167 resultados para Turbine engines.


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A Z-source inverter based grid-interface for a variable-speed wind turbine connected to a permanent magnet synchronous generator is proposed. A control system is designed to harvest maximum wind energy under varied wind conditions with the use of the permanent magnet synchronous generator, diode-rectifier and Z-source inverter. Control systems for speed regulation of the generator and for DC- and AC- sides of the Z-source inverter are investigated using computer simulations and laboratory experiments. Simulation and experimental results verify the efficacy of the proposed approach.

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In today’s world of information-driven society, many studies are exploring usefulness and ease of use of the technology. The research into personalizing next-generation user interface is also ever increasing. A better understanding of factors that influence users’ perception of web search engine performance would contribute in achieving this. This study measures and examines how users’ perceived level of prior knowledge and experience influence their perceived level of satisfaction of using the web search engines, and how their perceived level of satisfaction affects their perceived intention to reuse the system. 50 participants from an Australian university participated in the current study, where they performed three search tasks and completed survey questionnaires. A research model was constructed to test the proposed hypotheses. Correlation and regression analyses results indicated a significant correlation between (1) users’ prior level of experience and their perceived level of satisfaction in using the web search engines, and (2) their perceived level of satisfaction in using the systems and their perceived intention to reuse the systems. A theoretical model is proposed to illustrate the causal relationships. The implications and limitations of the study are also discussed.

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Optimisation of organic Rankine cycles(ORCs for binary cycle applications could play a major role in determining the competitiveness of low to moderate renewable sources. An important aspect of the optimisation is to maximise the turbine output power for a given resource. This requires careful attention to the turbine design notably through numerical simulations. Challenges in the numerical modelling of radial-inflow turbines using high-density working fluids still need to be addressed in order to improve the turbine design and better optimise ORCs. Thispaper presents preliminary 3D numerical simulations of a high-density radial-inflow ORC turbine in sensible geothermal conditions. Following extensive investigation of the operating conditions and thermodynamic cycle analysis, therefrigerant R143a is chosen as the high-density working fluid. The 1D design of the candidate radial-inflow turbine is presented in details. Furthermore, commercially-available software Ansys-CFX is used to perform preliminary steady-state 3D CFD simulations of the candidate R143a radial-inflow turbine for a number of operating conditions including off-design conditions. The real-gas properties are obtained using the Peng–Robinson equations of state.The thermodynamic ORC cycle is presented. The preliminary design created using dedicated radial-inflow turbine software Concepts-Rital is discussed and the 3D CFD results are presented and compared against the meanline analysis.

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A multi-objective design optimization study has been conducted for upstream fuel injection through porous media applied to the first ramp of a two-dimensional scramjet intake. The optimization has been performed by coupling evolutionary algorithms assisted by surrogate modeling and computational fluid dynamics with respect to three design criteria, that is, the maximization of the absolute mixing quantity, total pressure saving, and fuel penetration. A distinct Pareto optimal front has been obtained, highlighting the counteracting behavior of the total pressure against the mixing efficiency and fuel penetration. The injector location and size have been identified as the key design parameters as a result of a sensitivity analysis, with negligible influence of the porous properties in the configurations and conditions considered in the present study. Flowfield visualization has revealed the underlying physics associated with the effects of these dominant parameters on the shock structure and intensity.

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Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

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An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

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Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

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The generation of solar thermal power is dependent upon the amount of sunlight exposure,as influenced by the day-night cycle and seasonal variations. In this paper, robust optimisation is applied to the design of a power block and turbine, which is generating 30 MWe from a concentrated solar resource of 560oC. The robust approach is important to attain a high average performance (minimum efficiency change) over the expected operating ranges of temperature, speed and mass flow. The final objective function combines the turbine performance and efficiency weighted by the off-design performance. The resulting robust optimisation methodology as presented in the paper gives further information that greatly aids in the design of non-classical power blocks through considering off-design conditions and resultant performance.

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This paper presents an uncertainty quantification study of the performance analysis of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multi-purpose Small Power Unit. A deterministic 3D volume-averaged Computational Fluid Dynamics (CFD) solver is coupled with a non-statistical generalized Polynomial Chaos (gPC) representation based on a pseudo-spectral projection method. One of the advantages of this approach is that it does not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic and geometric fields. The stochastic results highlight the importance of the blade thickness and trailing edge tip radius on the total-to-static efficiency of the turbine compared to the angular velocity and trailing edge tip length. From a theoretical point of view, the use of the gPC representation on an arbitrary grid also allows the investigation of the sensitivity of the blade thickness profiles on the turbine efficiency. The gPC approach is also applied to coupled random parameters. The results show that the most influential coupled random variables are trailing edge tip radius coupled with the angular velocity.

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This study investigates the morphology, microstructure and surface composition of Diesel engine exhaust particles. The state of agglomeration, the primary particle size and the fractal dimension of exhaust particles from petroleum Diesel (petrodiesel) and biodiesel blends from microalgae, cotton seed and waste cooking oil were investigated by means of high resolution transmission electron microscopy. With primary particle diameters between 12-19 nm, biodiesel blend primary particles are found to be smaller than petrodiesel ones (21±2 nm). Also it was found that soot agglomerates from biodiesels are more compact and spherical, as their fractal dimensions are higher, e.g. 2.2±0.1 for 50% algae biodiesel compared to 1.7±0.1 for petrodiesel. In addition, analysis of the chemical composition by means of x-ray photoelectron spectroscopy revealed an up to a factor of two increased oxygen content on the primary particle surface for biodiesel. The length, curvature and distance of graphene layers were measured showing a greater structural disorder for biodiesel with shorter fringes of higher tortuosity. This change in carbon chemistry may reflect the higher oxygen content of biofuels. Overall, it seems that the oxygen content in the fuels is the underlying reason for the observed morphological change in the resulting soot particles.

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Knowmore (House of Commons) is a large scale generative interactive installation that incorporates embodied interaction, dynamic image creation, new furniture forms, touch sensitivity, innovative collaborative processes and multichannel generative sound creation. A large circular table spun by hand and a computer-controlled video projection falls on its top, creating an uncanny blend of physical object and virtual media. Participants’ presence around the table and how they touch it is registered, allowing up to five people to collaboratively ‘play’ this deeply immersive audiovisual work. Set within an ecological context, the work subtly asks what kind of resources and knowledges might be necessary to move us past simply knowing what needs to be changed to instead actually embodying that change, whilst hinting at other deeply relational ways of understanding and knowing the world. The work has successfully operated in two high traffic public environments, generating a subtle form of interactivity that allows different people to interact at different paces and speeds and with differing intentions, each contributing towards dramatic public outcomes. The research field involved developing new interaction and engagement strategies for eco-political media arts practice. The context was the creation of improved embodied, performative and improvisational experiences for participants; further informed by ‘Sustainment’ theory. The central question was, what ontological shifts may be necessary to better envision and align our everyday life choices in ways that respect that which is shared by all - 'The Commons'. The methodology was primarily practice-led and in concert with underlying theories. The work’s knowledge contribution was to question how new media interactive experience and embodied interaction might prompt participants to reflect upon the kind of resources and knowledges required to move past simply knowing what needs to be changed to instead actually embodying that change. This was achieved through focusing on the power of embodied learning implied by the works' strongly physical interface (i.e. the spinning of a full size table) in concert with the complex field of layered imagery and sound. The work was commissioned by the State Library of Queensland and Queensland Artworkers Alliance and significantly funded by The Australia Council for the Arts, Arts Queensland, QUT, RMIT Centre for Animation and Interactive Media and industry partners E2E Visuals. After premiering for 3 months at the State Library of Queensland it was curated into the significant ‘Mediations Biennial of Modern Art’ in Poznan, Poland. The work formed the basis of two papers, was reviewed in Realtime (90), was overviewed at Subtle Technologies (2010) in Toronto and shortlisted for ISEA 2011 Istanbul and included in the edited book/catalogue ‘Art in Spite of Economics’, a collaboration between Leonardo/ISAST (MIT Press); Goldsmiths, University of London; ISEA International; and Sabanci University, Istanbul.