Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Canonical Perturbations in the Inflow on Wind Turbine Loads

The turbulent velocity field in the atmospheric boundary layer creates inflow fluctuations at a wide range of spatial and temporal scales that must be considered for resilient turbine design. This study is focused on separately characterizing the wind turbine’s response to canonical spatial or temporal oscillations in the inflow with only one wavelength or frequency. Temporal fluctuations in the inflow produce a response that is up to an order of magnitude greater than that of purely spatial fluctuations. The response to spatial oscillations in the inflow is also highly dependent on the alignment of the spatial waveform in the inflow to the rotor hub (i.e. phase angle). Different phase angles produced up to a factor of 30 difference in load variations. The integrated wind turbine loads show the largest response to spatial oscillations at wavelengths around 100-140m which is approximately four times the response at the chord scales. Structural deformations largely dampen the response to spatial oscillations in the inflow while increasing the response to temporal fluctuations near the blade rotation frequency. The wind turbine control system also amplifies the load response to temporal oscillations near the blade rotation frequency.
A Flexible, Multi-fidelity Levelised Cost of Energy Model for Floating Offshore Wind Turbines Multidisciplinary Design, Analysis and Optimisation Approaches

As the UK takes a step towards a greener, cleaner future aiming to be net zero by 2050, continuous development of the power network is required. A clear solution is offshore wind, having already proved its feasibility and success in nearshore sites. However, a large majority of near shore sites in the UK are already being utilised. The next step is to move into deeper waters and utilise the stronger, more consistent wind resources. A solution could be floating offshore wind which is still in its infancy, with only a few operational floating wind farms installed. Building upon the multidisciplinary design, analysis, and optimisation framework (MDAO) for floating offshore wind turbines (FOWT) being developed at the University of Strathclyde, called FEDORA, the aim of this work is to refine the LCoE model adopted by FEDORA, and applying it to perform the optimisation of the floating offshore OC3 SPAR. There is limited data on cost, therefore Hywind Scotland Pilot Park will be used as a basis for the LCoE model, allowing the results to be validated. This model is not restricted to SPARs, as it establishes a general methodology to calculate the life cycle cost of floating offshore wind farms. Utilising the improved cost model this work finds four optimised SPAR structures for four different maximum angles of inclination which can be experienced in the wind turbines operation. The improved cost model has a much higher accuracy, highlighting the initial cost model underestimates the cost of the SPAR structure by around half.
Identification of torsional frequencies of a large rotor blade based on measurement and simulation data

With increasing size of wind turbines, the aerodynamic and structural properties of the rotor blades become more and more important. However, studies investigating the torsional properties using measurement data and simulation results on full-size turbines are still scarce. In this study, torsional measurements are carried out on a large wind turbine with a rotor diameter of 180 m and compared with model results. In this study, torsional measurements on a large wind turbine with a rotor diameter of 180 m are conducted and compared with model results. Torsional parameters of a rotor blade are identified using two different measurement methods, the first based on strain gauges and the second based on accelerometers. Both methods show frequencies that can be associated with the natural frequencies of the two first torsional modes of the rotor blade. To extract the natural frequencies and to reduce external effects, a manual pitching procedure is evaluated. Here, the first two natural frequencies associated with torsional modes can be identified. In a second step, the behaviour of the blade during power production is investigated and compared with the simulation results.
Long Memory Gated Recurrent Unit for Time Series Classification

Time series analysis is an important and challenging problem in data mining, where time series is a class of temporal data objects. In the classification task, the label is dependent on the features from the last moments. Due to the time dependency, the recurrent neural networks, as one of the prevalent learning-based architectures, take advantage of the relation among history data. The Long Short-Term Memory Network (LSTM) and Gated Recurrent Unit (GRU) are two popular artificial recurrent neural networks used in the field of deep learning. LSTM designed a gate-like method to control the short and long historical information, and GRU simplified those gates to obtain more efficient training. In our work, we propose a new model called as Long Memory Gated Recurrent Unit (LMGRU) based on such two remarkable models, where the reset gate is introduced to reset the stored value of the cell in Long Short-Term Memory (LSTM) model but the forget gate and the input gate are omitted. The experimental results on several time series benchmarks show that LMGRU achieves better effectiveness and efficiency than LSTM and GRU.
Turbulence above offshore wind farms measured by aircraft

The flow inside and around large offshore wind farms may range from smaller and shorter structures present due to the mechanical turbulence generated by wind turbines to larger, and longer structures indicative of the mesoscale flow. In this study, we explore the variation in turbulence structures above large offshore wind farms located in the North Sea using the data obtained from a research aircraft. The aircraft was flown upstream, downstream, and above the wind farm clusters. We evaluate the normalized autocorrelation of the wind velocity component in the mean wind direction for transverse separations and the rate of energy dissipation in the inertial subrange for neutral and stable stratification. During the neutral stratification, there is high ambient turbulence in the atmosphere and large values of the energy dissipation rate. The intensity of small-scale turbulence structures is increased above and downstream of the wind farm and it dominates the mesoscale flow structures. While in stable stratification, mesoscale flow structures are not only dominant upstream of the wind farm but also in the downstream flow. Apart from the atmospheric stratification, the wind farm layout also affects the turbulent structures, the rate of energy dissipation, and time for wake recovery.
Flow Modelling for Wind Farm Control: 2D vs. 3D

Control-oriented models provide a basis for wind farm control to improve power production and reduce structural loading. Wake steering is considered to be one of the most promising techniques to achieve this. Wind turbine wakes under yaw misalignment are deflected downstream and have been shown to produce a curled or kidney-shaped structure. A Navier-Stokes based code called FRED was developed to model wind farm flow in 2D to perform yaw control. To tackle the differences between 2D and 3D flow, this work introduces a generalised continuity correction and wind turbine force scaling terms to the FRED framework. The effectiveness of approximating 3D results is tested by comparison with 3D simulations in the same framework. The continuity correction is now applicable to general wind directions and effective in reducing wake width and speed-up effects. The magnitude of wake deflection can be tuned using a force scaling term. However, we show that there remains a qualitative difference in the deflection profile downstream, as well as a difference in the propagation of yaw effects over time. From this study we can conclude that there is a fundamental difference between 2D and 3D flow physics in spatial and temporal dynamics which makes the 2D modelling approach challenging for control without further empirical adjustments. The necessary corrections are likely to be complex and non-physical, leading to a departure from the first principles foundation that FRED is developed from.
On the ill-conditioning of the combined wind speed estimator and tip-speed ratio tracking control scheme

In recent years, industrial controllers for modern wind turbines have been designed as a combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking control scheme. In contrast to the conventional and widely used Kω 2 torque control strategy, the WSE-TSR scheme provides flexibility in terms of controller responsiveness and potentially improves power extraction performance. However, both control schemes heavily rely on prior information about the aerodynamic properties of the turbine rotor. Using a control-oriented linear analysis framework, this paper shows that the WSE-TSR scheme is inherently ill-conditioned. The ill-conditioning is defined as the inability of the scheme to uniquely determine the wind speed from the product with other model parameters in the power balance equation. Uncertainty of the power coefficient contribution in the latter mentioned product inevitably leads to a biased effective wind speed estimate. As a consequence, in the presence of uncertainty, the real-world wind turbine deviates from the intended optimal operating point, while the controller believes that the turbine operates at the desired set-point. Simulation results confirm that inaccurate model parameters lead to biased estimates of the actual turbine operating point, causing sub-optimal power extraction efficiency.
Modelling of turbine power and local wind conditions in wind farm using an autoencoder neural network

Neural network models have been widely applied in wind energy to enable data-driven modelling of turbine’s power, loads, damage, and other quantities in wind farm. They have shown advantages in the flexibility, robustness, and efficiency, as well as the ease of application. An important task in data-driven modelling of wind farm is the characterization of wake-induced effects within wind farm. Because of the large number of wind turbines in wind farm and the broad range of conditions required for power output assessment, it has become relevant to use computationally efficient surrogate models that are based on parameterizations of the wind farm layout. Previous studies on surrogate models of wake-induced effects have focused on parameterizations based on manually extracted features from the wind farm geometry. The present study applies an autoencoder neural network to automatically learn the features representing the wind farm layout. Specifically, two autoencoders are studied and compared. The autoencoder provides dimension reduction and feature learning that allow representing the entire wind farm layout in just a few latent variables, thus allowing efficient and accurate surrogate models of wake-induced effects. The outputs of the autoencoder, together with any ambient wind condition variables, serve as inputs to a standard feedforward neural network that predicts the wake-induced effects for any turbine in the wind farm. Our results show that the autoencoder provides an automatic and effective way to parameterize the wind farm geometry, and facilitates the building of efficient surrogate models that are capable of accurate modeling of wake-induced effects in a wind farm with arbitrary layout.
Score-CAM++: Class Discriminative Localization with Feature Map Selection

In recent years, explaining convolutional neural networks (CNN) has received increasing attention since it helps to build humans’ trust on CNNs by exposing their inference basis. In this field, generating intuitive saliency maps that highlight the input regions most related to model decisions is one of the popular approaches. Building on a state-of-the-art saliency method named Score-CAM, we propose Score-CAM++ to generate better saliency maps with higher efficiency (when compared to Score-CAM). Different from Score-CAM, which adopts all the feature maps in target layer to produce saliency maps, we propose the “feature map selection” operation to select the feature maps that capture the “positive” information (i.e., the features whose increment in intensity leads to an increase in target score). Then the selected feature maps are up-sampled as masks to perturb the input and compute the weights of the corresponding feature maps. Finally, the linear combination of the weighted feature maps forms the saliency map. Compared with Score-CAM, the experiments based on VGG-16 show that our method saves 25%-30% of the time when generating saliency maps. Meanwhile, our conducted evaluations, both subjective and objective, show that our method provides better visual explanations when compared to the previous saliency methods.
Deep Face-Swap Model Combining Attention Mechanism and CycleGAN

Deep face swapping is a hot field in computer vision, which uses deep learning technology to replace the face in the original image while keeping other contents unchanged. To solve the problem of discontinuities across the blending boundaries in existing mainstream deepfake methods, we propose a face swapping model combining attention mechanism and CycleGAN. Firstly, a generator incorporating the attention module is used to obtain the generated graph. Then, the discriminator based on PatchGAN is used to discriminate if the graph is synthesized. Finally, the least square loss and SmoothL1 loss are used to improve the loss function. Experimental results manifest that our method reduces the difference by 0.43 in facial region and increases the similarity by 0.56 in background region. The model we proposed can improve the quality of face swapping images effectively, and enrich the existing face swapping methods, which has great significance for both generation and detection of deep face swapping research.
Serial-Refine Method for Fast Wake-Steering Yaw Optimization

In this paper we present the Serial-Refine method for quickly finding the optimal yaw angles in wake steering. The method optimizes turbine angles serially from upstream to downstream using a small number of candidate angles. The presented results show that Serial-Refine finds solutions that are at least as good as former conventional optimization approaches but that require much less computation time.
History and Applications

Since 1901, the time of Marconi's first experiments with transmitting electromagnetic waves, antennas have found several important applications over the entire frequency range, and numerous designs of antennas now exist. Antennas are an integral part of our everyday lives and are used for a multitude of purposes. All antennas operate on the same basic principles of electromagnetic theory formulated by James Clark Maxwell. An antenna is used to either transmit or receive electromagnetic waves, and it serves as a transducer that converts guided waves into free-space waves in the transmitting mode, or vice-versa in the receiving mode. Maxwell put forth his unified theory of electricity and magnetism in 1873 [1] in his famous book A Treatise on Electricity and Magnetism, incorporating all previously known results on electricity and magnetism and expressing these mathematically through what we refer to as Maxwell's equations, which hold over the entire electromagnetic spectrum. His theory was met with much skepticism, and it was not until 1886 that Heinrich Hertz [2], considered the Father of Radio, was able to validate this theory with his experiments. The first radio system, at a wavelength of 4 m, consisted of a I»/2 dipole (transmitting antenna) and a resonant loop (receiving antenna) [3]. By turning on the induction coil, sparks were induced across the gap and detected at the receiving antenna.
Directionality detection of delaminations basing on analysis of CT slices using wavelet and Hough transforms-based algorithm

Summary Growing demands for high reliability of constructions made of composite materials lead to a significant development of non-destructive testing methods of structural diagnostics. Among the most accurate and sensitive techniques used for analysis of internal defects, is the computed tomography (CT). However, since the results obtained from CT are highly precise, certain problems with their interpretation occur. The visual information about defects has isotropic character, thus it is not possible to evaluate directions of propagation of defects. The following study presents an investigation on a problem of identifying the directionality of delaminations evolution in layered composites. The tests were performed on specimens made of polymeric composites with delamination resulted from water-jet cutting. The images of cross sections of specimens were acquired by CT scanning. In the paper, the developed algorithm based on wavelet and Hough transforms as well as other methods of image processing and analysis is presented. The proposed method allows for automatic detection of directionality of delaminations and could be applied in quality control of composite components as well as non-destructive testing during their operation.
Measuring Familiarity for Natural Environments Through Visual Images 1

: An on-site visual preference methodology involving a pre-and-post rating of bog landscape photographs is discussed. Photographs were rated for familiarity as well as preference. Preference was-shown to be closely related to familiarity, assuming visitors had the opportunity to view the scenes during the on-site hiking engagement. Scenes rated high on preference were likely to be scenes that visitors rated most familiar. In addition, familiarity ratings indicated that visitors were quite cognizant of what scenes they had or had not seen during the on-site hike. A single on-site experience was enough for visitors to recall what they had seen, with repeat visitors showing no apparent increased familiarity. Thus, considerable information con- cerning natural environments appears to be processed and recorded by visitors during on-site experiences.
Bridge information modelling (BrIM) for a listed viaduct

Bridge information modelling (BrIM) invokes stakeholder collaboration, supported by policy and process frameworks, combined with interoperable technology, to manage information and enable effective decision making throughout the lifecycle of a bridge asset. This paper considers the application of BrIM for the proposed rehabilitation of a heritage listed viaduct. The structure under consideration is the landmark Chetwynd viaduct in Cork (Ireland), which formed part of the Cork–Bandon railway infrastructure constructed in 1851 and decommissioned in 1961. As with infrastructure of its type, few as-built records exist. Consequently, the geometry, material properties and condition of the existing structure are required to inform the initial feasibility assessment and subsequent design, construction and operation of this viaduct asset. Respective BrIM and traditional approaches to the data capture phase are outlined and compared in terms of time, expertise and deliverables. The significant deliverables from the.
Feedbacks between plants, flow, and particle fate

Environmental flows carry a wide variety of particles that interact with vegetation. Vegetated canopies are anchored by sediment, release pollen and seeds to colonize new ground, rely on nutrients carried by sediment, and may be killed by disease spores. While the influence of vegetation on flow profiles has been investigated, the transport of particles within vegetated canopies remains poorly understood. In this thesis I present the results from laboratory experiments and numerical modeling that investigated particle fate and transport around emergent and submerged model vegetation canopies. The effect of canopy mediated flow on particle transport was explored in experimental studies using model vegetation. First, the flow diversion and extended wake region due to a circular patch of model reedy emergent vegetation resulted in regions of scour on the patch side and deposition in the patch wake, which are related to a non-dimensional flow blockage parameter. In-patch scour increased with turbulent kinetic energy levels, which were positively correlated to stem density. Second, within a long, submerged canopy, the capture of particles was reduced near the leading edge due to the presence of a vertical updraft. In the fully developed region, particle capture was increased for releases below the penetration of canopy scale vortices, and for particles with increased settling velocity. The impact of canopy flexibility on turbulence within the canopy was explored using a submerged canopy of model flexible vegetation. The drag reduction due to reconfiguration was described through a drag coefficient that decreased as a power-law function of velocity, with a negative exponent (Vogel number). Velocity measurements made within and above the canopy demonstrate that unsteady reconfiguration, responding to individual turbulent events, preferentially allows stronger sweeps to penetrate the canopy, enhancing the skewness. Next, spore escape was investigated across a range of canopy densities and particle settling velocities using a random displacement model (RDM) parameterized with an eddy diffusivity based on a simple set of physical parameters. This work filled a gap between field observations and traditional Lagrangian stochastic modeling, improving predictions of fungal spore escape to drive long range transport models. The effect of canopy and particle characteristics on the genotypic diversity of Zostera marina canopies was explored, pointing to the physical mechanisms governing successful pollination. Thesis Supervisor: Heidi M. Nepf Title: Donald and Martha Harleman Professor of Civil and Environmental Engineering
Diagnosis and Characterization of Middle-ear and Cochlear Functions

Hearing impairment is one of the most common physical disabilities in the world. Conductive hearing loss results from dysfunction of the middle ear, and sensorineural hearing loss results from abnormalities within the cochlea or central nervous system. To improve the diagnosis of hearing loss and develop new technologies for restoration of hearing, changes of the middle ear and cochlear functions in relation to ear structural disorders in hearing impaired ears need to be characterized. This paper summarizes our biomedical engineering approach to characterize the middle ear and cochlear functions in normal and diseased ears. With the experimental measurements in human temporal bones and animal models and the finite element (FE) modeling of the human ear, a better understanding of biomechanics of the ear for sound transmission is achieved. The acoustic-mechanical vibration and energy transmission through the middle ear in normal and diseased ears can be visualized and quantified in a 3-dimensional FE model. Four novel model-derived “auditory test curves” named as the middle ear transfer function, energy absorbance, admittance tympanogram, and tympanic membrane holography are generated with clinically relevant applications.