Constant Wind Research Articles

Optimizing Ship Airwake Database for Predicting Autospectra Using Deep Learning

Airwakes are shed behind a ship’s superstructure, and they represent a highly turbulent and rapidly distorting flow field. Because of this complexity, they require a baseline: A database of flow velocity points, preferably through experiments and also through high-fidelity computational fluid dynamics simulations. However the database is prohibitively costly, and in situ measurements, at a constant wind angle, are often affected by the weather. A previously developed perturbation scheme helps to generate the autospectrum in closed form from a given wind database, specifically, from the numerically extracted autospectrum for this database. The primary objective of this paper is to demonstrate how deep neural networks can be fruitfully exploited to reduce the dependence on a database of flow velocity points in generating the autospectra, by reducing the amount of in situ data required to achieve the same accuracy as established methods. Two machine learning approaches are developed to predict the autospectrum for a data set when provided with a subset of the wind data set at the input and are assessed using cross-validation. In the first, a neural network is applied to predict the perturbation scheme. Though this method did not predict the autospectrum better than the previously developed perturbation scheme, it laid the foundation for the second method. In the second, a neural network is applied to the autospectra which can be numerically extracted from a database. An improvement is found in the average error computed over all data sets for the model-predicted autospectrum compared to the average error in the autospectrum predicted using the conventional method. The model provided better results for 51.4% of the data sets compared to the conventional method, with many data sets showing marked improvement. The database measured from the Naval Academy YP676 training vessel is used for training and testing the neural network.

Active manipulation of a tethered drone using explainable AI

Tethered drones are currently finding a wide range of applications such as for aerial surveillance, traffic monitoring, and setting up ad-hoc communication networks. However, many technological gaps are required to be addressed for such systems. Most commercially available tethered drones hover at a certain position; however, the control task becomes challenging when the ground robot or station needs to move. In such a scenario, the drone is required to coordinate its motion with the moving ground vehicle without which the tether can destabilize the drone. Another challenging aspect is when the system is required to operate in GPS denied environments, such as in planetary exploration. In this paper, to address these issues, we take advantage of passive or force-based control in which the tension in the tether is sensed and used to drive the drone. Fuzzy logic is used to implement the force-based controller as a tool for explainable Artificial Intelligence. The proposed fuzzy logic controller takes tether force and its rate of change as the inputs and provides desired attitudes as the outputs. Via simulations and experiments, we show that the proposed controller allows effective coordination between the drone and the moving ground rover. The rule-based feature of fuzzy logic provides linguistic explainability for its decisions. Simulation and experimental results are provided to validate the novel controller. This paper additionally develops an adaptive controller for estimating unknown constant winds on these tethered drone systems using a proportional controller. The simulation results demonstrate the effectiveness of the proposed adaptive control scheme in addressing the effect of wind on a tethered drone.

Kelvin Wave Propagation over a Sloping Interface and Relationships with El Niño Southern Oscillation

Internal Kelvin Wave (KW) propagation is studied about variations in the sea surface temperature anomaly (SSTA) over the tropical Pacific. Temperature and Salinity (TS) observations have been used to define the vertical structure of the ocean about the propagation properties of KWs. Changes in the vertical structure of the water column determine consistent zonal variations in the wave velocity, with values varying, roughly, from 1.8 to 2.6 m/s. The authors document that KWs are formed regularly at the western boundary of the tropical Pacific, but, in these cases, never overcome the dateline. Occasionally, KWs are generated in the region comprised between 170oE and 170oW, and, on all these occasions, a positive phase of the El Niño Southern Oscillation (El Niño) event is recorded. A model, named Sloping Interface Model (SIM), is proposed to relate changes in the pycnocline depth, associated with transiting KWs, and SST anomaly variations. In the SIM, whose equations are consistent with the Recharge/Discharge paradigm, the ocean is described as a two-layer system and the climatological state, represented by a sloping pycnocline, is maintained by a constant easterly wind stress. Using the SIM and coherently with the Recharge/Discharge paradigm, the authors show that changes in the averaged SSTA over El Niño 3, 3.4 and 4 regions are nearly perfectly correlated to pycnocline displacements due to transiting KWs.

Development of a methodology for radon pollution studies based on algorithms taking into account the influence of constant mountain-valley winds

The article describes the development of a methodology for radon pollution studies based on algorithms that take into account the influence of constant mountain-valley winds. The solved problem of the study of radon emanations arising from the stress-strain state of rocks is an important step in the study of man-made bulk arrays on the environment and the assessment of radiation safety. Decommissioned tailings dumps eventually dry up and turn into hardening man-made bulk arrays, which negatively affect the surrounding ecosystems. The proposed methodology is implemented on the basis of the proposed algorithms for determining the optimal choice of measurement conditions, taking into account the influence of constant mountain-valley winds. As an approbation of the methodology, field studies were carried out, including measurements of the equivalent equilibrium volume activity of radon-222 at various points of the tailings dump. For this purpose, specialized methods and devices were used, which made it possible to determine the concentration of radon in the air and evaluate its emanations from the tailings dump. The data obtained were processed and analyzed using specialized software and algorithmic software, which allows for a detailed analysis and evaluation of the values.

Time-Optimal Path Planning in a Constant Wind for Uncrewed Aerial Vehicles Using Dubins Set Classification

Time-Optimal Path Planning in a Constant Wind for Uncrewed Aerial Vehicles Using Dubins Set Classification

Development and verification of a method to simulate non-stationary vehicle interior wind noise

As speeds and directions of the vehicle and wind change, the time-varying flow creates variations in wind noise, which can be referred to as non-stationary wind noise. To investigate people's perceptions of non-stationary wind noise inside the vehicle, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary wind noise recordings taken at several constant wind speeds and directions to form functions that relate the 1/3 octave sound pressure level with wind speed and direction. These functions are used to create time-varying filters based on provided time histories of wind speed and direction. To reduce the cost of taking many stationary measurements, an improved method was investigated. At each yaw angle, one speed sweep wind tunnel measurement was used to estimate the relationship between sound pressure level and wind speed. Two partially correlated white noise signals were then filtered to simulate binaural sounds that had a similar coherence structure between the left and right ear sounds to that observed in binaural measurements in the vehicle. The accuracy of the simulations was validated by comparing wind noise simulations to vehicle interior noise measured in the wind tunnel and on the road.

The analysis of variants of the main frame of the two-nave steel hall for sports purposes

The objective of the analysis was the choice of the cross sections of the Steel hall’s main frame. The minimisation of weight of the construction elements was assumed as a criterion of optimisation. Initially, the forces which load the main frame, caused by constant interactions, snów interactions and wind interactions, were calculated. In the first phase of the analysis, the choice of cross sections for the elements of the lattice girder was madę. Ninę variants of the lattice girder were considered and the lightest truss was chosen. In the second phase, the analysis of the cross sections of the spandrel beam and the columns was performed. Four variants of these elements were considered. Similarly to the first phase, the variant considered best in terms of weight of the elements was chosen. The results of the calculations from both phases were shown in the bar graphs

Modelling and Simulation of Grid Connected Wind Turbine Induction Generator for Windfarm

As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.

The Study of the Impacts Introduced by a Wind Farm, having Doubly Fed Induction Generators, on a Power System

The paper discusses the 5th order model of a doubly fed induction generator which is used to study the impacts of a wind farm at the power system. The integration of wind farms poses serious problems on the stability of a power system. The study also illustrates the need for reactive power compensation when a wind generator is to be connected to a power system. A realistic 50 MW wind farm, proposed in the southern Sindh province of Pakistan, is being simulated to illustrate the dynamics of this integration on the national grid. This preliminary study assumes a constant wind speed for the duration of the studied period.

3D pressure-corrected ballistic extrapolation of solar wind speed in the inner heliosphere

Solar wind parameters at different locations in the inner heliosphere can be estimated using various solar wind extrapolation methods. The simple ballistic method extrapolates solar wind parameters from the point of measurement to a chosen heliospheric position by assuming that major solar wind structures are persistent and arrive relatively unaltered to the target position. The method considers the rotation period of the Sun while assuming a constant solar wind speed during radial propagation. We improve the simple ballistic model by considering the interaction between the slow and the fast solar wind with a pressure correction during the propagation. Instead of extrapolating from the position of a single spacecraft, we apply this pressure-corrected ballistic method to 2D speed maps of the solar source surface available from solar coronal models to determine the solar wind speed in the inner heliosphere in 3D, between latitudes of ±50°. We also take into account the effects of the solar differential rotation in our model. Our method is simple and fast, and it can be applied to different source surface datasets. The results of our model are validated with in situ data from the ACE spacecraft. We find that the pressure-corrected ballistic method can give accurate predictions of the solar wind in 3D.

A catalogue of observed geo-effective CME/ICME characteristics

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), to improve our ability to forecast their propagation characteristics and mitigate the risks to our technologically driven society. The essential part of achieving this goal is to assess the performance of forecasting models. To this end, the quality and availability of suitable data are of paramount importance. In this work, we merged publicly available data of CMEs from both in-situ and remote observations in order to build a dataset of CME properties. To evaluate the accuracy of the dataset and confirm the relationship between in-situ and remote observations, we have employed the Drag-Based Model (DBM) due to its simplicity and modest consumption of computational resources. In this study, we have also explored the parameter space for the drag parameter and solar wind speed using a Monte Carlo approach to evaluate how efficiently the DBM determines the propagation of CMEs for the events in the dataset. The geoeffective CMEs selected as a result of this work are compliant with the hypothesis of DBM (isolated CME, constant solar wind speed beyond 20 R⊙) and also yield further insight into CME features such as arrival time and arrival speed at L1 point, lift-off time, speed at 20 R⊙ and other similar quantities. Our analysis based on the acceptance rate in the DBM inversion procedure shows that almost 50% of the CME events in the dataset are well described by DBM as they propagate in the heliosphere. The dataset includes statistical metrics for the DBM model parameters. The probability distribution functions (PDFs) for the free parameters of DBM have been derived through a Monte Carlo-like inversion procedure. Probability distribution functions obtained from this work are comparable to PDFs employed in previous works. The analysis showed that there exist two different most probable values (median values) of solar wind speed for DBM input based on slow (wslow ≈ 386 km/s) and fast (wfast ≈ 547 km/s) solar wind type. The most probable value for the drag parameter (γ ≈ 0.687 × 10−7 km−1) in our study is somewhat higher than the values reported in previous studies. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. Additionally, possible CME events are identified where the DBM prediction is not valid due to model limitations and higher uncertainties in the input parameters. These events require further thorough investigation in the future.

Effect of sliding friction on torsional vibration of wind turbine drivetrain system under constant wind load

Effect of sliding friction on torsional vibration of wind turbine drivetrain system under constant wind load

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.

Mass Mortality of Asari Clams (Ruditapes philippinarum) Triggered by Wind-Induced Upwelling of Hypoxic Water Masses

To investigate the mass mortality of the macrobenthos community, primarily asari clams, triggered by upwelling-driven hypoxia, we conducted continuous observations of temperature, salinity, and DO, and monthly macrobenthos monitoring on the Rokujo tidal flat in Mikawa Bay, central Japan, from 2014 to 2016. Additionally, laboratory experiments were conducted using sediments on a tidal flat containing macrobenthos to examine the possibility of hydrogen sulfide formation in tidal flats. The bottom layer at the offshore station was intermittently hypoxic, and the station of the tidal flat was occasionally hypoxic in August and September for three years. Hypoxia was mostly observed on the tidal flat when constant easterly winds were recorded offshore. The biomass of asari clams decreased considerably from September to October 2016 when hypoxia was intermittent. Hypoxia persisted for approximately one week from 20 September, which was associated with the calm weather and stagnation of tidal currents owing to the neap tide. Conversely, the hydrogen sulfide concentration in the water directly above the sediment exceeded 30 mg L−1 after 3 days of incubation in the laboratory experiment. Therefore, the possibility of oxygen consumption on tidal flats due to hydrogen sulfide formed by biological die-offs was considered in the long-term persistence of hypoxia.

An “objective” definition of potential vorticity. Generalized evolution equation and application to the study of coastal upwelling instability

In this paper, we propose a form for potential vorticity (PV), rescaled using the Lorenz’s rearranged density profile, the novelty being that we here take into account its time evolution. We argue this rescaled PV is more representative of the dynamics, in particular to evaluate the respective impact of mixing and friction on the generation of geostrophic circulation. The impact of mixing at global scale, which only modifies the global stratification at rest, is taken into account in the evolution equation of this “objective” definition of PV, in the sense that it scales the PV changes with respect to its effect on the circulation. Numerically, we show that all terms can be calculated coherently using a single computation cell.We illustrate our purpose by studying the instability of coastal upwelling currents, using a numerical model at high resolution. The configuration is a periodic flat channel on the f-plane with vertical walls at the southern and northern boundaries. A constant wind is applied over a fluid at rest with an initial linear stratification. An upwelling current forms at the northern coast. After a few days, instabilities develop and vortices eventually emerge with surface intensified cyclones and subsurface anticyclones. We show that these instabilities and eddies are associated with (rescaled) PV anomalies, triggered by mixing and friction.We describe rescaled PV budgets in a layer bounded between the surface and an isopycnal level. Eulerian and Lagrangian diagnostics allow to analyze irreversible PV production terms, distinguishing the influence of advection, friction (associated with wind stress) and mixing. We find that friction plays the main role, generating negative PV anomalies, while mixing acts to dampen this negative PV production. The association of this negative PV anomaly with the outcropping front leads to the baroclinic destabilization of the upwelling front, creating subsurface anticyclonic vortices and surface intensified cyclonic vortices. Varying the strength of the wind forcing shows that mixing is the most sensitive process, with a net effect that is strongly reduced or even reversed with moderate to weak winds.When the dynamics is fully turbulent, with filaments and vortices of small sizes, the PV production by mixing and friction is enhanced but the Lagrangian diagnostics are more difficult to analyze, since fluctuations at grid scale become significant and numerical effects – associated with imperfections of the numerical schemes – spoil the PV budget calculation.

Analysis of a hybrid heat and underwater compressed air energy storage system used at coastal areas

Offshore wind power has achieved a storage interest in recent years due to the advantages such as high wind speed, constant wind direction, rich wind energy resources and sufficient annual utilization hours. It has been widely recognized that the underwater compressed air energy storage is one of the most competitive technologies to be integrated into the offshore wind farms. However, the economics of the underwater gas storage device is the main obstacle when this type of energy storage is arranged in shallow-water areas. A hybrid heat and underwater compressed air energy storage system is thus suggested to be integrated with the fluctuating renewable energies. This necessitates the use of electrically heated solid thermal energy storage to provide greater flexibility. Both the system thermodynamics and economics are examined under the premise of constant or varying power input. Results demonstrate that the system behavior is strongly dependent on the water depth at 0–100 m. The combined efficiency and net present value are 44.4% and 67.41 million dollars for a 10 MW hybrid energy storage system as the water depth is 100 m, while they are 29.9% and 40.39 million dollars for the standalone system. The system integrating with the solid thermal energy storage is verified to be a much more valuable technology to manage offshore renewable powers. Furthermore, it indicates that the underwater pipe with a larger diameter is favorable to improve system performance.

Exploring storm petrel pattering and sea-anchoring using deep reinforcement learning

Developing hybrid aerial-aquatic vehicles that can interact with water surfaces while remaining aloft is valuable for various tasks, including ecological monitoring, water quality sampling, and search and rescue operations. Storm petrels are a group of pelagic seabirds that exhibit a unique locomotion pattern known as ‘pattering’ or ‘sea-anchoring,’ which is hypothesized to support forward locomotion and/or stationary posture at the water surface. In this study, we use morphological measurements of three storm petrel species and aero/hydrodynamic models to develop a computational storm petrel model and interact it with a hybrid fluid environment. Using deep reinforcement learning algorithms, we find that the storm petrel model exhibits high maneuverability and stability under a wide range of constant wind velocities after training. We also verify in the simulation that the storm petrel can use its ‘pattering’ or ‘sea-anchoring’ behavior to achieve different biomechanical sub-tasks (e.g. weight support, forward locomotion, stabilization) and adapt it under different wind speeds and optimization objectives. Specifically, we observe an adjustment in storm petrel’s movement patterns as wind velocity increases and quantitively analyze its biomechanics underneath. Our results provide new insights into how storm petrels achieve efficient locomotion and dynamic stability at the air–water interface and adapt their behaviors to different wind velocities and tasks in open environments. Ultimately, our study will guide the design of next-generation biomimetic petrel-inspired robots for tasks requiring proximity to the water interface and efficiency.

Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics

AbstractWith the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.

Offshore Wind Farms in South Korea: A Potential Site for Scallop Culture

Owing to the constant wind generated by the vast ocean, energy production from offshore wind farms (OWFs) plays an important role in the expansion of renewable energy. However, areas close to large wind farms are often left unutilized, and aquaculture farmers find it difficult to efficiently utilize these unoccupied spaces due to limited information showing the feasibility of utilization of OWFs as potential scallop culture sites. To analyze whether the two scallop species Zhikong scallop (Chlamys farreri) and bay scallop (Argopecten irradians) can be grown at OWFs of Gochang and Buan, Jeollabuk-do, Republic of Korea, the growth characteristics of the two scallop species were analyzed and compared with those grown at the Tongyeong Megacosm Test Station. The results clearly showed that the growth of scallops at the OWF was significantly lower with respect to the shell lengths, height, width, and weight, compared to those grown at the megacosm station. However, scallops grown at the OWF still showed consistent growth in parallel with those grown at the megacosm test station. Yet, there was a species-specific mortality rate between the two sites. In addition, our results suggest that temperature may be a key determinant of the growth of C. farreri and A. irradians. Overall, this study contributes to establishing a foundation for the stable and continuous farming of marine bivalves (e.g., clams, oysters, mussels, and scallops) in OWF areas.

Wandering albatrosses exert high take-off effort only when both wind and waves are gentle

The relationship between the environment and marine animal small-scale behavior is not fully understood. This is largely due to the difficulty in obtaining environmental datasets with a high spatiotemporal precision. The problem is particularly pertinent in assessing the influence of environmental factors in rapid, high energy-consuming behavior such as seabird take-off. To fill the gaps in the existing environmental datasets, we employed novel techniques using animal-borne sensors with motion records to estimate wind and ocean wave parameters and evaluated their influence on wandering albatross take-off patterns. Measurements revealed that wind speed and wave heights experienced by wandering albatrosses during take-off ranged from 0.7 to 15.4 m/s and 1.6 to 6.4 m, respectively. The four indices measured (flapping number, frequency, sea surface running speed, and duration) also varied with the environmental conditions (e.g., flapping number varied from 0 to over 20). Importantly, take-off was easier under higher wave conditions than under lower wave conditions at a constant wind speed, and take-off effort increased only when both wind and waves were gentle. Our data suggest that both ocean waves and winds play important roles for albatross take-off and advances our current understanding of albatross flight mechanisms.