Wind Turbine Simulator Research Articles

Internal flow effect of a flexible riser system for a floating offshore wind turbine with on-board carbon dioxide capture

This paper designs a flexible riser for transporting carbon dioxide (CO2) off a floating offshore wind turbine (FOWT)-powered CO2 capture platform, and analyzes the internal flow-induced effects caused by the CO2 on the flexible riser. Internal effects on flexible risers due to the pressurized and internal dynamic flow are a well-studied problem in offshore oil and gas (O&G) applications, which typically requires the use of tools capable of representing their pressurized contents and flows. However, because the flow rates and pressure conditions expected from individual FOWT-CO2 capture platforms are much lower than those used in O&G installations, we studied the importance of internal flow effects on riser dynamics. To determine their relevance, we designed and modelled the flexible riser in OrcaFlex™ with different design pressure and flow conditions under normal and extreme environmental events. The results indicate that the riser’s effective tension and curvature are not significantly affected by internal flow effects, but differences were observed in the von Mises stress arising from the shear stress, which is a purely hydrostatic term. As such, as long as the shear term is properly accounted for, these results enable future work to utilize simplified models for the flexible riser system, similar to models for dynamic power cables employed in FOWT farms. This simplification allows us to design and analyze the whole FOWT-CO2 system alternatively with lower fidelity and open-source offshore wind turbine simulation tools, like OpenFAST, without overlooking relevant riser-dynamics phenomena.

Aeroelastic Simulation of Full-Machine Wind Turbines Using a Two-Way Fluid-Structure Interaction Approach

Two-way fluid–structure interaction (FSI) simulation of wind turbines has gained significant attention in recent years due to the growth of offshore wind energy development. Strong coupling procedures in these simulations predict realistic behavior with higher accuracy but result in increased computational costs and potential numerical instabilities. This paper proposes a mixed weak and strong coupling approach for the FSI simulation of a 5 MW wind turbine. The deformation of the turbine blade is calculated using a weak coupling approach, ensuring blade deflection meets a convergence criterion before rotating to the next azimuthal position. Fluid and solid solvers are partitioned, utilizing the commercial software packages STAR-CCM+ and Abaqus, respectively. Flexible and rigid blade cases are modeled, and the calculated loads, power, and blade tip displacement for the rotor at a constant rotating speed are compared. The proposed model is validated, showing good agreement with the existing literature and results comparable to those from another validated wind turbine simulator. The effect of rotor–tower interaction is evident in the results. Based on our calculations, the power production of flexible blades is evaluated to be 9.6% lower than that of rigid blades.

A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.

Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements

Abstract. Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.

Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines

Abstract Structural health monitoring in floating offshore wind turbines’ mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions’ stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM’s capability in detecting the simulated wind turbine’s faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method’s effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.

Three-dimensional simulation of a floating wind turbine platform

Abstract As the need for renewable energy has grown in recent years, the possibility of creating and collecting deep-sea wind energy has become a research hotspot. Floating wind turbines need platforms to provide a stable working state and structural safety. This article focuses on the study of the dynamics of the deep-water semi-submersible floating platform OC4-DeepCwind. The open-source computational fluid dynamic toolbox OpenFOAM is used for comparative analysis of the floating structure motion response. In order to eliminate the influence of grid size and refinement level on the simulation results, a grid refinement study was conducted before detailed hydrodynamic discussions. In absence of wind, the dynamic response of the platform is studied under the influence of the impact with regular waves with different amplitudes. The effect of the presence of the tower supporting the wind turbine on the dynamics of the platform is investigated as well. Results show that the addition of the tower exerted a slight destabilising influence on the support’s dynamic behaviour, confirming its stability against different wave height. Finally, the model has demonstrated its efficacy as a highly valuable tool for the simulation of wind turbines on floating foundations.

High-Precision Numerical Investigation of a VAWT Starting Process

For both conventional and renewable energy conversion processes, computational fluid dynamics (CFD) has been used to address more energy-related challenges in recent decades. Using CFD to investigate vertical-axis wind turbines has become more common in recent years. The main goals of this application have been to more accurately predict the turbine’s performance and to comprehend the complicated nature of the complex turbulent flow. The vertical-axis wind turbine (VAWT) simulation for energy-generating applications has several intricate components. One of them is the study of the chaotic flow that occurs during the first stages of the starting process, and which greatly influences overall effectiveness. In this article, the performance of the wind turbine was increased using a passive flow control approach. The numerical research was carried out using Large Eddy Simulation for four alternative tip speed ratios in both cases, the classic and the optimized case, equipped with a vortex trap on the extrados of the blades. The power and torque coefficient variations, as well as the velocity magnitude contours, show that the starting process may begin with a significant improvement in efficiency when flow control is used.

Combined genetic algorithm and response surface methodology‐based bi‐optimization of a vertical‐axis wind turbine numerically simulated using CFD

AbstractIn this study, computational fluid dynamic (CFD) simulation of a vertical axis wind turbine (VAWT) geometry based on the Unsteady Reynolds–Averaged Navier–Stokes equations was investigated. In addition, the relationship between the geometric parameters of the VAWT and the two response variables, that is, moment and lift force, was determined using response surface methodology (RSM). Then, the Non‐Dominated Sorting Genetic Algorithm (NSGA‐II) was used to solve the multi‐objective optimization problem. The results obtained from the RSM showed that the lift force of the turbine is more sensitive to the change in the blade chord length, and the output moment of the turbine is more sensitive to the change in the rotor radius. Using the NSGA‐II multi‐objective optimization algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, it was determined that among the optimal values of the independent variable, the most optimal response occurs in blade chord length = 0.18 m, rotor radius = 0.4 m, blade pitch angle = −3.27° and number of blades = 4. In these optimal values of the independent variables, the values of the dependent variables, which included the turbine's moment and the blades’ lift force, were obtained as 9.58 N m and 57.89 N, respectively.

Assessing wind turbine blade durability longevity utilizing national renewable energy laboratory tools

With the escalating significance of renewable energy, there exists a pressing demand for precise software and tools capable of designing and forecasting wind turbine blade service life. OpenFAST, TurbSim, and MLife are pivotal open-source wind turbine simulation tools that integrate aerodynamics, structural dynamics, wind simulation, and control systems. They enable accurate prediction of wind turbine performance. This study aims to leverage these software resources to model turbulent wind conditions using predefined basic parameters and then calculate fatigue and longevity of the wind turbine blade to determine service life. Particularly, MLife was employed for rose loading, enhancing the fidelity of the loading cycle simulation. Collaboration between the National Renewable Energy Laboratory and the research team played a crucial role in establishing a foundational baseline and implementing realistic input parameters for evaluating the ultimate load, determining damage equivalent loads (DEL), and projecting service life. In all simulations, a constant factor of safety (FOS) of 1.5 was consistently applied. Additionally, Finite Element Analysis (FEA) was employed to validate our numerical experiments, ensuring the accuracy of the obtained results by correlating simulation outcomes with real-world scenarios, further strengthening the credibility of the study.

Investigation of blade flexibility effects on the loads and wake of a 15 MW wind turbine using a flexible actuator line method

Abstract. This paper investigates the impact of blade flexibility on the aerodynamics and wake of large offshore turbines using a flexible actuator line method (ALM) coupled to the structural solver BeamDyn in large-eddy simulations. The study considers the IEA 15 MW reference wind turbine in close-to-rated operating conditions. The flexible ALM is first compared to OpenFAST simulations and is shown to consistently predict the rotor aerodynamics and the blade structural dynamics. However, the effect of blade flexibility on the loads is more pronounced when predicted using the ALM compared with using the blade element momentum theory. The wind turbine is then simulated in a neutral turbulent atmospheric boundary layer with flexible and rigid blades. The significant flapwise and torsional mean displacements lead to an overall decrease of 14 % in thrust and 10 % in power compared to a rotor with no deformation. These changes influence the wake through a reduced time-averaged velocity deficit and turbulent kinetic energy. The unsteady loads induced by the rotation in the sheared wind and the turbulent velocity fluctuations are also substantially affected by the flexibility and exhibit a noticeably different spectrum. However, the influence of these load variations on the wake is limited, and the assumption of rigid blades in their deformed geometry is shown to be sufficient to capture the wake dynamics. The influence of the resolution of the flow solver is also evaluated, and the results are shown to remain consistent between different spatial resolutions. Overall, the structural deformations have a substantial impact on the turbine performance, loads, and wake, which emphasizes the importance of considering the flexibility of the blades in simulations of large offshore wind turbines.

Uncertainty quantification of structural blade parameters for the aeroelastic damping of wind turbines: a code-to-code comparison

Abstract. Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest based on a solid mathematical foundation. In the wind energy field most UQ studies focus on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2, and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross-section parameters. The code-to-code comparison shows good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.

Analyzing and Validating Energy Performance through Computational Simulation of a Helical Vertical Axis Wind Turbine

Analyzing and Validating Energy Performance through Computational Simulation of a Helical Vertical Axis Wind Turbine

Unsteady numerical simulation of wind turbine with bio-inspired wing-tip modification

This study evaluates the effects of a novel winglet design on the aerodynamics of the 10 MW Denmark Technical University wind turbine. The unsteady Reynolds-averaged Navier–Stokes (URANS) and the detached eddy simulation (DES) are used to numerically simulate the physics of both the baseline turbine (i.e., no winglet included) and a wingletted turbine under the rated operating condition. The results show that the addition of the winglet alters both the structure of the wing-tip vortex and the vorticity distribution in the wake, leading to lower levels of average vorticity. Moreover, the wingletted wind turbine increases the torque of the turbine by 6.3% while only increasing the drag by 2.5%. Although the URANS formulation performs well at calculating the power and force distribution at the turbine, it falls short of providing an accurate description of the flow field of the wake, failing to calculate the unsteady scales captured by the DES model.

A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques

There is renewed interest in floating vertical axis wind turbines (FVAWTs) as offshore wind turbines progressively increase in size and move into deeper waters. To explore the potential of large-scale FVAWTs for future commercialization, it is crucial to investigate blade deformations using an accurate and effective method. In this study, we developed a hybrid model, namely, the SVST-ANN, which integrates dynamic theory and machine learning techniques to predict blade deformations. Specifically, an artificial neural network (ANN) module is incorporated into the slack coupled vertical axis wind turbine simulation tool (SVST), which significantly reduces the total computational time. A comparative study was conducted between the SVST-ANN model and the traditional SVST model, employing a 10 MW helical-type FVAWT as an example. The results show that the SVST-ANN model can accurately and efficiently predict blade deformations. The maximum errors for the maximum value, average value, and standard deviation across all nodes are minimal, with a corresponding computational time reduction of approximately 60 %. This study provides a novel method for investigating the dynamic behavior of the FVAWTs, which is more effective for calculating the elastic deformations of blades than traditional numerical methods.

Wind Potential Assessment for Songkhla, Thailand

The analysis and evaluation of wind energy projects are important to understand their economic and environmental suitability for installation and operation. Using wind data from the Meteorological Department, Used WAsP software for calculating, simulating, and analyzing the Annual Energy Production (AEP) and then applied it to calculate the Levelized Cost of Electricity (LCOE) of wind turbines installed in the Songkhla province area. The selected areas are suitable for all three models of wind turbines in the simulation. From the wind data analysis, wind direct was found that in the east sector (sector 4), there is the highest wind frequency at the wind station. The simulation results from Laem son-on area and Hua khao area show suitable mean wind speeds for wind farm installations. In the wind turbine simulation, three models were compared: Bonus 300 kW MkIII, Bonus 1.3 MW, and PowerWind 56 900 kW. At the Songkhla cape area, the positions with the highest AEP were 132.485 MWh, 344.419 MWh, and 332.597 MWh, respectively. Hua Khao area, the positions with the highest AEP were 225.284 MWh, 586.303 MWh, and 540.045 MWh, respectively. In the LCOE calculations for all model of wind turbines, at both Laem Son- On and Hua Khao locations, the most cost-effective wind turbine is the Bonus 300 kW. It has an LCOE of $142.43/MWh and $83.76/MWh, respectively. Therefore, the most cost-effective location is Hua Khao.

Tuning the Actuator Line Method to Properly Modelling Tip Effects in Finite-Length Blades

The Actuator Line Method (ALM) is gaining popularity in wind turbine simulations, as it can better handle some of the challenging operating conditions experienced by modern machines, such as highly turbulent inflows, severe aero-elastic forcing, and complex rotor-to-rotor interactions. However, it still falls behind other medium-fidelity methods such as the Lifting Line Free Vortex Wake (LLFVW) when it comes to resolving tip vortices and their effect on the blade spanwise load profile. The reason for such behavior is still unclear. A recent study suggested that this issue can be solved by reducing the scale of the angle of attack (α) sampling and force insertion towards the tip, without the need of additional corrections. This study builds on these findings to further investigate how the ALM base formulation - in terms of α sampling and force insertion - can be tuned to properly describe tip effects. An in-house ALM tool was employed to simulate a finite, constant-chord, NACA0018 wing, for which high-fidelity blade-resolved CFD (BR-CFD) data are available as benchmark. In the first part of the work, different strategies are outlined, including a novel approach for the de-coupling of the angle of attack sampling step from the force projection one, here called DE-coupled LineAverage (DELA). Their accuracy and sensitivity to ALM numerical settings are assessed at a fixed wing pitch angle of 6°. The analysis is then extended to a wider range of blade pitch angles, benchmarking the new ALM formulation against BR-CFD, ALM with the Dağ and Sørensen correction, and LLFVW in terms of blade loads, tip vortex structure, and computational effort.

Deviation of drive torque and rotational speed of a wind turbine drivetrain between test bench and wind turbine simulations

Wind turbines are a major contributor to a clean and independent energy mix in the future and therefore need to have as little downtime as possible. Downtime can be minimised by increasing the robustness of the wind turbine. The drivetrain is one of the major contributors of downtime in wind turbines. One way of increasing the robustness of wind turbine drivetrains is extensive drivetrain testing and loop-back optimization of the design and control. On the drivetrain test bench torque and other wind loads are applied. To ensure that the drivetrain reaction to the loads is the same as of the erected wind turbine the accuracy of the application of the loads needs to be sufficient. Only if the reaction loads of the drivetrain are similar to the rotor loads of the erected wind turbine a robustness increase can be achieved via the loop-back optimization after the tests. This paper therefore quantifies the deviations occurring in torque and rotational speed of the hub between the erected wind turbine and the test bench for a specific wind turbine. To do so the results of a test bench simulation are compared to results of an aerodynamic simulation of the wind turbine. For the investigated wind turbine drivetrain, the deviation in drive torque increases slightly with higher windspeeds. The torque values are maximum 2 % higher at the wind turbine compared to the rated torque. The rotational speed values are represented very well regarding the time series as well as the occurring frequencies. The speed values deviate maximum 0.5 % compared to the rated speed. The deviations are a result of differences in an underestimation of the drivetrain efficiency in the aerodynamic simulation and the inertia of the drivetrain on the test bench and wind turbine. Since the deviations are small the abstraction losses at the test bench regarding torque and speed are acceptable.

Further improvements to the double-Gaussian wake model

In onshore wind farms, the turbine-to-turbine distance can be in the order of three diameters or less. In these circumstances, traditional wake models, developed for the far wake region, lose accuracy. The double-Gaussian wake model is an approach to extend algebraic wake modelling to the near wake region, where the wake is not yet self-similar. In this work, the double-Gaussian wake model is advanced by performing large eddy simulations of a reference wind turbine and a scaled wind turbine model to investigate how the thrust coefficient and the turbulence intensity influence the near wake and wake recovery. Results show that in the near wake the maximum velocity deficits move towards the outer part of the rotor with an increasing thrust coefficient. Thus, a thrust coefficient dependent position of the double-Gaussian peaks is proposed. In addition, a wake expansion function including turbulence intensity and thrust coefficient is tuned. The new model proves to be capable of predicting the wake of different turbine models under various ambient conditions and operational states.

Floaters upscaling for multi-megawatt floating wind turbines

The floating wind industry requires wind turbines with increasing power ratings, which will require bigger platforms. However, few floater concepts are publicly available for numerical simulation of future wind turbines. To overcome this limit, this work proposes a fast and straightforward upscaling methodology to upscale already available floating platforms to greater wind turbine power ratings. The methodology does not aim to optimize the floater design, but it can generate realistic floater data for preliminary numerical simulations. The methodology is applied here to four floaters: a semisubmersible, a barge, a TLP and a spar. The platforms are upscaled for a 15 MW wind turbine and a specific deployment site. The upscaling methodology is adapted for each floater since some differences arise from one platform to the other. Whenever possible a comparison with already existing 15 MW substructures is proposed and commented.

Sensitivity of the Prediction of Wind Turbine Wakes to the Sub-Grid Scale Model

In a large-eddy simulation (LES) approach, the sub-grid scale (SGS) model accounts for the contribution of eddies and their fluxes whose length scales are smaller than the filter width. In wind turbine and farm simulations, different SGS models have been adopted, but their impact on turbine performance and wake prediction remains unknown for non-neutrally stable atmospheric boundary layers. Here, large-eddy simulations of an NREL–5MW wind turbine in stable atmospheric conditions are performed with six SGS models: standard Smagorinsky, Lagrangian-Averaged Scale-Dependent Dynamic (LASDD), Wall-Adapting Local Eddy-Viscosity, Turbulent Kinetic Energy, Stability Dependent Smagorinsky, and Anisotropic Minimum-Dissipation (AMD) models. The resolved flow field and turbine loading have shown limited sensitivity to the SGS model with some deviations from the LASDD in wind speed and turbulence intensity at the turbine elevation. This limited sensitivity is owed to the adopted high-resolution grid necessary to provide an acceptable resolution for the actuator-line method. Regarding the computational costs, the LASDD model has the highest compute overhead to the LES compared to the other five SGS models. The AMD model is simple to implement and provides three-dimensional variation of the SGS eddy-viscosity without any parameter tuning, thus it has the highest potential to be used in LES of wind turbines and farms operating in stable conditions.