A Review of Recent Advancements in Offshore Wind Turbine Technology

Offshore wind turbines are becoming increasingly popular due to their higher wind energy harnessing capabilities and lower visual pollution. Researchers around the globe have been reporting significant scientific advancements in offshore wind turbines technology, addressing key issues, such as aerodynamic characteristics of turbine blades, dynamic response of the turbine, structural integrity of the turbine foundation, design of the mooring cables, ground scouring and cost modelling for commercial viability. These investigations range from component-level design and analysis to system-level response and optimization using a multitude of analytical, empirical and numerical techniques. With such wide-ranging studies available in the public domain, there is a need to carry out an extensive yet critical literature review on the recent advancements in offshore wind turbine technology. Offshore wind turbine blades’ aerodynamics and the structural integrity of offshore wind turbines are of particular importance, which can lead towards system’s optimal design and operation, leading to reduced maintenance costs. Thus, in this study, our focus is to highlight key knowledge gaps in the scientific investigations on offshore wind turbines’ aerodynamic and structural response. It is envisaged that this study will pave the way for future concentrated efforts in better understanding the complex behavior of these machines.

MACHINE LEARNING FOR THE DEVELOPMENT OF DATA DRIVEN TURBULENCE CLOSURES IN COOLANT SYSTEMS

This work shows the application of Gene Expression Programming to augment RANS turbulence closure modelling, for flows through complex geometry designed for additive manufacturing. Specifically, for the design of optimised internal cooling channels in turbine blades. One of the challenges in internal cooling design is the heat transfer accuracy of the RANS formulation in comparison to higher fidelity methods, which are still not used in design on account of their computational cost. However, high fidelity data can be extremely valuable for improving current lower fidelity models and this work shows the application of data driven approaches to develop turbulence closures for an internally ribbed duct. Different approaches are compared and the results of the improved model are illustrated; first on the same geometry, and then for an unseen predictive case. The work shows the potential of using data driven models for accurate heat transfer predictions even in non-conventional configurations and indicates the ability of closures learnt from complex flow cases to adapt successfully to unseen test cases.

Analysis and Detection of Erosion in Wind Turbine Blades

This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.

A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach.

Multi–Disciplinary Optimizations of Small-Scale Gravitational Vortex Hydropower (SGVHP) System through Computational Hydrodynamic and Hydro–Structural Analyses

Hydropower is a superior energy extraction approach, which has been made to work based on renewable energy sources. In the generation of hydropower, Gravitational Vortex Hydropower (GVHP) plays a predominant contributor role because of its free turbulence-relayed energy utilization concept and flexible as well as compact size. Owing to the huge contribution of GVHP in the hydropower sector, multi-objective-based investigations have emerged. However, there is still insufficient literature available for the technology to precede optimum turbine blade design. Two important categories are involved in these multidisciplinary investigations, in which the first phase, a numerical investigation has been done using ANSYS to identify the location of maximum tangential velocity in a conical basin with different notch angles, conical angles, basin shapes, anddiameters. In this second phase, the focal aim is to carry out the numerical investigation on Gravitation Vortex Turbine Blades (GVTB) for the different geometry in order to get the optimum power output with a high structural lifetime through HSI (Hydro–Structural Interaction) computation. The entire conceptual designs of this SGVHP and its hydro-rotors are modeled with the help of CATIA. ANSYS Fluent is a CFD (Computational Fluid Dynamics) numerical tool, which is primarily used in this paper for all the hydrodynamic analyses. Finally, the standard analytical approaches are used for the comparative determinations of thrust production by hydro-rotors, power extraction by hydro-rotors, and propulsive efficiency for the selection process of best hydro-rotors. HSI analyses are additionally carried out and thereby the suitable lightweight material is picked.

Effect of Types of Grinding Fluid on Grinding Characteristics of CMSX4

Nickel-based heat-resistant alloys are widely used for fabricating the turbine blades in gas turbine engines. An increase in the number of such engines operated by air carriers will increase the demand for high-efficiency machining of nickel-based heat-resistant alloys. However, the high-efficiency grinding of nickel-based heat-resistant alloys is challenging because of their low thermal conductivity and thermal diffusivity, high chemical activity, large work-hardening properties, and high-temperature strength. In this work, the authors propose a high-efficiency grinding technique that uses speed-stroke grinding of nickel-based heat-resistant alloys, and aim to clarify the optimum grinding conditions for the proposed grinding method. The workpiece material is CMSX4 used for the turbine blades. A Cubitron + WA grinding wheel and WA grinding wheel mounted on a linear motor-driven surface grind machines are used for grinding, and the grinding force, surface roughness, and grinding ratio are investigated with the removal rate maintained constant. Two types of grinding fluid are prepared: solution and soluble. From the experiments, it is found that wet grinding features a lower grinding force, smaller surface roughness, and higher grinding ratio when compared to dry-cut grinding. The improvement in the grinding ratio at high table speeds is significant, and it is found to be greater for the soluble-type fluid than for the solution-type fluid.