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Wind turbine blade leading edge erosion reduces the lift and increases the drag of the blade airfoils. This occurrence, in turn, reduces turbine power and energy yield. This study focuses on the aerodynamic analysis of large and sparse erosion cavities, observed in intermediate to advanced erosion stages, whose size and surface pattern do not lend themselves to experimental and numerical analysis by means of distributed roughness models alone. Making use of three‐dimensional Navier‐Stokes computational fluid dynamics enhanced by laminar‐to‐turbulent transition modeling, and geometrically resolving individual erosion cavities, the study validates this simulation‐based approach for predicting the aerodynamics and performance loss of blade sections featuring the aforementioned erosion cavities against available experimental data. It is found that the considered cavities can trigger transition, indicating the necessity of both resolving their geometry in the simulations and also modeling distributed surface roughness, of typically lower level, as this latter affects the properties of boundary layers and, if sufficiently high, may trigger transition over the entire spanwise length affected. The energy yield loss of a utility‐scale turbine due to the considered erosion pattern is found to be between 2.1% and 2.6% using measured and computed force data of the nominal and eroded outboard blade airfoil. A parametric analysis of the cavity geometry suggests that the geometry of the cavity edge has a much larger impact on aerodynamic performance than the cavity depth.

This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2‐D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

Blade leading edge erosion is acknowledged to significantly reduce the energy yield of wind turbines. The problem is particularly severe for offshore installations, due to faster erosion progression boosted by harsh environmental conditions. This study presents and demonstrates an experimentally validated simulation-based technology for rapidly and accurately estimating wind turbine energy yield losses due to general leading edge erosion. The technology combines the predictive accuracy of two- and three-dimensional Navier-Stokes computational fluid dynamics with the runtime reductions enabled by artificial neural networks and wind turbine engineering codes using the blade element momentum theory. The main demonstration is based on the assessment of the annual energy yield of the National Renewable Energy Laboratory 5 MW reference turbine affected by leading edge erosion damage of increasing severity, considering damages based on available laser scans and previous leading edge erosion analysis. Results also include sensitivity studies of the energy loss to the wind characteristics of the installation site. It is found that the annual energy loss varies between about 0.3 and 4%, depending on the damage severity and the site wind characteristics. The study also illustrates the necessity of resolving the geometry of eroded leading edges rather than accounting for the effects of erosion with surrogate models, since, after an initial increase of distributed surface roughness, erosion yields leading edge geometry alterations causing aerodynamic losses exceeding those due to the loss of boundary layer laminarity consequent to roughness-induced transition. The presented technology enables estimating in a few minutes the amount of energy lost to erosion for many-turbine wind farms, and offers a key tool for predictive maintenance.

We present an unsteady simulation of an axial fan with low pressure and high flow rate, developed during the MinWaterCSP EU project, specifically designed and optimized for Concentrating Solar Power (CSP) plants with the aim of reducing water consumption at the condenser stage. An earlier version of the fan, referred as M-fan, was initially developed to serve the hybrid cooling system, to boost the overall heat transfer performance with dry cooling. In this work, we will performing a Computational Fluid Dynamics (CFD) analysis of the unsteady flow on a redesigned and scaled version of the M-fan, referred as T-fan, by The T-fan was designed and optimized to reduce noise emissions and improve efficiency with respect to the M-fan. We perform the simulation using an in-house built C++ parallel code for CFD analysis using Finite Elements (FE). We model the flow dynamics using the Residual Based Variational MultiScale method (RBVMS) to obtain a stabilized solution of the Navier-Stokes equation using FE discretization. We will discuss the simulation results of the RBVMS model by comparing our simulation of the T-fan with experimental results on the same fan.

The detailed prediction of the upcoming wind on wind farms can support optimization of wind energy production and operation and maintenance. Numerical Weather Prediction (NWP) tools allow to simulate the wind over long-term forecasting horizons (up to several days) with a spatial resolution ranging between the continental level down to a few hundred meters. We present a methodology, based upon Computational Fluid Dynamics (CFD) and Reynolds Averaged Navier Stokes (RANS) modelling, that allows to downscale the spatial resolution of the wind prediction supplied by a NWP model down to the typical length-scale of wind energy applications. The proposed approach combines a number of standard tools, including: Geographical Information Systems (GIS), Advanced Research - Weather Research and Forecasting (WRF-ARW) and OpenFOAM, and proposes methods to interface these tools and set-up the local-scale simulation. Models and problem sizes are selected to keep the computational cost of the system sustainable in view of its implementation in operational forecasting. Finally, we present the application of the method on a given onshore site, and for three different meteorological conditions, showing the potential of the approach, but also giving an account of the limitations that it may encounter when dealing with complex planetary boundary layers.

Fans used in tunnel ventilation operate for decades in an atmosphere that carries dust, soot, and other solid particles. The formation of deposit on the rotor blades, considering a so long time of exposition to this particle-laden flow, is highly probable. A not negligible quantity of deposited material can produce damages on the performance of the fan, but also mass unbalancing, which is potentially dangerous for the structural integrity of the fan components. We applied our simulation tool to study a case of deposition on a large axial fan blade, used for tunnel ventilation. The outcome of the study is a parametric map of fouled blade geometries, obtained by simulating the deposition process over the increasing quantity of ingested particles mixture. The final map correlates the level and shape of deposit to the overall amount of particle ingested by the fan in its operating life. The same map can be easily used to predict the time needed in a specific application to reach any specific deposit thickness. The evolution algorithm and simulation tools developed in the past years by the authors was applied to predict the modified geometry of eroded rotor blades. Here the same framework is updated to simulate the deposit problem. We use an integrated multiphase solver, coupled with a geometry update method. The solver can iteratively simulate the flow field, compute the particle tracking, dispersion and deposit processes, and modify the geometry (and mesh) according to the predicted deposit shape and rate.

In this paper a preliminary design and a 2D computational fluidstructure interaction (FSI) simulation of a flexible blade for a Wells turbine is presented, by means of stabilized finite elements and a strongly coupled approaches for the multi-physics analysis. The main objective is to observe the behaviour of the flexible blades, and to evaluate the eventual occurrence of aeroelastic effects and unstable feedbacks in the coupled dynamics. A series of configurations for the same blade geometry, each one characterized by a different material and mechanical properties distribution will be compared. Results will be given in terms of total pressure difference, supported by a flow survey. The analysis is performed using an in-house build software, featured of parallel scalability and structured to easy implement coupled multiphysical systems. The adopted models for the FSI simulation are the Residual Based Variational MultiScale method for the Navier-Stokes equations, the Total Lagrangian formulation for the nonlinear elasticity problem, and the Solid Extension Mesh Moving technique for the moving mesh algorithm.

Computational analysis of particle-laden-airflow erosion can help engineers have a better understanding of the erosion process, maintenance and protection of turbomachinery components. We present an integrated method for this class of computational analysis. The main components of the method are the residual-based Variational Multiscale (VMS) method, a finite element particle-cloud tracking (PCT) method with ellipsoidal clouds, an erosion model based on two time scales, and the Solid-Extension Mesh Moving Technique (SEMMT). The turbulent-flow nature of the analysis is addressed with the VMS, the particle-cloud trajectories are calculated based on the time-averaged computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the target-geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated based on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the PCT computation. As the target geometry evolves, the mesh is updated with the SEMMT. We present a computation designed to match the sand-erosion experiment we conducted with an aluminum-alloy target. We show that, despite the problem complexities and model assumptions involved, we have a reasonably good agreement between the computed and experimental data.

Wind-turbine blade rain and sand erosion, over long periods of time, can degrade the aerodynamic performance and therefore the power production. Computational analysis of the erosion can help engineers have a better understanding of the maintenance and protection requirements. We present an integrated method for this class of computational analysis. The main components of the method are the streamline-upwind/Petrov–Galerkin (SUPG) and pressure-stabilizing/Petrov–Galerkin (PSPG) stabilizations, a finite element particle-cloud tracking method, an erosion model based on two time scales, and the solid-extension mesh moving technique (SEMMT). The turbulent-flow nature of the analysis is handled with a Reynolds-averaged Navier–Stokes model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated in different ways depending on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the fluid–particle simulation. As the blade geometry evolves, the mesh is updated with the SEMMT. We present computational analysis of rain and sand erosion for a wind-turbine blade strip, including a case with actual rainfall data and experimental aerodynamic data for eroded airfoil geometries.

Wind-turbine blades exposed to rain can be damaged by erosion if not protected. Although this damage does not typically influence the structural response of the blades, it could heavily degrade the aerodynamic performance, and therefore the power production. We present a method for computational analysis of rain erosion in wind-turbine blades. The method is based on a stabilized finite element fluid mechanics formulation and a finite element particle-cloud tracking method. Accurate representation of the flow would be essential in reliable computational turbomachinery analysis and design. The turbulent-flow nature of the problem is dealt with a RANS model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the flow field and closure models for the turbulence–particle interaction, and one-way dependence is assumed between the flow field and particle dynamics. The erosion patterns are then computed based on the particle-cloud data.

Numerical tools aiming at the evaluation of ground acoustic impact of helicopters typically rely on databases given in terms of acoustic disturbance over hemispheres surrounding the helicopter (noise hemispheres). These are evaluated for a discrete number of steady flights falling within the flight envelope. The objective of the present work is the identification of flight parameters to be considered for the characterization of noise hemispheres, particularly when related to unsteady maneuvers. To this purpose, different approaches based on steady flight aeroelastic/aerodynamic/aeroacoustic predictions are examined for assessing their capability of simulating the acoustic impact of helicopters in arbitrary unsteady flight. The numerical investigation demonstrates that at least three parameters, including disk loading, are required to adequately characterize noise hemispheres. Conversely, the similarity of kinematic parameters alone may yield steady flight acoustic predictions poorly correlated with those obtained for unsteady maneuvers.