Force ripple compensation in a tubular linear generator for marine renewable generation

Tubular permanent magnet linear generators are a promising generator technology for use in marine renewables. One aspect of their design relates to the conditions necessary for achieving a smooth thrust response from the generator, free from cogging and periodic variations due to spatial harmonics of the flux cutting the generator coils. This paper presents an experimental and finite element study of the sources of thrust ripple in a prototype linear generator for marine generation. A simple self-commutated control scheme is shown, which uses linear Hall-effect sensors and look-up-table based feed-forward compensation to derive the excitation currents required to drive the machine with constant force. Details of the controller's FPGA based implementation are given, including its strategy for detecting sensor failure. © 2011 IEEE.

Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior

Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers. © 2006 IEEE.

Seismic response of stone masonry spires: Analytical modeling

Stone masonry spires are vulnerable to seismic loading. Computational methods are often used to predict the dynamic linear elastic response of masonry towers and spires, but this approach is only applicable until the first masonry joint begins to open, limiting the ability to predict collapse. In this paper, analytical modeling is used to investigate the uplift, rocking and collapse of stone spires. General equations for static equilibrium of the spire under lateral acceleration are first presented, and provide a reasonable lower bound for predicting collapse. The dynamic response is then considered through elastic modal analysis and rigid body rocking. Together, these methods are used to provide uplift curves and single impulse overturning collapse curves for a complete range of possible spire geometries. Results are used to evaluate the historic collapse of two specific stone spires. © 2012 Elsevier Ltd.

Efficient truncation scheme for modeling deepwater mooring lines

This paper is aimed at enabling the confident use of existing model test facilities for ultra deepwater application without having to compromise on the widely accepted range of scales currently used by the floating production industry. Passive line truncation has traditionally been the preferred method of creating an equivalent numerical model at reduced depth; however, these techniques tend to suffer in capturing accurately line dynamic response and so reproducing peak tensions. In an attempt to improve credibility of model test data the proposed truncation procedure sets up the truncated model, based on line dynamic response rather than quasi-static system stiffness. The upper sections of each line are modeled in detail, capturing the wave action zone and all coupling effects with the vessel. These terminate to an approximate analytical model that aims to simulate the remainder of the line. Stages 1 & 2 are used to derive a water depth truncation ratio. Here vibration decay of transverse elastic waves is assessed and it is found that below a certain length criterion, the transverse vibrational characteristics for each line are inertia driven, hence with respect to these motions the truncated model can assume a linear damper whose coefficient depends on the local line properties and vibration frequency. Stage 3 endeavors to match the individual line stiffness between the full depth and truncated models. In deepwater it is likely that taut polyester moorings will be used which are predominantly straight and have high axial stiffness that provides the principal restoring force to static and low frequency vessel motions. Consequently, it means that the natural frequencies of axial vibrations are above the typical wave frequency range allowing for a quasi-static solution. In cases of exceptionally large wave frequency vessel motions, localized curvature at the chain seabed segment and tangential skin drag on the polyester rope can increase dynamic peak tensions considerably. The focus of this paper is to develop an efficient scheme based on analytic formulation, for replicating these forces at the truncation. The paper will close with an example case study of a single mooring under extreme conditions that replicates exactly the static and dynamic characteristics of the full depth line. Copyright © 2012 by the International Society of Offshore and Polar Engineers (ISOPE).

Design methodology of HTS bulk machine for direct-driven wind generation

This paper introduces the design methodology of HTS bulk generator for direct-driven wind turbine. The trap field capability of HTS bulks offer the potential of maintaining similar or even higher magnetic loading level without the iron circuit in the generator. This so-called air-cored design can reduce the weight and increase the power outing per volume of the machine. The detailed design method of the air-cored HTS bulk machine is presented; 3D modeling is applied to consider the total trapped field of bulk arrays; a case study is performed to demonstrate the advantages of air-cored HTS bulk machine over conventional permanent magnet machine. Our results show that the air-cored HTS bulk machine has the potential to maintain the same magnetic loading level as that of the conventional permanent magnet machine. More importantly, it can reduce the total machine weight by 30%. © 2002-2011 IEEE.

Design of brushless doubly-fed machines based: On magnetic circuit modeling

This paper proposes a magnetic circuit model (MCM) for the design of a brushless doubly-fed machine (BDFM). The BDFM possesses advantages in terms of high reliability and reduced gearbox stages, and it requires a fractionally-rated power converter. This makes it suitable for utilization in offshore wind turbines. It is difficult for conventional design methods to calculate the flux in the stator because the two sets of stator windings, which have different pole number, form a complex flux pattern which is not easily determined using common analytical approaches. However, it is advantageous to predict the flux density in the teeth and air-gap at the initial design stage for sizing purposes without recourse finite element analysis. Therefore a magnetic circuit model is developed in this paper to calculate the flux density. A BDFM is used as a case study with FEA validation. © 1965-2012 IEEE.

Strategies for modeling turbulent flows in electronics

Hybrid methods based on the Reynolds Averaged Navier Stokes (RANS) equations and the Large Eddy Simulation (LES) formulation are investigated to try and improve the accuracy of heat transfer and surface temperature predictions for electronics systems and components. Two relatively low Reynolds number flows are studied using hybrid RANS-LES, RANS-Implicit-LES (RANS-ILES) and non-linear LES models. Predictions using these methods are in good agreement with each other, even using different grid resolutions. © 2008 IEEE.

Modeling natural sounds with modulation cascade processes

Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains features that span four orders of magnitude: Sentences ($\sim1$s); phonemes ($\sim10$−$1$ s); glottal pulses ($\sim 10$−$2$s); and formants ($\sim 10$−$3$s). The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis [1]. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscience-inspired algorithms which solve similar tasks and to compare the properties of these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for natural sounds that captures structure across a wide range of time-scales, and to provide efficient learning and inference algorithms. We demonstrate the success of this approach on a missing data task.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Achievements and challenges in machine vision-based inspection of large concrete structures

Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.