Torque measurement on wind turbines and its application in the determination of drivetrain efficiency

The input torque of a wind turbine contains an abundance of information about the operating conditions of the turbine. At the same time it is also a critical input for the efficiency determination of the drivetrain. An appropriate method of torque measurement plays an important role in wind turbines research and helps to further reduce the COE (cost of energy) of the wind turbines. However, a number of challenges are currently restricting the breadth and depth of torque measurement applications in the testing and operation of wind turbines. The research described in this dissertation studies the major challenges posed by the different aspects of torque measurement, including the measuring principle, the calibration and the uncertainty. The efficiency determination of the drivetrain is also studied as an important application, which has the highest demand on the accuracy of torque measurement and its calibration among applications in the wind energy industry. The research proposes new approaches and improvements to cope with the above mentioned challenges. A new method of torque measurement based on the rotary encoders is proposed and realised during a test campaign. During the same test campaign, improvement of the traditional torque measurement based on strain gauges is also demonstrated, where the influence of non-torque loads is greatly reduced by having more measuring points for the torque measurement. A new method is also proposed to address the problem of insufficient accuracy in the drivetrain efficiency determination of a wind turbine on the test bench. With the proposed method, the dependency of the determined efficiency on the accuracy of torque and electrical power measurement can be effectively reduced. As a result, the efficiency can be determined with an uncertainty considerably lower than that of the torque measurement. The method takes advantage of a specially designed test sequence whereby the test bench and the wind turbine drivetrain take turns to run in motor mode and drive the other one which operates in generator mode. The same test sequence is also adopted to develop a method of torque calibration. The method establishes a relationship between the torque and the electrical power using measurements from the two tests where the turbine drivetrain operates in different modes. The calibration uncertainty introduced by the power loss in the drivetrain is thus reduced. Detailed uncertainty analysis for the efficiency determination and torque calibration is carried out in this research to confirm the benefit as well as quantify the effectiveness of the methods proposed. Future work and further applications of the methods proposed are presented at the end of the dissertation

An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine

In this article, a new method of pattern recognition for machine working conditions is presented that is based on time-frequency image (TFI) feature extraction and support vector machines (SVMs). In this study, the Hilbert time-frequency spectrum (HTFS) is used to construct TFIs because of its good performance in non-stationary and non-linear signal analysis. Cyclostationarity signal analysis is a pre-processing method for improving the performance of the HTFS in the construction of TFIs. Feature extraction for TFIs is investigated in detail to construct a feature vector for pattern recognition. Gravity centre and information entropy of TFIs are used to construct the feature vector for pattern recognition. SVMs are used for different working conditions classification by the constructed feature vector because of its powerful performance even for small samples. In the end, rolling bearing pattern recognition is used as an example to testify the effectiveness of this method. According to the result analysis, it can be concluded that this method will contribute to the development of preventative maintenance

Stability of periodic orbits in no-slip billiards

Rigid bodies collision maps in dimension-two, under a natural set of physical requirements, can be classified into two types: the standard specular reflection map and a second which we call, after Broomhead and Gutkin, no-slip. This leads to the study of no-slip billiards—planar billiard systems in which the moving particle is a disc (with rotationally symmetric mass distribution) whose translational and rotational velocities can both change after collisions with the boundary of the billiard domain. This paper, which continues the investigation initiated in Cox and Feres (2017 Dynamical Systems, Ergodic Theory, and Probability: in Memory of Chernov (Providence, RI: American Mathematical Society), is mainly focused on the issue of stability of periodic orbits in no-slip planar billiards. We prove Lyapunov stability of periodic orbits in polygonal billiards of this kind and, for general billiards domains, we obtain curvature thresholds for linear stability at commonly occurring period-2 orbits. More specifically, we prove that: (i) for billiard domains in the plane having piecewise smooth boundary and at least one corner of inner angle less than π, no-slip billiard maps admit elliptic period-2 orbits; (ii) polygonal no-slip billiards under this same corner angle condition always contain small invariant neighborhoods of the periodic point on which, up to smooth conjugacy, orbits of the return map lie on concentric circles; in particular the system cannot be ergodic with respect to the canonical invariant billiard measure; (iii) the no-slip version of the Sinai billiard must contain linearly stable periodic orbits of period 2 and, more generally, we obtain a curvature threshold at which the period-2 orbits go from being hyperbolic to being elliptic; (iv) finally, we make a number of observations concerning periodic orbits in wedge and triangular billiards. Our linear stability results extend those of Wojtkowski for the no-slip Sinai billiard

Refinement of primary Si in hypereutectic Al-Si alloys by intensive melt shearing

Hypereutectic Al-Si based alloys are gaining popularity for applications where a combination of light weight and high wear resistance is required. The high wear resistance arising from the hard primary Si particles comes at the price of extremely poor machine tool life. To minimize machining problems while exploiting outstanding wear resistance, the primary Si particles must be controlled to a uniform small size and uniform spatial distribution. The current industrial means of refining primary Si chemically by the addition of phosphorous suffers from a number of problems. In the present paper an alternative, physical means of refining primary Si by intensive shearing of the melt prior to casting is investigated. Al-15wt%Si alloy has been solidified under varying casting conditions (cooling rate) and the resulting microstructures have been studied using microscopy and quantitative image analysis. Primary Si particles were finer, more compact in shape and more numerous with increasing cooling rate. Intensive melt shearing led to greater refinement and more enhanced nucleation of primary Si than was achieved by adding phosphorous. The mechanism of enhanced nucleation is discussed.EPSRC (grant EP/H026177/1)

Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.Comment: Accepted by IEEE Transactions on Signal and Information Processing over Network

Solitary wave billiards

In the present work we introduce the concept of solitary wave billiards. I.e., instead of a point particle, we consider a solitary wave in an enclosed region and explore its collision with the boundaries and the resulting trajectories in cases which for particle billiards are known to be integrable and for cases that are known to be chaotic. A principal conclusion is that solitary wave billiards are generically found to be chaotic even in cases where the classical particle billiards are integrable. However, the degree of resulting chaoticity depends on the particle speed and on the properties of the potential. Furthermore, the nature of the scattering of the deformable solitary wave particle is elucidated on the basis of a negative Goos-Hänchen effect which, in addition to a trajectory shift, also results in an effective shrinkage of the billiard domain

An investigation into frequency resolution estimation model for impact signal analysis by using Hilbert spectrum and condition classification for marine diesel engine

In this paper, frequency resolution determination method is investigated according to Hilbert spectrum performance for impact signal analysis. A new constructed performance estimation model for the best frequency resolution is put forward in this research for the impact signal pattern recognition. Different parameters in the time-frequency distribution by using Hilbert spectrum are considered in this estimation model for the best frequency resolution determination. To verify the effectiveness of this estimation model, numerical simulation is used for Hilbert spectrum construction analysis. At the same time, different marine diesel engine working condition signals analysis are also used to illustrate the methodology developed in this research and verify the effectiveness. It can be concluded that this method can contribute the development for impact signal analysis by using Hilbert spectrum