Tupperwave device and a corresponding conventional OWC device are developed and validated against experimental tests. Both devices have the same floating spar buoy structure and a similar turbine technology. The models include wave-structure hydrodynamic interaction, air turbines and generators, along with their control laws in order to encompass all power conversion stages from wave to electrical power. Hardware-in-the-loop is used to physically emulate the last power conversion stage from mechanic to electrical power and hence validate the control law and the generator numerical model. The dimensioning methodology for turbines and generators for power optimisation is explained. Eventually, the validated wave-to-wire numerical models of the conventional OWC and the Tupperwave device are used to assess and compare the performances of these two OWC type wave energy device concepts in the same wave climate. The benefits of pneumatic power smoothing by the Tupperwave device are discussed and the required efficiency of the non-return valves is investigated.

A vibration-based bridge scour detection procedure using a cantilever-based piezoelectric energy harvesting device (EHD) is proposed here. This has an advantage over an accelerometer-based method in that potentially, the requirement for a power source can be negated with the only power requirement being the storage and/or transmission of the data. Ideally, this source of power could be fulfilled by the EHD itself, although much research is currently being done to explore this. The open-circuit EHD voltage is used here to detect bridge frequency shifts arising due to scour. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. The method would work best with an initial modal analysis of the bridge structure in order to identify frequencies that may be sensitive to scour. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilised towards structural health monitoring of a model bridge affected by scour.

Tupperwave is a wave energy device based on the Oscillating-Water-Column (OWC) concept. Unlike a conventional OWC, which creates a bidirectional air flow across the self-rectifying turbine, the Tupperwave device uses rectifying valves to create a smooth unidirectional air flow, which is harnessed by a unidirectional turbine. This paper deals with the development and validation of time-domain numerical models from wave to pneumatic power for the Tupperwave device and the conventional OWC device using the same floating spar buoy structure. The numerical models are built using coupled hydrodynamic and thermodynamic equations. The isentropic assumption is used to describe the thermodynamic processes. A tank testing campaign of the two devices at 1/24th scale is described, and the results are used to validate the numerical models. The capacity of the innovative Tupperwave OWC concept to convert wave energy into useful pneumatic energy to the turbine is assessed and compared to the corresponding conventional OWC.