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Multi-rotor system (MRS) wind turbines can provide a competitive alternative to large-scale wind turbines due to their significant advantages in reducing capital, transportation, and operating costs. The main challenges of MRS wind turbines include the complexity of the supporting structure, mathematical modeling of the aerodynamic interaction between the rotors, and the yaw control mechanism. In this work, MATLAB 2018b/Simulink® software was used to model and simulate a twin-rotor wind turbine (TRWT), and an NREL 5 MW wind turbine was used to verify the model outputs. Different random signals of wind velocities and directions were used as inputs to each rotor to generate different thrust loads, inducing twisting moments on the main tower. A yaw controller system was adapted to ensure that the turbine constantly faced the wind to maximize the power output. A DC motor was used as the mechanism’s actuator. The goal was to achieve a compromise between aligning the rotors with the wind direction and reducing the torque induced on the main tower. A comparison between linear and nonlinear controllers was performed to test the yaw system actuator’s response at different wind speeds and directions. Sliding mode control (SMC) was chosen, as it was suitable for the nonlinearity of the system, and its performance showed a faster response compared with the PID controller, with a settling time of 0.17 sec and a very low overshoot. The controller used the transfer function of the motor to generate a sliding surface. The dynamic responses of the controlled angle are shown and discussed. The controller showed promising results, with a suitable response and low chattering signals.

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

Spar is one of the promising floating platforms to support offshore wind turbines. Wind heeling moment is large in the case of floating offshore wind turbines and, therefore, it is important to reduce the pitch motion of the floating platform. To address this issue, a spar platform with damping plates is proposed and investigated in this study. (i) Type-A, (ii) Type-B, and (iii) Type-C models of 1/120 scale were fabricated with similar stability parameters. Type-A is a classic spar, Type-B and Type-C are spar with damping plates by replacing the ballast water part with horizontal plates and vertical plates, respectively. The rotor model consists of (i) no disk and (ii) with disk conditions. A series of model scale experiments were carried out in the water tank in regular waves, and motion response was measured. A calculation method based on classic frequency-domain was developed to incorporate damping plates and validated with the experiment results in no disk and with disk conditions. When pitch response of Type-B and Type-C were compared with respect to Type-A, it was found that the spar platform with damping plates reduced the pitch response in most wave frequencies.

A coaxial counter-rotating rotor (CCRR), which has two rotors rotating in the opposite directions on the same axis, is seen as a promising technology for low-cost floating tidal current/ocean current power generation using single-point mooring, as the torques of the front and rear rotors are cancelled. In the evaluation and design of such turbines, there is a need for an accurate analysis method with a low computational load that considers the strong mutual induction between the two rotors placed close together. An individual rotor mutual induction (IRMI) method was developed in this study, aiming to significantly reduce the calculation time of conventional computational fluid dynamics (CFD), considering the mutual induction that are not considered in conventional modified blade element and momentum methods. In this method, the basic characteristics of the front and rear rotors are calculated in advance using full-model CFD. In calculations for the CCRR, in addition to these individual characteristics of each rotor, the interaction between the rotors is considered using the actuator disk model CFD calculated in advance. The condition where the torques of the front and rear rotors are cancelled is determined at the same time. This method was used to analyze models in which the front and rear rotors were approximately the same diameter and placed close together (10% of the rotor diameter). A comparison with the mixing plane model CFD revealed that they agree quite well when mutual induction is considered, although both the power and thrust are overcalculated when it is ignored. The simulation time of the IRMI would be almost counter-proportional to the numbers of TSR conditions to solve as compared with the CFD with the MP model.

Typical large scale pitch-controlled wind turbines idle their rotors during storm conditions. The design loads of wind turbines are calculated by aeroelastic simulations under various conditions. These include grid loss and failures, which can increase rotor speed and excite the first-mode of the tower bending. In this study, the influences of self-excitation by the idling rotor on the ultimate loads in storm conditions were investigated. Aeroelastic simulations were conducted for a three-bladed 5 MW upwind turbine as an example, under steady and extreme turbulent wind conditions according to the international design standard IEC61400-1 ed.4. As a result, we confirmed that yaw misalignment increases the idling rotor speed and 6P, second order harmonics of blade passing frequency, excites the first-mode tower bending, which can generate a large load on the tower. Pitch stick can increase the rotor speed but not as noticeably as yaw error. Although no clear provisions exist in wind turbine design standards or guidelines for the self-excited vibration during wind turbine idling, these results indicate that conditions must be set that consider self-excited vibration.

Many vertical-axis wind turbines (VAWTs) require arms, which generally provide aerodynamic resistance, to connect the main blades to the rotating shaft. Three–dimensional numerical simulations were conducted to clarify the effects of a gap placed at the blade–arm connection portion on VAWT performance. A VAWT with two straight blades (diameter: 0.75 m, height: 0.5 m) was used as the calculation model. Two horizontal arms were assumed to be connected to the blade of the model with or without a gap. A cylindrical rod with a diameter of 1 or 5 mm was installed in the gap, and its length varied from 10 to 30 mm. The arm cross section has the same airfoil shape (NACA 0018) as the main blade; however, the chord length is half (0.04 m) that of the blade. The simulation shows that the power of the VAWT with gaps is higher than that of the gapless VAWT. The longer gap length tends to decrease the power, and increasing the diameter of the connecting rod amplifies this decreasing tendency. Providing a short gap at the blade–arm connection and decreasing the cross–sectional area of the connecting member is effective in increasing VAWT power.

In recent years, the increasing environmental problems, especially the issue of global warming, have motivated demand for a cleaner, more sustainable, and economically viable energy source. In this context, wind energy plays a significant role due to the small negative impact it has on the environment, which makes it among the most widespread potential sustainable renewable fuel nowadays. However, wind turbine control systems are important factors in determining the efficiency and cost-effectiveness of a wind turbine (WT) system for wind applications. As wind turbines become more flexible and larger, it is difficult to develop a control algorithm that guarantees both efficiency and reliability as these are conflicting objectives. This paper reviews various control strategies for the three main control systems of WT, which are pitch, torque, and yaw control, in different operational regions considering multi-objective control techniques. The different control algorithms are generally categorized as classical, modern (soft computing) and artificial intelligence (AI) for each WT control system. Modern and soft computing techniques have been showing remarkable improvement in system performance with minimal cost and faster response. For pitch and yaw systems, soft computing control algorithms like fuzzy logic control (FLC), sliding mode control (SMC), and maximum power point tracking (MPPT) showed superior performance and enhanced the WT power performance by up to 5% for small-scale WTs and up to 2% for multi-megawatt WTs. For torque control systems, direct torque control (DTC) and MPPT AI-based techniques were suitable for reducing generator torque fluctuations and estimating the torque coefficient for different wind speed regions. Classical control techniques such as PI/PID resulted in poor dynamic response for large-scale WTs. However, to improve classical control techniques, AI algorithms could be used to tune the controller’s parameters to enhance its response, as a WT is a highly non-linear system. A graphical abstract is presented at the end of the paper showing the pros/cons of each control system category regarding each WT control system.

In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone dynamics were wrapped in Matlab. The proposed controller was tested by simulating the tracking of a 3D helical reference trajectory, and its efficiency was evaluated in terms of numerical performance and tracking accuracy. The results showed that the proposed controller leads to faster computational times, approximately 20 times faster than the Matlab toolbox (nlmpc), and provides better tracking accuracy than both the Matlab toolbox and classical PID controller. The robustness of the proposed control algorithm was also tested and verified under model uncertainties and external disturbances, demonstrating its ability to effectively eliminate tracking errors.

Diabetic retinopathy (DR) is a major cause of blindness worldwide and may be non-proliferative (NPDR) or proliferative (PDR). To investigate the metabolomic and lipidomic characteristics of plasma in DR patients, plasma samples were collected from patients with type 2 diabetes mellitus (DR group) with PDR (n = 27), NPDR (n = 18), or no retinopathy (controls, n = 21). Levels of 54 and 41 metabolites were significantly altered in the plasma of DR patients under positive and negative ion modes, respectively. By subgroup analysis, 74 and 29 significantly changed plasma metabolites were detected in PDR patients compared with NPDR patients under positive and negative ion modes, respectively. KEGG analysis indicated that pathways such as biosynthesis of amino acids and neuroactive ligand-receptor interaction were among the most enriched pathways in altered metabolites in the DR group and PDR subgroup. Moreover, a total of 26 and 41 lipids were significantly changed in the DR group and the PDR subgroup, respectively. The panel using the 29-item index could discriminate effectively between diabetic patients with and without retinopathy, and the panel of 22 items showed effective discrimination between PDR and NPDR. These results provide a basis for further research into the therapeutic targets associated with these metabolite and lipid alterations.

Bacterial endophthalmitis is an intraocular infection that causes rapid vison loss. Pathogens can infect the intraocular space directly (exogenous endophthalmitis (ExE)) or indirectly (endogenous endophthalmitis (EnE)). To identify predictive factors for the visual prognosis of Japanese patients with bacterial endophthalmitis, we retrospectively examined the bacterial endophthalmitis characteristics of 314 Japanese patients and performed statistics using these clinical data. Older patients, with significantly more severe clinical symptoms, were prevalent in the ExE group compared with the EnE group. However, the final best-corrected visual acuity (BCVA) was not significantly different between the ExE and EnE groups. Bacteria isolated from patients were not associated with age, sex, or presence of eye symptoms. Genus Streptococcus, Streptococcus pneumoniae, and Enterococcus were more prevalent in ExE patients than EnE patients and contributed to poor final BCVA. The presence of eye pain, bacterial identification, and poor BCVA at baseline were risk factors for final visual impairment.

The diffuser total efficiency was formulated and defined based on the generalized actuator disc model for the index of the efficiency of the diffuser-alone of the diffuser-augmented wind turbines. An optimization method to maximize the diffuser total efficiency was developed using a genetic algorithm and axisymmetric computational fluid dynamics. A case study was conducted for a 10% chord-to-diameter ratio, 2% thickness-to-chord plate, and the crest position at 50% chord of the diffuser. The optimal result showed a diffuser total efficiency of 1.087. Furthermore, 1392 (=48 population × 29 generations) simulation cases of the optimization process showed that high diffuser total efficiency appears at a low-drag coefficient, high-lift coefficient, and 15–25% low diffuser height-to-chord ratio.

A dynamic stall model for tower shadow effects is developed for downwind turbines. Although Munduate’s model shows good agreement with a 1.0 m wind tunnel test model, two problems exist: (1) it does not express load increase before the entrance of the tower wake, and (2) it uses the empirical tower wake model to determine the wind speed profile behind the tower. The present research solves these problems by combining Moriarty’s tower wake model and the entrance condition of the tower wake. Moriarty’s model does not require any empirical parameter other than tower drag coefficient and it expresses positive wind speed around the tower also. Positive wind speed change is also allowed as the tower wake entrance condition in addition to the negative change observed in the previous model. It demonstrates better agreement with a wind tunnel test and contributes to the accuracy of the fatigue load, as it expresses a slight increase in load around the entrance of the tower wake. Furthermore, the scale effects are also evaluated; lift deviation becomes smaller as the scale increases, i.e., lower rotor speed.

Kites can be used to harvest wind energy with substantially lower material and environmental footprints and a higher capacity factor than conventional wind turbines. In this paper, we present measurement data from seven individual tow tests with the kite system developed by Kyushu University. This system was designed for 7 kW traction power and comprises an inflatable wing of 6 m2 surface area with a suspended kite control unit that is towed on a relatively short tether of 0.4 m by a truck driving at constant speed along a straight runway. To produce a controlled relative flow environment, the experiment was conducted only when the background wind speed was negligible. We recorded the time-series of 11 different sensor values acquired on the kite, the control unit and the truck. The measured data can be used to assess the effects of the towing speed, the flight mode and the lengths of the control lines on the tether force.

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system identification algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system identification algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system identification algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system identification algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response.

Complex terrain can influence wind turbine wakes and wind speed profiles in a wind farm. Consequently, predicting the performance of wind turbines and energy production over complex terrain is more difficult than it is over flat terrain. In this preliminary study, an engineering wake model, that considers acceleration on a two-dimensional hill, was developed based on the momentum theory. The model consists of the wake width and wake wind speed. The equation to calculate the rotor thrust, which is calculated by the wake wind speed profiles, was also formulated. Then, a wind-tunnel test was performed in simple flow conditions in order to investigate wake development over a two-dimensional hill. After this the wake model was compared with the wind-tunnel test, and the results obtained by using the new wake model were close to the wind-tunnel test results. Using the new wake model, it was possible to estimate the wake shrinkage in an accelerating two-dimensional wind field.

Most vertical axis wind turbines (VAWTs) need arms connecting the blades with the rotational axis. The arms increase the power loss of VAWTs; however, the distribution between the pressure and friction influences and their degrees of influence have not yet been investigated in detail in past research. We applied computational fluid dynamics (CFD) targeting a small-sized straight-bladed VAWT to elucidate the effects of arms on turbine performance. In the analysis, three kinds of arms with different cross-sections (NACA 0018 airfoil, 18% rectangular, circular) with the same height were added to an armless rotor. The tangential forces and resistance torques caused by the added arms were recalculated by dividing the pressure and friction influences based on the surface pressure and friction distributions obtained by the CFD on an arm or a blade. The pressure-based tangential force of an arm, regardless of the cross-section, had a tendency to increase near the connection part between the arm and a blade. Though the value was small, the friction on the rectangular arm generated a driving force, whereas the friction on the other arms generated resistance forces. The pressure-based tangential force of a blade increased for a wide region around the connection part. The friction-based tangential force of a blade dropped around the connection part of every arm-equipped rotor. The arm resistance torque added to a VAWT by the existence of arms was larger than the added blade resistance torque in the cases of rectangular and circular arm rotors. Conversely, in the case of the airfoil arm rotor, the resistance torque added to blades became larger than that of arms.

Multi-rotor system (MRS) wind turbines can be a competitive alternative to large-scale wind turbines. In order to address the structural behavior of the turbine tower, an in-house aeroelastic tool has been developed to study the dynamic responses of a 2xNREL 5MW twin-rotor configuration wind turbine. The developed tool has been verified by comparing the results of a single-rotor configuration to a FAST analysis for the same simulation conditions. Steady flow and turbulent load cases were investigated for the twin-rotor configuration. Results of the simulations have shown that elasticity of the tower should be considered for studying tower dynamic responses. The tower loads, and deformations are not straightforward with the number of rotors added. For an equivalent tower, an additional rotor will increase the tower-top deflection, and the tower-base bending moment both in the fore-aft direction will be more than doubled. The tower torsional stiffness becomes a crucial factor in the case of a twin-rotor tower to avoid a severe torsional deflection. Tower natural frequencies are dominant over the flow conditions in regards to the loads and deflections.

A new analysis method to calculate the rotor-induced average tower drag of downwind turbines in the blade element momentum (BEM) method was developed in this study. The method involves two parts: calculation of the wind speed distribution using computational fluid dynamics, with the rotor modeled as a uniform loaded actuator disc, and calculation of the tower drag via the strip theory. The latter calculation considers two parameters, that is, the decrease in wind speed and the pressure gradient caused by the rotor thrust. The present method was validated by a wind tunnel test. Unlike the former BEM, which assumes the tower drag to be constant, the results obtained by the proposed method demonstrate much better agreement with the results of the wind tunnel test, with an accuracy of 30%.

In recent years, the SPM (Single-Point Mooring) concept has been widely employed in several branches of the naval architecture and marine engineering field, such as FPSOs (Floating Production, Storage and Offloading units), offshore oil rigs, etc., but not yet popular in the offshore wind energy. To investigate the response characteristics of an SPM-moored FWT (Floating Wind Turbine), in the present work, we perform a numerical study on the DeepCwind semisubmersible wind turbine, using the state-of-the-art open-source tool FAST. The free-decay test results show that the SPM layout affects the natural periods of the wind turbine in rotational modes, as well as the mooring stiffness of the diagonal rotational and crossing rotational-translational terms, especially in relation to the yaw mode. Comparisons of the RAOs (Response Amplitude Operators) elucidate that the presence of wind influences significantly the sway, roll and yaw motions of the SPM layout. Finally, the weathervane test shows that an asymmetry exists in the free-yaw motion response when the semisubmersible wind turbine is moored by an SPM system.

Downwind rotors are a promising concept for multi-megawatt scale large wind turbines due to their advantages in safety and cost reduction. However, they have risks from impulsive loads when one of the blades passes across the tower wake, where the wind speed is lower and locally turbulent. Although the tower shadow effects on the tower loads have been discussed in former studies, there is currently no appropriate model for the blade-element and momentum theory so far. This study formulates the tower shadow effects on the tower load variation induced by blades using the lifting line theory, which does not require any empirical parameters. The method is verified via computational fluid dynamics for a 2 MW(megawatt), 3-bladed downwind turbine. The amplitude and the phase of the variation are shown to be accurate in outboard sections, where the rotor-tower clearance is large (>3.0 times of the tower diameter) and the ratio of the blade chord length is small (

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters in the coastal region, etc. A higher-order scheme is used to discretize geometry of the structure as well as the physical wave potentials. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss–Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve its computation efficiency, an analytical solution is derived for a fast evaluation of the Green function that needs to be implemented thousands of times. In addition, the OpenMP parallelization technique is applied to the formation of the influence coefficient matrix, significantly reducing the running CPU time. Computations are performed on wave-exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates a good performance of the present method.

A multi-rotor system (MRS), in which multiple wind turbines are placed on one tower, is a promising concept for super-large wind turbines at over 10 MW due to the cost and weight advantages. The coherence effects on an MRS were investigated in this study. Although a wide range of coherences were measured so far, a decay constant of C = 12 is recommended in the IEC61400-1 standard. Dynamic simulations were performed for a 14-MW MRS, which consists of seven 2-MW turbines and includes wind models with three different coherences. Although the results show that a larger coherence increases the output power and the collective loads due to tower base fore-aft bending, it reduces the differential loads due to tower-base torque and tower-top nodding. The most significant case is the fatigue damage due to tower base fore-aft bending, which was more than doubled between the decay constants of C = 6 and C = 12. The present results indicate that the coherence should be defined carefully in the design of large-scale MRSs because its effect on them is not straightforward.