Renewable Energy Global Innovations features: Verification of a novel innovative blade root design for wind turbines using a hybrid numerical method

Significance 

Increasing the power efficiency of wind turbine rotors is still a challenging task for many designers despite the recent aerodynamics knowledge input. In fact, the effected changes in rotor design from the formerly over-sized dimensioned turbines to the present slender turbines of higher power output is the best illustration of this aerodynamic progress. Therefore, to harness more energy, there will be need to increase the rotor size thereby increasing the turbine capacity. Unfortunately, extending the rotor length requires the development of new lightweight materials and causes logistic problems associated with transportation, and the construction and erection the rotor blades. More so, matters concerning public acceptance inhibits the size of onshore wind turbines. To counteract this, there is need for optimal aerodynamic design solutions. To this regard, researchers have developed robust airfoils with a high lift to drag ratio and a low noise level, combined with optimized blade plan forms to enhance aerodynamic performance. Regardless, the aerodynamic performance near the blade root is less studied, and a generally poor aerodynamic performance in the blade root region is considered a problem that cannot be directly solved with conventional design techniques.

In a recent research collaborations between scientists at Technical University of Denmark and Yangzhou University in China and led by professor Wei Jun Zhu, the team successfully enhanced the performance of horizontal axis wind turbines. Their aim was to introduce a cylindrical disk in front of the rotor in order to lead the incoming flow from the inner part to the outer part of the rotor blades. In return, they hoped that this would increase the power output, since the kinetic energy is mainly captured at the outer part of the blades, where the relative wind speed is high. Their work is now published in the research journal, Energy.

To assess the impact of their novel design idea, the researchers employed a hybrid numerical technique, based on solving the Reynolds-averaged Navier-Stokes equations, to determine the aerodynamic performance. They then used an in-house developed EllipSys3D code to represent the upstream cylindrical disc. Eventually, the research team assessed the impact of the disc on the rotor performance by systematically changing the size of the circular disc and its axial distance to the rotor.

The authors of this paper found out that the resulting maximum gain in relative power was around 1.5%. The team also noted that the power was found to increase in most of the simulations, as long as the disc size was not too large, where for the latter case, it started blocking the flow though the effective blade elements at the outer part.

The study reported by Wei Jun Zhu and colleagues presented a thorough numerical investigation on a novel horizontal axis wind turbine rotor system in which a circular disc has been added in front of the main rotor. The results have indicated that additional energy can be captured by placing a circular disc with a suitable diameter upstream of the rotor plane. This being the first numerical attempt to make proof of the concept, it is expected that it will provide a basis for future works that may further optimize the shape of the disc.

About the author

Wei Jun Zhu received his PhD degree in Wind Energy from Technical University of Denmark in 2008. During his Phd, he received national award from Chinese government as Outstanding self-financed students abroad.  Since 2004, he was a faculty member in the department of wind energy. Start from 2012, he was employed as permanent senior researcher at department of Wind Energy, Technical University of Denmark. From 2016 until now, he is a full Professor at Yangzhou University and Special Pointed Professor of Jiangsu province in China.

He has been involved in teaching wind turbine aerodynamic and aeroacoustic courses and in the general field of wind energy research. He has authored/co-authored over 50 peer reviewed journal papers in the field of wind turbine aerodynamics, computational aeroacoustics and computational fluid dynamics. He is the recognized reviewer of many journals and he is the best reviewer of Renewable Energy in 2014.

Reference

Wei Jun Zhu, Wen Zhong Shen, Jens Nørkær Sørensen, Hua Yang. Verification of a novel innovative blade root design for wind turbines using a hybrid numerical method. Energy, volume 141 (2017) pages 1661-1670.

 

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Renewable Energy Global Innovations features: Analysis of rain-induced erosion in wind turbine blades

Significance Statement

In conjunction with the increasing interest in renewable energy as an alternative to fossil fuels, researchers have continually focused on wind energy industry in a bid to increase the power output of wind turbines. This has prompted production of more wind turbines with higher power output. Increasing the blade size is a primary method for enhancing the turbine’s power output which has resulted in blade tip velocities of up to 120m/s.

With the high blade tip velocities, high susceptibility to erosion comes in, particularly in harsh environments such as regions with heavy rain and hail. Rain erosion of the blades initiates with a consistent rise in the blade’s surface roughness until small pits form close to the leading edge. With time, the density of the resulting pits increase until gouges form. An increase in surface roughness of the blades translates to a rise in aerodynamic drag coefficient that consequently leads to low performance as well as energy loss.

University of Massachusetts Dartmouth researchers, led by Dr. Mazdak Tootkaboni from the department of civil engineering,  have recently published a two-part research paper on predicting rain erosion  in wind turbine blades. The aim of the research was to integrate well-established theories as well as computational models to come up with a framework that could approximate the expected erosion lifetime of a selected blade, given rainfall history at a particular region, blade shell attributes, and operational conditions of the wind turbine. These papers are published in the Journal of Wind Engineering and Industrial Aerodynamics.

As a first step, the research team developed a stochastic model of rain texture that was capable of relating the integral attributes of rain, for instance, rain intensity and average volume of water per unit volume of air to its micro-structural attributes including raindrop sizes as well as their spatial distribution. The model allowed for the reproduction of three-dimensional fields of raindrops.

Temporal and spatial variations of the impact pressure developed in  droplet-surface collision were then computed using a GPU accelerated CFD model of free surface flows. The authors proposed a multiresolution method in a bid to minimize computational cost and an interpolation scheme to compute the impact pressure profile for any drop size efficiently and accurately.

The final component of the proposed framework  entailed the computation of fatigue damage for every raindrop by undertaking a stress analysis of its collision with the coating surface and probabilistically integrating these fatigue damages to estimate the “expected” erosion life time of the coating. The authors computed the stresses through a finite element modeling of the drop impact, where the droplet impact pressure, already calculated from CFD analysis of rain drop impact on the coating surface, was applied on the surface as a spatially varying time dependent external load.

Ingredients of Computational Framework

About The Author

Dr. Mazdak Tootkaboni is an associate professor in the in the Department of Civil and Environmental Engineering at the University of Massachusetts Dartmouth. He holds a Ph.D. and a M.Sc. in Engineering Mechanics from the Johns Hopkins University. Dr Tootkaboni’s research interests include uncertainty quantification, stochastic computational mechanics, topology optimization, design under uncertainty, and design of multifunctional architected materials. Dr. Tootkaboni is also interested in the application of stochastic analysis, data Analytics, and machine learning techniques in predictive modeling in solid and structural mechanics.

About The Author

Behrooz Amirzadeh holds a M.Sc. degree in Mechanical Engineering from University of Massachusetts Dartmouth and a B.Sc. degree in Materials Science and Engineering from Sharif University of Technology in Tehran. During his time at UMass Dartmouth, his research was primarily focused on stochastic modeling and high performance computational simulation of fluid-structure interaction. He is currently working at RAID Inc in Andover, MA as a Senior HPC Solutions Architect helping scientists in national labs and universities design and deploy HPC clusters for next generation simulations research.

About The Author

Dr. Arghavan Louhghalam is an assistant professor in the Department of Civil and Environmental Engineering at the University of Massachusetts Dartmouth. Dr. Louhghalam holds a Ph.D. and a M.Sc. in Engineering Mechanics from the Johns Hopkins University and  prior to joining  University of Massachusetts, she was a postdoctoral research associate at Massachusetts Institute of Technology’s Concrete Sustainability Hub (CSHub@MIT). Dr. Louhghalam’s research interests are mainly focused on interconnected areas of solid mechanics, material modeling and applied statistics with applications to sustainability, durability and resilience of civil infrastructure as well as high-performance structures such as light weight high speed trains and wind-turbine blades.

About The Author

Dr. Mehdi Raessi is an associate professor in the Mechanical Engineering Department at the University of Massachusetts Dartmouth. His research interests include advanced computational simulations of multiphase flows with applications in energy systems (renewable and conventional), material processing, and microscale transport phenomena. Raessi has a PhD in mechanical engineering from the University of Toronto and was a Postdoctoral Fellow at NASA-Stanford University’s Center for Turbulence Research before joining UMASS-Dartmouth.

Reference

Amirzadeh, A. Louhghalam, M. Raessi, M. Tootkaboni. A computational framework for the analysis of rain-induced erosion in wind turbine blades, part I: Stochastic rain texture model and drop impact simulations. Journal of Wind Engineering & Industrial Aerodynamics, volume 163 (2017), pages 33–43.

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Amirzadeh, A. Louhghalam, M. Raessi, M. Tootkaboni. A computational framework for the analysis of rain-induced erosion in wind turbine blades, part II: Drop impact-induced stresses and blade coating fatigue life. Journal of Wind Engineering and Industrial Aerodynamics, Volume 163, (2017), Pages 44–54.

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Renewable Energy Global Innovations features: Ageing Assessment of a Wind Turbine Over Time by Interpreting Wind Farm SCADA Data

Significance Statement

The quest for green energy has motivated the world over to see the potential of wind as an important source of energy. The wind power industry is rapidly developing and therefore there is an increasing need to lower the Cost of Energy. Wind farms are being put up each day around the world in places of high wind energy potential so as to harness the renewable power of the wind. However, ageing of wind turbines and their components is inevitable. Over time, ageing will affect the reliability and power generation efficiency of the turbine. Therefore, performing an ageing assessment of the wind turbines is of significance so as to not only optimize the operation and maintenance strategy of the turbine, but also improve the management of the wind farm. Little has so far been done on this ageing led performance degradation concern of the turbine since most of the existing turbine monitoring techniques are mainly focused on condition monitoring for fault detection purposes.

An Innovative research was organized by Newcastle University in the UK, in collaboration with Hunan University of Science and Technology, Hunan Institute of Engineering, and XEMC Windpower Co. Ltd in China, to investigate the ageing issue of onshore wind turbines over time through interpreting the data collected by the wind farm Supervisory Control and Data Acquisition (SCADA) system. The research aimed at assessing the decline in performance of the turbines that could be directly attributed to their ageing. This research work is now published in the peer-reviewed journal, Renewable Energy.

The research team started by discussing the SCADA parameters that potentially could be used for the ageing assessment. The team then developed four ageing assessment criteria so as to describe the ageing issues of wind turbines from various viewpoints. From these, they were able to develop an innovative information fusion based ageing assessment method. Eventually, the effectiveness of the proposed method was verified using real SCADA data collected from an onshore wind farm.

The team observed that the values of the four ageing criteria deviated from 1. Since it is clear that during the turbines’ service life ageing will inevitably happen, the separate analysis of the individual ageing assessment criterion could not lead to reliable results regarding a turbines’ ageing effect. Therefore, the conventional individual assessment technique failed to yield reliable assessment results. However, the team noted that when the information fusion-based method was applied using the four ageing criteria, more realistic and acceptable results were obtained. The value of the information fusion based criterion was noted to deviate well from 1 in presence of ageing over time.

Herein, the conventional individual-based turbine age assessment method and the comprehensive information fusion-based method have been applied hand in hand. The latter technique exhibits reliability and robustness in assessing a turbines performance decline with age. It can therefore be recommended for use in such related works as its estimates from computations are quite reliable.

About The Author

Wenxian Yang has completed his PhD in 1999 from Xi’an Jiaotong University, China. Currently, He is the Lecturer of Offshore Renewable Energy of Newcastle University, United Kingdom. He has over 100 publications in top journals and peer-reviewed conferences. According to the statistics of Google Scholar, his publications have been cited over 1400 times since 2012, and his publication H-index is 20 and has been serving as an associate editor of IET Journal of Renewable Power Generation and the editorial board member of a number of reputed Journals.

Email: wenxian.yang@newcastle.ac.uk, Contact Number: +44 191-208-6171.

About The Author

Juchuan Dai received his PhD from Central South University (China) in 2011 and is a visiting scholar in Newcastle University (UK) in 2016. He is currently an associate professor in Hunan university of Science and Technology (China). He is the person in charge of the project of the National Natural Science Foundation of People’s Republic of China. His main research interest is wind power technology and equipment, also is reviewer for many international academic journals.

Email: daijuchuan@163.com

About The Author

Deshun Liu received his PhD from Central South University (China) in 1996 and is a visiting scholar in University of Missouri-Rolla in 2004. He is currently a professor in Hunan university of Science and Technology (China). He is the person in charge of the project of the National Natural Science Foundation of People’s Republic of China. He has over 100 publications in journals and peer-reviewed conferences.  His main research interest is wind power technology and equipment, also is reviewer for many international academic journals.

Email: deshunliu@hnust.edu.cn

About The Author

Xin Long is currently the chief expert of XEMC Wind Power Cooperation. He has been the person in charge of the project of the National High Technology Research and Development Program  (“863” Program) of China. He has long been engaged in the design and R & D of large wind turbines, and has rich experience in actual product research and development.

Email: lx@xemc-wind.cn

About The Author

Junwei Cao received his master’s degree from Hunan University of Science and Technology (China) in 2016. He works as an engineer in XEMC Wind Power Cooperation after graduation. His main research interest is wind power technology and currently engaged in data analysis work.

Email: caojunwei@xemc-wind.cn

Reference

Juchuan Dai, Wenxian Yang, Junwei Cao, Deshun Liu, Xing Long. Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data. Renewable Energy, Available online 31 March 2017.

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Renewable Energy Global Innovations features: Adaptable wind/solar powered hybrid system for household wastewater treatment

Significance Statement

Droughts, explosive population growth and the continuing view that water is an infinite resource are reasons for water shortages in many areas of the world. Reusing wastewater as part of sustainable water management allows water to remain as an alternative water source for human activities reducing the demand on groundwater. Initially, wastewater reuse was focused for irrigation and non-potable purposes, but with more innovative advancements in technology, wastewater reuse domain has significantly expanded. Typical wastewater treatment processes are fit for non-potable water reuse that does not necessitate the wastewater to be treated to drinkable standards.

Treatment of raw water from rivers, lakes and wells must meet drinkable standards. To achieve this, intensive energy is required and this is yet another issue when it comes to energy supply in rural areas. Access to modern energy is an economic and social priority for the rural population owing to its direct environmental and economic benefits.

Jacqueline Stagner and David Ting at the University of Windsor in Canada in collaboration with Akhilesh Soni at Indian Institute of Technology presented a design of a self-sustainable stand-alone water treatment system. The application of the innovative system which is “taking wastewater reuse into another level” was driven by renewable energy sources and was designed for an off-grid community. Their design philosophy was the use of solar energy as a primary source and wind power as a secondary source in powering the purification system. Their research work is published in Sustainable Energy Technologies and Assessments.

The water purification process presented by the authors implemented two models of renewable energy, wind and solar. Wind power was applied to drive a vacuum pump, which reduced the air pressure inside the system. They optimized the number of processing stages to be four. This was based on the fact that increasing the number of stages would not be economically viable. The vapor pressure was maintained in the successive stages of the still at 31, 27, 20, and 18 kPa.

The researchers used constricting nozzles to connect the household drain with the recirculating loop and still. Solar power was used to heat the water in the chambers of the still. This solar energy was transferred to the system from the bottom chamber through a heat exchanger, and from the top of the chamber through heating the water directly.

The authors observed that the fresh water production capacity of the proposed solar-collector four-stage solar still, operating for 6 hours a day and a constant flux of 850W/m², was 17.4kg/m²/day. This value was higher than for conventional solar stills. They found that the annual cost of the system was about Rs7450, and per unit water cost in the range of 0.5-1.2Rs/kg for the wind speed ranging from 1-5m/s. In the absence of wind, a hand-driven wheel could be used to drive the reciprocating pump to propel the water from ground to roof level.

Considering wind speeds of approximately 1-5 m/s, the proposed multistage solar desalination system can meet the fresh water needs for urban as well as rural communities by distilling 25-45kg/day. This wastewater reuse innovation will advance the application of recycled water as a reliable alternative water source.

About The Author

David S-K. Ting worked on Combustion and Turbulence (Premixed Turbulent Flame Propagation) during his graduate years. He then ventured into Convection Heat Transfer and Fluild-Structure Interactions prior to joining University of Windsor. Professor Ting is the founder of the Turbulence & Energy Laboratory. Dr. Ting supervises students on a wide range of research projects primarily in the Energy Conservation and Renewable Energy areas. To date, he has co/supervised over sixty graduate students and co-authored more than one hundred journal papers. Fundamentally, this once a jungle boy of Borneo rainforest is ever astounded by the beautiful, orderly and yet impregnable creation. His love is still faithfully on Flow Turbulence; see
http://ift.tt/2wrHbxe

About The Author

Jacqueline Stagner is the Undergraduate Programs Coordinator in the Faculty of Engineering at the University of Windsor, and an adjunct faculty member in the Department of Mechanical, Automotive, and Materials Engineering. Dr. Stagner co-advises students in the Turbulence and Energy Lab, in the area of solar water desalination. Prior to working at the University of Windsor, she attained a PhD in Materials Science and Engineering, a Master of Business Administration, and a bachelor’s degree in Mechanical Engineering. She also worked as a release engineer in the automotive industry for 6 years.

Reference

Akhilesh Soni, Jacqueline A. Stagner, and David S.-K. Ting. Adaptable wind/solar powered hybrid system for household wastewater treatment. Sustainable Energy Technologies and Assessments. Available online 8 March 2017

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Renewable Energy Global Innovations features: Short-term electric energy production forecasting at wind power plants in pareto-optimality context

Significance Statement

Wind energy is considered as a clean renewable resource that plays an important role in mitigating greenhouse emissions, which are pivotal in climate change. Therefore, there has been observed a consistent growth of the wind energy sector in the recent past. Unfortunately, despite the undoubted features of being a free and clean energy resource, wind power is generally intermittent and uncontrollable. Market participants as well as power system operators therefore face major challenges, one being unable to control uncertainty and variability of wind power generation.

One main duty of transmission system operators is to maintain the balance of electric power generation and electric load. For wind power farms, maintaining the power balance appears to be quite challenging in view of the fluctuating nature of wind resources as well as the problem of large-scale energy storage. Therefore, transmission systems operators generally schedule an optimal combination of controlled generating units to satisfy a forecasted load, while having an assumed wind generation prediction, power system reserve needs and generation and transmission constraints.

Wind power forecasting therefore becomes an indispensable factor for electricity market players, for instance, energy trading companies and wind energy producers. Therefore, Jacek Wasilewski at PSE Innowacje sp. z.o.o and Dariusz Baczynski from the Warsaw University of Technology Poland formulated an approach allowing for estimating an assembly of prediction models satisfying a number of model  learning and testing criteria. They then developed the Pareto-optimization method. They presented a mathematical model and a case study of the wind power-forecasting taking into account the Pareto-optimization of the selected prediction criteria. Their work is published in Renewable and Sustainable Energy Reviews.

The authors discussed and analyzed the application issues of multi-criteria prediction model optimization in the intra- as well as next day wind power prediction. In order to understand the artificial neutral networks, a multi-objective method was developed based on Pareto-optimization. The method takes into account a concept of forecast and enables the analysis of accuracy prediction models implementing a generalized multi-criteria function.

Wasilewski and Baczynski showed that the effectiveness of the ANN-MLP learning reference to a particular criterion could be achieved independently of the algorithms (Back Propagation, Particle Swarm Optimization and hybrid BP+PSO algorithms were used) .The learning process was effective when the learning data set was applied to choose the optimal model.  An excess of coordinate of the utopia vector at the NISE procedure occurred only if the ANN process was not effective. However, the utopia vector was as a result of ANN optimization reference to the testing data.

It was observed that no impact of the analyzed wind farm and the ANN-MLP structure on the given outcomes has been evident in most analyzed situations. The ANN-MLP model was a machine learning model with low bias and high variance. The experiment outcomes presented should be confirmed also with the use of other categories of prediction models and different statistical attributes.

Future works by Wasilewski and Baczynski will perhaps focus on developing an interface between a decision making process and the forecasting. This will be aiming at presenting how operators as well as decision makers in energy markets would implement the proposed forecasting method reference on the multi-criteria approach.

About The Author

Jacek Wasilewski was born in Elblag, Poland in 1981. He received the M.Sc. and Ph.D. degrees in electrical engineering from Warsaw University of Technology in 2005 and 2011 respectively. From 2006 to 2015 he was employed at the Institute of Electrical Power Engineering, Warsaw University of Technology as an academic teacher.

Since 2015 he has been employed at the Research and Development Centre in PSE (Polish transmission system operator) where he is currently a principal consultant.  His interests include methodologies of power system optimisation, both in strategic planning and operation.

About The Author

Dariusz Baczyński was born in Warsaw, Poland in 1972. He received the M.Sc. (1996), Ph.D. (1999) and D.Sc. (2014) degrees in electrical engineering from Warsaw University of Technology. Since 1999 he is an assistant professor at the Institute of Electrical Power Engineering, Electrical Faculty, Warsaw University of Technology. He also cooperated with IT companies as an expert.

His interests include optimisation, forecasting and applications of computer systems in broadly understood electrical power engineering. In particular he is much interested in pareto optimisation and modern computational intelligence methods. As a result of his recent analysis of the latter he developed a new concept of Artificial Ecosystem Algorithm for optimization problems.

Reference

Wasilewski and D. Baczynski. Short-term electric energy production forecasting at wind power plants in pareto-optimality context. Renewable and Sustainable Energy Reviews, volume 69 (2017), pages 177–187.

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Renewable Energy Global Innovations features: Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect

Significance Statement

Various aerodynamic load computation approaches for undertaking the aeroelastic analysis of wind turbines has been developed. Blade Element Momentum, for instance, is applied frequently owing to its fast and simple nature. This method provides the loads along the blade span, torque and the amount of power generated by the turbine from the wind speed, aerofoil attributes and blade geometry. Wind turbine blades are designed with aerofoil cross sections; therefore, the Blade Element Method demands aerofoil characteristics as a function of the angle of attack in order to calculate the forces.

Real time flow over the turbine are in axial and radial directions. However, only axial flow is taken into consideration when using 2D aerofoil characteristics while radial flow is normally neglected. This creates high discrepancies of the computed aerofoil characteristics and forces of the CFD or Experiment analysis with the values calculated from the Blade Element Momentum code implementing the 2D aerofoil attributes. This unpleasant agreement is due to stall delay and is evident in most stall-regulated turbines.

Professor E.Y.K. Ng and his PhD student Ijaz Fazil at Nanyang Technological University in Singapore discussed the aerofoil characteristics as well as its various extrapolation methods for an array of angles of attack. They conducted Unsteady RANS (URANS) CFD analysis on two-bladed NREL Phase VI wind turbine and the measurements were compared with the experimental outcomes of the NREL/NASA test for confirmation. Their research work is now published in Energy.

The NREL phase VI rotor adopted for in their study was a two blade stall regulated wind turbine and the blades were tapered and twisted. The rotational speed was 72rpm for all wind speeds.. They conducted the URANS analysis for different inlet wind speeds by implementing the sliding mesh approach. The authors measured pressure at 18 radial locations including the five radial locations considered in experimental measurements.

There was a rise in the lift coefficient along the blade span when compared to the 2-dimensional aerofoil attributes for the same angle of attack owing to the span wise flow. This is the stall delay. This effect if effective at inboard sections and it reduces gradually towards the blades’ tips. Apparently, there is no clear explanation for stall delay but the authors believed that when stall occurred, separated flow on the suction side of the blade, appeared to rotate along with the blade, and experienced centrifugal force. The centrifugal force caused the separated flow to move in a radial manner towards the tip. This span wise flow also permitted Coriolis force to act towards the trailing edge, and therefore, resulted in stall delay.

The research team also discussed in their paper the reasons for the over prediction of the Blade Element Momentum analysis on using existing stall delay correction models. The Blade Element Momentum with or without the stall delay models couldn’t predict the correction distribution of aerofoil characteristics along the span of the blade in order to contemplate the impact of the stall delay.

The study proposed the use of the 3D aerofoil characteristics which is computed using Inverse BEM method in the Blade Element Momentum to take care of the stall delay. A good agreement with the aerofoil characteristics distribution along the span of the blade was realized in the current method.

The comparison of extracted CL (at 18 locations) from CFD analysis using inverse BEM and Distribution of CL along the blade span from BEM analysis on using existing stall delay models.

Comparison of extracted CL (at 18 locations) from CFD analysis using inverse BEM and distribution of CL along the blade span from proposed BEM analysis.

About The Author

Ijaz Fazil received his Bachelor of Engineering in Mechanical Engineering from Anna University, India in 2008. He has scored 93.94 percentile in GATE 2008. He received his Master of Science in Mechanical Engineering from National University of Singapore, Singapore in 2010. He commenced his Ph.D. study on Computational Fluid Dynamics (CFD) analysis of rotor and wake aerodynamics of wind turbine at Nanyang Technological University, Singapore, in August 2011. To date, he has presented 3 conference papers and has 1 journal publication. His research interests are –CFD, Wind Turbine Aerodynamics, Building Physics, Thermal Comfort and Fire Modelling. LinkedIn profile.

About The Author

EYK Ng obtained his Ph.D at Cambridge Univ. and a faculty in NTU. He is the Editor-in-Chief for the ISI Journal of Mechanics in Medicine and Biology for dissemination of original research in all fields of mechanics in medicine and biology since 2000;

He is the Founding Editor-in-Chief for the ISI indexed Journal of Medical Imaging and Health Informatics. His main area of research is human physiology, biomedical engg; computational fluid dynamics and numerical heat transfer. Ng has had more than 275 ISI journal articles and 100 conference papers and 13+1 books published including “Compressor Instability with Integral Methods” (2007); “Cardiac Pumping and Perfusion Engineering” (2007); “Imaging and Modelling of Human Eye” (2008); “Distributed Diagnosis and Home Healthcare, D2H2 v.1 and 3” (2009, 2012); “Performance Evaluation in Breast Imaging, Tumor Detection and Analysis” (2010); “Computational Analysis of Human eye with Applications” (2011); “Multimodality Breast Cancer Imaging” (2013); “Human eye imaging and modeling,” “Image Analysis and Modeling in Ophthalmology,” “Ophthalmology Imaging and Applications” (2013, 2014), “Bio-inspired Surfaces and Applications” (2016); “Computation and Mathematical Methods in Cardiovascular Physiology” by WSPC (2017, in-press) and “Application of Infrared to Biomedical Sciences” (2017). He is an invited keynotes speaker for more than 15 international scientific confs./workshops. 15 of his papers have been adopted as references in Singapore Standard (SS 582: 2013) and ISO/IEC 80601-2-59:2008.

He is also presently serving as panel member for the Biomedical Standards Committee, Singapore. The co-inventor of three USA patents on multiple analytical software classifier programs to identify the different stages of breast cancer development using thermal data with Cyrcadia Health, Inc. system, he further explores the use of IR in the field of ophthalmology for early detection of health abnormality.  Here is his publication list  and CV . 

Reference

Ijaz Fazil Syed Ahmed Kabir, E.Y.K. Ng. Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect. Energy, volume 120 (2017), pages 518-536.

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Renewable Energy Global Innovations features: Predicting the performance of a floating wind energy converter in a realistic sea

Significance Statement

This research paper focuses on the methods for predicting the working of a floating wind energy converter, for instance, a wind turbine set up on a floating platform moored to the sea bed. Practically, the performance analysis of the floating wind energy converter is normally performed by solving the converter motion equation with a complex convolution integral term that represents the hydrodynamic effects.

The computation of the convolution integral term is complicated, time consuming and demands a large amount of memory on the selected computing machine. Above all, most research works have investigated the performances of the wind energy converters in an ideal unidirectional random sea. The researchers have applied the wave spectra in these works as uni-directional spectra, meaning that the wave energy travels in one direction. However, wind-generated energy propagates in various directions.

Researchers led by Professor Yingguang Wang from Shanghai Jiao Tong University, China, investigated rigorously the performance of a floating wind energy converter set up in a realistic, multi-directional random sea. They considered in the numerical simulation process, all the wind loads acting on the energy converter. Above all, the authors adopted the new state space model, the FDI-SS model, to solve the motion equation of the floating wind energy converter. This was in a bid to enhance the efficiency of the simulation. Their work is published in Renewable Energy.

The authors adopted a 5MW wind turbine with a 3-blade turbine with 126m rotor diameter. The hub diameter was 3m and was 90m high above the still water surface. In order to analyze the performance of the proposed wind energy converter, they numerically integrated the vector-form time domain motion equations with a convolution integral term.

The researchers selected specific load cases for the operation condition. The 10-min average wind speed was 11.2m/s at the top of the tower and 0.15 turbulence wind intensity. They applied a Kaimal power spectrum to characterize the turbulence wind random field over the turbine’s rotor plane. The turbulence wind information was applied to calculate the wind loads prior to the numerical integration. The JONSWAP wave spectrum was adopted in the simulation process of the unidirectional random waves.

In the study, the wave height for a selected sea state was 5m, spectral peak period was 12.4s and peakedness factor was 2.0. The JONSWAP wave spectrum was unidirectional in the sense that the wave energy was travelling in one direction. However, in a realistic sea, wind generated wave energy propagates and spreads in various directions. Therefore, the authors multiplied the uni-directional wave spectrum by a spreading function in a bid to obtain a directional spectrum.

The results of their paper demonstrated a great need for using a realistic, multidirectional sea state when determining electrical generation as well as dynamic responses of the floating wind energy converter. Above all, with an aim of improving the simulation efficiency, the authors used a new state space model to estimate the convolution integral term when solving the motion equation of the floating converter.

Yingguang Wang and Lifu Wang systematically analyzed and compared the simulation results and confirmed the precision and efficiency of the proposed model. They found that the new FDI-SS model could be a helpful tool for the design of floating wind turbines, therefore, helping in the exploitation of renewable wind energy.

Figure caption: Simulated sea surfaces on a square of 128 [m] by 128 [m] based on a directional JONSWAP wave spectrum with Hs=7m, Tp=11s and a cos-2s type spreading function (s=15)).

About The Author

Dr. Yingguang Wang earned a B. S. degree and a M. S. degree from Shanghai Jiao Tong University and the University of Washington at Seattle, respectively. He received his Ph. D degree from Shanghai Jiao Tong University in 2008.

Presently he is an Associate Professor in the Department of Naval Architecture and Ocean Engineering at Shanghai Jiao Tong University (SJTU). He has been teaching in the Department of Naval Architecture and Ocean Engineering of SJTU since February, 2003. The SJTU course “Principles of Naval Architecture” he teaches was honored in 2007 by the Chinese Ministry of Education as a national level excellent course. The textbook “Marine Structural Analysis and Design” sole authored by him is honored as a “China’s 12th Five-Year Plan national key book”.

Dr. Yingguang Wang’s research efforts have focused on the development of techniques for predicting the dynamics and stability of ships and floating offshore systems subject to random environmental loads. Systems exhibiting nonlinear behavior and/or exposed to risk inducing conditions receive his particular attention. In addition, his international academic renown also has much to do with his superb work on the prediction of extreme waves critical to the design of marine structures, including oil and gas facilities as well as ocean renewable energy systems. During the past ten years, his research work in the aforementioned areas has resulted in 32 outstanding journal papers as the sole author or first author in peer-reviewed journals, many of which are leading academic journals in the world. One of his first-authored papers was honored in 2015 as one of the “Top articles in outstanding scientific and technical journals of China” by the Chinese Ministry of Science and Technology. On November 2010, he won a prestigious second-class Science and Technology Advancement Award bestowed by Shanghai municipal people’s government.

Reference

Yingguang Wang, Lifu Wang. Predicting the performance of a floating wind energy converter in a realistic sea.  Renewable Energy,  volume 101 (2017),  pages 637-646.

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Renewable Energy Global Innovations features: Reproducing Statistical Property of Short-term Fluctuation in Wind Power Profiles

Significance Statement

Wind power generation which serves as a source of renewable energy faces certain challenges due to short-term fluctuations in power output. This led to the addition of a battery system in order to reduce these pitfalls and as a result, the effect of the short-term fluctuations in relation to the battery system needs to be evaluated. One means of evaluating this effect is the use of a power flow simulation.

A group of researchers from , Waseda University in Japan, proposed an innovative method whereby synthetic wind power profiles with high temporal resolutions for power flow simulation can be generated by reproducing plausible statistical behavior of a realistic short-term fluctuation. The work is now published in journal, Energy Procedia.

In order to achieve a realistic short-term fluctuation which occurs in wind power generation, the power flow simulation observes the time-series statistical behaviors. The methods used in achieving the realistic short-term fluctuation include; the previously used autoregressive mean average approach and the block bootstrap approach.

The authors further compared the statistical property of the short-term fluctuations generated from three different approaches; naive bootstrap, autoregressive mean average bootstrap approach and the block bootstrap coupled with evaluations made by finding the autocorrelation functions of the detrended sequence which stands for the typical short-term fluctuation in wind power generation.

Following the generation of ten plausible short-term fluctuations for each approach from a dataset of a case study in Japan, the lowest root mean square error of the autocorrelation functions was observed in the block bootstrap approach. This shows that the block bootstrap approach gave the highest accuracy amongst the three. It improved 26.5% from the autoregressive mean average approach.

The lowest root mean square error for variance sequences was also observed with the block bootstrap approach, which indicates that the generated short-term fluctuations possess realistic volatility.

The block bootstrap approach which exhibited plausible volatility and accuracy of the detrended sequence indicated an imaginative time-series statistical property of the real-world fluctuation in wind power generation which would be of relevance in determining the effects of the short-term fluctuations on battery systems of future wind energy technologies.

About The Author

Seigo Furuya received his B.Eng and M.Eng degree in electrical engineering and bioscience from Waseda University, Japan, in 2014 and 2016, respectively. His research interests are generating synthetic wind power generation profiles by statistical approach.

About The Author

Yu Fujimoto received his Ph.D. in engineering from Waseda University, Tokyo, Japan, in 2007. He is an Associate Professor at the Advanced Collaborative Research Organization for Smart Society (ACROSS), Waseda University.

His primary areas of interest are machine learning and statistical data analysis. His current research interests include data mining in energy domains especially for operating and controlling devices in smart grids, and statistical prediction of the power fluctuation under the large introduction of renewable energy sources. He is a Member of Information Processing Society of Japan.

About The Author

Noboru Murata received the B. Eng, M. Eng, and Dr. Eng degrees in mathematical engineering and
information physics from the University of Tokyo in 1987, 1989, and 1992, respectively. After working at the University of Tokyo, GMD FIRST in Germany, and RIKEN in Japan, since April 2000, he joined Waseda University in Japan where he is currently a professor.

His research interest includes the theoretical aspects of learning machines such as neural networks, focusing on the dynamics and statistical properties of learning.

About The Author

Yasuhiro Hayashi received his B. Eng., M. Eng., and D. Eng. degrees from Waseda University, Japan, in 1989, 1991, and 1994, respectively. In 1994, he became a Research Associate with Ibaraki University, Mito, Japan. In 2000, he became an Associate Professor with the Department of Electrical and Electronics Engineering, Fukui University, Fukui, Japan. He has been with Waseda University as a Professor of the Department of Electrical Engineering and Bioscience since 2009; and as a Director of the Research Institute of Advanced Network Technology since 2010. Since 2014, he has been a Dean of the Advanced Collaborative Research Organization for Smart Society at Waseda University.

His current research interests include optimization of distribution system operation and forecasting, operation, planning, and control concerned with renewable energy sources and demand response. Prof. Hayashi is a Member of the Institute of Electrical Engineers of Japan and the International Council on Large Electric Systems.

Reference

Furuya, S., Fujimoto, Y., Murata, N., Hayashi, Y. Reproducing Statistical Property of Short-term Fluctuation in Wind Power Profiles, Energy Procedia 99 ( 2016 ) 130 – 136.

Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.

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Renewable Energy Global Innovations features: Reformulation of parameters of the logistic function applied to power curves of wind turbines

Significance Statement

The power curve of a wind turbine indicates the relationship between the wind speed and the electric power supplied. Therefore, it is widely used when analyzing or studying a wind turbine or a wind farm. The power curve of a wind turbine is the one provided in the manufacturer’s paperwork. This paper will focus on the modeling of this curve although measurements in the wind turbine in a wind farm will eventually reveal conditions that can slightly differ from those stated by the manufacturer. There are circumstances that can affect the operating conditions, and they include, turbulence, gusty winds, wind shear, wake effects, icing and component fatigue in the wind turbine.

Daniel Villanueva and Andrés Feijóo from de Vigo university in Spain proposed a method that would lead to the reformulation of parameters of the logistic function applied to power curves of wind turbines. They presented an alternative procedure to obtain parameters of the 4-parameter logistic function in order to improve the model. Their work is now published in peer-reviewed journal, Electric Power Systems Research.

The models used were based on the 4-parameter logistic function model, where the relationship between wind speed and generated power is a continuous curve. The parameters of the model obtained by using optimization techniques have no technical meaning. In order to know the influence of the wind turbine’s features and behaviors, another model is needed. The reason behind using parameters with some technical meaning is in order to assess the power curve from a theoretical point of view and obtain the weight of each parameter in the power curve and in the power output.

The method adopted in this paper consists of obtaining the parameters of the model from the features of a wind turbine power curve. Each feature investigated imposes a constraint that must be satisfied by the model, and this contributes to the configuration of the end result. Therefore, a deterministic process is proposed to obtain the parameters of the 4-parameter logistic model, which are obtained directly from the features of the power curve.

Three wind turbine power curve models based on a reformulation of the parameters of the logistic curve function were presented. The first one 4P-DP is similar to the 4-parameter logistic function but takes into account a deterministic procedure to obtain its parameters making the model meaningful since it provides information on the wind turbine behavior.

The second model (4P-DS), obtained by the means of simplification, some issues were improved, for instance, approximations to the power curve lower values of wind speed and ease of obtaining probability density function (PDF) expressions. The models consist of a continuous function that simplified the implementation of the curve in a computer program compared to the piecewise models.

The third one (3P-DP) simplifies even more the former expression and provides an easy way to model the power curve with just three parameters.

One result that can be obtained from these models is the expression of the PDF of the output power. This will provide a cumulative behavior of the power and can be used as input data to solve more complex problems such as probabilistic load flow, where the probabilistic behavior of the power is taken into consideration.

In order to check the proposed models, several wind turbines were taken into account. This was in a bid to make detailed comparison of wind turbines of the same rated power from difference manufacturers, and to check models for a wide range of rated powers.

About The Author

Daniel Villanueva joined the Departamento de Enxeñería Eléctrica of the Universidade de Vigo, Spain, in 2009, obtaining a PhD built on the analysis of the correlation among neighboring wind speeds and its joint impact in the electrical networks. As a consequence, he has co-developed several mathematical/technical tools, as the following: analysis of the wind speed behaviour, simulation of correlated non-Normal series of data, different wind power curve models, expressions for the wind power Probability Density Function, analysis of Probabilistic Load Flow with wind power, simulation of wind speed data for Economic Dispatch assessment, etc.

About The Author

Andrés Elías Feijóo Lorenzo received his MsC in electrical engineering from the Universidade de Santiago de Compostela, Spain, in 1990. After this, he obtained a PhD degree in electrical engineering from the Universidade de Vigo, Spain, with a thesis about the influence of wind farms in steady-state security assessment and power quality of large electrical power networks.

He is now with the Departamento de Enxeñería Eléctrica of this university and his field of research is wind energy, in particular steady-state and dynamic models of wind turbines, and simulation of wind turbines and wind farms including the analysis of the behavior of wind speed. All mathematical tools for the approach to these problems are of interest.

Reference

Daniel Villanueva, Andrés E. Feijóo. Reformulation of parameters of the logistic function applied to power curves of wind turbines. Electric Power Systems Research, volume 137 (2016), pages 51–58.

Departamento de Enxeñería Eléctrica, Universidade de Vigo, EEI, Campus de Lagoas-Marcosende, 36310 Vigo, Spain.

 

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Renewable Energy Global Innovations features: Structural Optimization of Vertical-axis wind Turbine Composite Blades Based on Finite Element Analysis and Genetic Algorithm

Significance Statement

Installing offshore wind farm can be challenging. Horizontal-axis wind turbines (HAWTs) have maintenance difficulty due to the location of its rotor and drive-train which are to be installed at the top of very tall towers. To improve the use of wind turbine, vertical-axis wind turbines (VAWTs) were introduced which overcame the disadvantages of HAWTs by locating their main components at the base of the wind turbine and made both installation and maintenance easier. It is possible to further improve the performance of wind turbine by optimizing the blades of the wind turbine. Wind turbine blades are made of composite materials due to their high strength-to-weight ratio and good fatigue performance. Additionally, wind turbine blades generally have complex structural layout including one or more shear webs and a number of composite plies placed at different ply angles, making their structural design quite challenging.

Dr. Lin Wang and colleagues from Cranfield University in the UK combined finite element analysis (FEA) and genetic algorithm (GA) to develop a structural optimization model of wind turbine composite blades. The research work is now published in peer-reviewed journal, Composite Structures.

The research team categorized the structural models used for wind turbine blades into two groups, that is one dimensional (1D) beam model and three dimensional (3D) FEA model. Due to the complexity of wind turbine blades structural layout, the team suggested combining FEA and GA to develop a structural optimization model of wind turbine composite blades. They applied the structural optimization model to ELECTRA 30 kW wind turbine blade, which is a novel VAWT blade, to optimize the structural layout of the blades. The optimization model took into consideration stress constraint, deformation constraint, vibration constraint, buckling constraint, and manufacturing maneuverability and continuity of laminate layups constraint.

The optimal blade design leads to a mass reduction of 17.4% in comparison with the initial design, the maximum total deformation is about 0.593 m and observed at the tip of the upper sail. The deformation value obtained is 15.3% lower than the allowable value of 0.7 m, the shows the new blade design is quite stiff and is not likely to experience large deformations. The team pointed out that the blade will not suffer from buckling, due to its load multiplier being 2.15 that is 43.33% higher than the minimum allowable value of 1.5. From the stress distribution, the research team observed the maximum positive normal stress to be 151.72 MPa, which is 52.90% lower when compared with the allowable value of 322.1 MPa. And the maximum negative normal stress (i.e. maximum compressive stress) to be very close to the allowable value.

This study demonstrated that the structural optimization model presented is capable of improving the efficiency of blade structural optimization and effectively and accurately determining the optimal structural layups of composite blades.

Reference

Lin Wang¹, Athanasios Kolios¹, Takafumi Nishino¹, Pierre-Luc Delafin, Theodore Bird², Structural optimization of vertical-axis wind turbine composite blades based on finite element analysis and genetic algorithm, Composite Structures 153 (2016) 123–138.

Show Affiliations

1. Centre for Offshore Renewable Energy Engineering, School of Energy, Environment and Agrifood, Cranfield University, Cranfield MK43 0AL, UK.
2. Aerogenerator Project Limited, Ballingdon Mill, Sudbury CO10 7EZ, UK.

 

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Renewable Energy Global Innovations features: Novel plant development for a high performance 3 kW integrated wind and solar system

Significance Statement

Professor Hsing-Sheng Chai and colleagues in Taiwan proposed to investigate the performance of a parallel system of four Savonius wind rotors with a solar panel deflector. The work is published in peer-reviewed Journal of Renewable and Sustainable Energy.

Since emphasis has been placed on renewable energies to protect the world from unwanted pollution, Chai and colleagues also join other research groups in improving savonius wind turbine which was used to generate energy. The savonius is a drag-type wind turbine, where the blades are the only driving force which the wind drag act. The rotational speed of the rotors and the relationship between the tip-speed ratio TSR and power coefficient Cp are to be considered.

According to the research team, to improve the performance of savonius wind rotors, they placed a series of rotors in a line, with a fixed distance between them, with each one rotating at a specific phase angle. The team in their view to improve the performance of 3 Kw integrated wind and solar system, they considered the number of blades, the wind velocity, the height of the rotor, and the blade overlap ratio. A system of four two-bladed savonius wind rotors in parallel matrix was constructed. The team employed a computational fluid dynamics software, Fluent, to analyze the flow fields then compared the simulations to their own experimental data.

They experienced magnus effect as each rotor absorbed momentum from other rotors during rotation, and is responsible for the additional rotation of the downstream rotor and the periodic coupling of local flow between the two rotors, enhancing the performance of the overall system. They found that the higher the wind velocity the better the performance of the system. The TSR values at the slopes of the simulation and experimental curves differ, this is because in simulations, the rotational speed of the wind rotors and inlet wind velocity are fixed values. A fast rotating wind that wind will easily passes through wind rotors, thereby causing Cp to decrease as a result of the wind not doing significant work on the rotors, said the research team.

This study experimentally tested novel four Savonius rotor system with a solar panel so as to improve the system performance and they were able to generate 14.55kWh of power per day at an efficiency of 21.7%. The results of this study show that the two-bladed configurations have better performance than the three-bladed ones, except with respect to the starting torque.

About The Author

Dr. Hsing-Sheng Chai is an assistant professor of Aletheia University in Taiwan. He obtained his PhD degree from National Chiao Tung University. His PhD major is mechanical engineering, while Bachelor and Master major is aerospace engineering. Renewable energy, thermal engineering, combustion science, fire safety, and computational fluid dynamics are the research topics appealing to him.  

Journal Reference

Hsing-Sheng Chai¹, Chang-An Chen², Chiun-Hsun Chen², Novel Plant Development for a High Performance 3 Kw Integrated Wind and Solar System, Journal of Renewable and Sustainable Energy 8, 045302 (2016).

Show Affiliations

1. General Education Center, Aletheia University, 32 Zhenli St., Danshui Dist., New Taipei City 251, Taiwan, R.O.C.
2. Department of Mechanical Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, R.O.C

 

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Renewable Energy Global Innovations features: The identification of structurally sensitive zones subject to failure in a wind turbine blade using nodal displacement based finite element sub-modeling

Significance Statement

To deal with the structural complexity, the wind turbine blades are modeled using finite elements. Vibrations at natural frequencies is an important part of blade designing. Therefore, for the validation of the structural design and the dynamic or modal response of the blades, finite elemental analysis technique was used. The analysis was done with varying degrees of complexity. Hence, the approach used by  Professor Mostapha Tarfaoui and Dr. Owaisur Rahman Shah from ENSTA Bretagne & Institut de Recherche Dupuy de Lôme (France) in their studies, was to model the blade and to define the material. Furthermore, to validate the proposed design they applied a static bending test on the full scale blade. The results from these static tests, using strain gauges, were then used to validate the sub-modeling regime in terms of strains measured at the blade surface.

The researchers considered the sub-models as sections or portions of the larger model. The larger complete model; the parent (as it can be a sub-model of a higher and larger, more complex model) and the child as sub-model. They compared the global model with the experimental results to validate the approach of sub-modeling and then later all the sub-models were validated successively.

Sub-modeling technique reduces the domain size of a finite element model to a more manageable size. From the different methods of sub dividing the problem domain into simpler smaller domains, they used the method of transfer of nodal displacement from one parent model to its child model. The method of nodal displacement was used to predict failure in large structures and to identify the sensitive zones.

Only ‘delamination’ was studied as the criteria for the failure instead of different criteria of failure of plies. They used the criteria of combination of failure as it would be necessary to simulate accurately and appropriately the failure of these structures.

In their experiments, the wind turbine blade was subjected to buckling at its compression side and that too, at its critically loaded section at 7.55m from the root which has produced waviness in the blade’s structure due to which the plies have inter-ply transverse tensile stresses in the stacking direction. So this caused the delamination type failure.

Sub-modeling results corresponded well from the parent level to its child level or sub-level. Therefore, the researchers concluded that the nodal displacement based sub-modeling can be used to locate weak points in a structure under extreme loading conditions. They further validated the global model in strain calculations when compared to full scale physical tests.

Due to the consistent results of sub-models, Shah and Tarfaoui concluded that the finest sub-models are an accurate representation of the failure that would take place eventually.

About The Author

 

Mostapha TARFAOUI is professor specialized in structural mechanics. His main expertise is focused on the composite and Nanocomposite materials behaviour. This includes the experimental and numerical investigation of the static and dynamic responses involved in the shipbuilding structures. He has expertise in Dialogue test/calculation in the heterogeneous structures relationships and numerical modelling. He obtained his Ph.D. of Engineering Science (Faculty of Science, University Haute Alsace, France) in 1999 (dissertation: “Finite element analysis of mechanical behaviour of plain and twill fabrics”) and M.S. Physics and Applications (Faculty of Science, University Haute Alsace, France) in 1993. He has more than 60 papers. He participated in many congresses (CanCOM, HIPER, ICCM, CFM, IMAC, ESMC, CST, JNC…). He is member of Organization committee and Scientific committee of the international Congress. He was chairman and co-chairman of different congress sessions.

Professor TARFAOUI is memberships of AFM (French Association of Mechanics), AMAC (Association for Composite Materials) and AF3M (Association Franco-Maghrebian of Mechanics and Materials). He is reviewer of international scientific journals:  Journal of Composite Materials, Advanced Materials Research, Computational Materials Science, Applied Mechanics and Materials, Journal of Reinforced Plastics and Composites, Mechanics of Advanced Materials and Structures, Mécanique & Industries, Matériaux et Techniques… 

 

Journal Reference

Owaisur Rahman Shah, Mostapha Tarfaoui. The identification of structurally sensitive zones subject to failure in a wind turbine blade using nodal displacement based finite element sub modeling. Renewable Energy, 2016, Volume87, pp 168-181. 

ENSTA Bretagne, MSN/LBMS/DFMS, 2 Rue François Verny, 29806, Brest, CEDEX 9, France

 

 

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Renewable Energy Global Innovations features: Floating offshore wind turbines: designing against the forces of nature

Significance Statement

Studies done before on a small scale floating horizontal axis wind turbine in surge motion showed thatthere is an increasing amplitude of the cyclic thrust and power generation against tip speed ratio. A numerical study using an Actuator Disk (AD) Navier Strokes model, a Blade Element Momentum (BEM) model and a Generalized Dynamic Wake (GDW) model was performed in order to determine the previous observations on the full-scale NREL 5MW reference rotor in surge motion. The research question was to understand the reason why such high variations in thrust and torque occur at non-optimal tip speed ratios. The research was done to improve the understanding of the fundamental science governing floating offshore machines so as to make them commercially viable in the future.

The test was performed by maintaining the surge amplitude and surge frequency fixed and changing the tip speed ratio. Full details of the AD model can be found in the full paper. Results are then compared with BEM combined with dynamic inflow engineering models as well as the GDW model.

When the operating tip speed is increased the released vorticity in the wake becomes stronger causing an increase in the amplitude variations of the flow inductions in the axial, radial and swirl directions. The extent of the radial expansion and contraction of the wake was found to increase with increasing tip speed ratio. The study makes us conclude that the dynamic wake model can be adopted for BEM modeling of a surging rotor but only if mean quantities are of interest. The GDW model on the other hand gives quite an acceptable agreement with what the AD model.

Their work give credit to the previous experiments conducted on a small model rotor. They produce similar results that thrust and power amplitudes vary with wave amplitude and frequency. The unsteady variations in thrust and power are clearly observedto increase at higher tip speed ratios related to turbulent wake condition. This affects the structural and electrical design of the commercial turbines to manage fatigue when the turbine is operated in its rated conditions. The power will have to be tapped by suitable electronics that can handle the strength and instability from the turbine. The study recommends that it is ideal to operate at low speed tip ratios to reduce the fatigue loads on the blades, especially where the power demands are not very high. Concludingly, the results from this quantitative study were compared to the FAST code results using both BEM and unsteady GDW models.. Some difference was found at high tip speed ratio towards the onset of the turbulent wake state. The results for low tip speed ratios agreed quite well. The study was however limited due to the fact that the rotor was tested under fixed surge conditions and varying tip speed ratios. 

 

About The Author

Dr. Daniel Micallef is an academic at the University of Malta where he joined the Environmental Design department of the Faculty for the Built Environment in September 2014.

Dr. Micallef graduated in Mechanical Engineering from the University of Malta in 2008 with first class honours. He started his professional career in the public sector with the Malta Resources Authority as an energy analyst. During this time he started pursuing a career in academia. He read for a joint PhD with the Delft University of Technology in the Netherlands (where he formed part of the DUWIND wind energy research group) and the University of Malta.

His research focused on furthering the understanding of wind turbine flow phenomena close to the tip. He was awarded his PhD in 2012. During the final year of his PhD, Dr. Micallef also worked as a project officer at the Mechanical Engineering Department of the University of Malta where he developed analysis tools and contributed in the design of an urban wind turbine being developed by industry. His research career took a twist in 2012 were he continued his research experience as a post-doctoral researcher on the HILDA FP7 project. While continuing to publish his work in wind energy, his post-doc research focused on a different topic – modelling of friction stir welding of steels.

He developed finite element and computational fluid dynamics models for the numerical analysis of the process. During his final months on the project, he was engaged as a lecturer at the Malta College of Arts Science and Technology (MCAST). His experience as a post-doc researcher and MCAST lecturer ended in September 2014.

Dr. Micallef published in high quality peer reviewed journals and conferences worldwide. His current major interests are in the fields of wind energy, wind engineering and building physics. Apart from his research activities, he lectures in undergraduate and Masters courses. Dr. Micallef is currently the secretary general of the Chamber of Engineers (an NGO). He is also the COST (Cooperation in Science and Technology) representative of Malta in two COST actions.  

Journal Reference

Daniel Micallef¹, Tonio Sant². Loading effects on floating offshore horizontal axis wind turbines in surge motion.  Renewable Energy, Volume 83, November 2015, Pages 737–748.

Show Affiliations

1. Department of Environmental Design, Faculty for the Built Environment, University of Malta, Malta
2. Department of Mechanical Engineering, Faculty of Engineering, University of Malta, Malta

 

 

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