scholarly journals Machine Learning-Aided Assessment of Wind Turbine Energy Losses due to Blade Leading Edge Damage

2019 ◽  
Author(s):  
Anna Cavazzini ◽  
Edmondo Minisci ◽  
M. Sergio Campobasso
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Leading Edge ◽  
Surface Damage ◽  
Energy Losses ◽  
Edge Damage ◽  
Blade Leading Edge

Abstract Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the frequent case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate wind turbine annual energy production losses due to blade leading edge damage. The power curve of a turbine with nominal or damaged blade surfaces is determined respectively with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, consists of 2700+ airfoil geometries, each analyzed with Navier-Stokes computational fluid dynamics over the working range of angles of attack. To avoid the need for lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results focus on the analysis of a utility-scale offshore wind turbine and demonstrate that realistic estimates of the annual energy production loss due to leading edge surface damage can be obtained in just a few seconds using a standard desktop computer, highlighting the viability and the industrial impact of this new technology for wind farm energy losses due to blade erosion.

scholarly journals Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
M. Sergio Campobasso ◽  
Anna Cavazzini ◽  
Edmondo Minisci
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Leading Edge ◽  
Prototype System ◽  
Desktop Computer ◽  
Edge Delamination ◽  
Blade Leading Edge

Abstract Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes, and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier–Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion.

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part II: Implementation With Machine Learning

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Three Dimensional ◽  
Leading Edge ◽  
Cfd Modeling ◽  
Inlet Temperature ◽  
Training Set ◽  
Simplified Approach ◽  
Flow Mixing

Abstract Reynolds based thermo-elasto-hydrodynamic (TEHD) simulations of tilting pad journal bearings (TPJBs) generally provide accurate results; however, the uncertainty of the pad’s leading edge thermal boundary conditions causes uncertainty of the results. The highly complex thermal-flow mixing action between pads (BPs) results from the oil supply nozzle jets and geometric features. The conventional Reynolds approach employs mixing coefficients (MCs), estimated from experience, to approximate a uniform inlet temperature for each pad. Part I utilized complex computational fluid dynamics (CFD) flow modeling to illustrate that temperature distributions at the pad inlets may deviate strongly from being uniform. The present work retains the uniform MC model but obtains the MC from detailed three-dimensional CFD modeling and machine learning, which could be extended to the radially and axially varying MC case. The steps for implementing an artificial neural network (ANN) approach for MC regression are provided as follows: (1) utilize a design of experiment step for obtaining an adaptable training set, (2) conduct CFD simulations on the BP to obtain the outputs of the training set, (3) apply an ANN learning process by Levenverg–Mardquart backpropagation with the Bayesian regularization, and (4) couple the ANN MC results with conventional TEHD Reynolds models. An approximate log fitting method provides a simplified approach for MC regression. The effectiveness of the Reynolds TEHD TPJB model with ANN regression-based MC distributions is confirmed by comparison with CFD based TEHD TPJB model results. The method obtains an accuracy nearly the same as the complete CFD model, but with the computational economy of a Reynolds approach.

Distributed wind resource assessment: Comparing measured annual energy production with predictions from computational fluid dynamics

2019 ◽  
Vol 43 (6) ◽  
pp. 657-672
Author(s):  
Devon L Martindale ◽  
Thomas L Acker
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Resource Assessment ◽  
Energy Output ◽  
Wind Resource Assessment ◽  
Production Values ◽  
The Us ◽  
Wind Resource

The US Department of Energy’s Distributed Wind Resource Assessment Workshop identified predicting the annual energy production of a kilowatt-sized wind turbine as a key challenge. This article presents the methods and results for predicting the annual energy production of two 2.1 kW Skystream 3.7 wind turbines using computational fluid dynamics, in this case Meteodyn WT. When compared with actual production data, annual energy production values were uniformly underpredicted, with errors ranging from 1% to in excess of 30%, depending on the solver settings and boundary conditions. The most accurate of the simulations with errors consistently less than 10% were achieved when using recommended solver settings of neutral atmospheric stability, and roughness values derived from the US National Land Cover Database. The software was used to create an annual energy production map for the modeling domain, which could be a valuable tool in estimating the energy output and economic value of a proposed wind turbine.

scholarly journals Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion

2021 ◽  
Vol 245 ◽  
pp. 114567
Author(s):  
Lorenzo Cappugi ◽  
Alessio Castorrini ◽  
Aldo Bonfiglioli ◽  
Edmondo Minisci ◽  
M. Sergio Campobasso
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Leading Edge ◽  
Energy Yield ◽  
Yield Losses ◽  
Blade Leading Edge

Computational Analysis of Symmetric and Cambered Blade Darrieus Vertical Axis Wind Turbine With Bio-Mimicked Blade Design

2022 ◽  
pp. 225-240
Author(s):  
Punit Prakash ◽  
Praveen Laws ◽  
Nishant Mishra ◽  
Santanu Mitra
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Leading Edge ◽  
Vertical Axis ◽  
Symmetric Model ◽  
Blade Design ◽  
Vertical Axis Wind Turbine ◽  
2D Simulation ◽  
Low Performance

Vertical axis wind turbine suffers from low performance, and the need for improvement is a challenge. This work addresses this problem by using computational fluid dynamics. This chapter aims to analyze and compare symmetric and cambered Darrieus turbine. These analyses are usually carried for straight leading-edge blades, and cambered resembles more the natural shape of the wing of birds and other aquatic mammals, which helps them generate extra lift during movement. Moreover, recent studies suggest better performance was observed for NACA0018 symmetric aerofoil blades, and a similar trend has been observed for NACA2412 cambered aerofoil profiles. Turbine models having symmetric NACA0018 and cambered NACA2412 profiles have been studied. By comparing the symmetric model with cambered blade models, differences in coefficient of torque have been presented. OpenFOAM is used for performing the 2D simulation with dynamicOverset-FvMesh for motion solver with overset mesh method. Meshed geometry was constructed with GMSH codes and the simulation uses overPimpleDyMFoam algorithm as a solver.

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