930 resultados para Spark ignition engines


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The CRP-2/2A core, drilled in western McMurdo Sound in October and November 1998, penetrated 624 m of Quaternary. Pliocene, lower Miocene, and Oligocene glacigenic sediments. The palaeoclimatic record of CRP-2/2A is examined using major element analyses of bulk core samples of fine grained sediments (mudstones and siltstones) and the Chemical Index of Alteration (CIA) of Nesbitt & Young (1982). The CIA is calculated from the relative abundances of AI, K, Ca, and Na oxides, and its magnitude increases as the effects of chemical weathering increase. However, changes in sediment provenance can also affect the CIA, and provenance changes are recorded by shifts in the Al2O3/TiO2 ratios and the Nb contents of these CRP-2/2A mudstones. Relatively low CIA values (40-50) occur throughout the CRP-2/2A sequence, whereas the Al2O3/TiO2 ratio decreases upsection. The major provenance change is an abrupt onset of McMurdo Volcanic Group detritus at ~300 mbsf and is best characterized by a rapid increase in Nb content in the sediments. This provenance shift is not evident in the CIA record, suggesting that a contribution from the Ferrar Dolerite to the older sediments was replaced by an input of McMurdo Volcanic Group material in the younger sediments. If this is true, then the relatively uniform CIA values indicate relatively consistent palaeoweathering intensities throughout the Oligocene and early Miocene in the areas that supplied sediment to CRP-2/2A.

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This paper presents and validates a methodology for integrating reusable software components in diverse game engines. While conforming to the RAGE com-ponent-based architecture described elsewhere, the paper explains how the interac-tions and data exchange processes between a reusable software component and a game engine should be implemented for procuring seamless integration. To this end, a RAGE-compliant C# software component providing a difficulty adaptation routine was integrated with an exemplary strategic tile-based game “TileZero”. Implementa-tions in MonoGame, Unity and Xamarin, respectively, have demonstrated successful portability of the adaptation component. Also, portability across various delivery platforms (Windows desktop, iOS, Android, Windows Phone) was established. Thereby this study has established the validity of the RAGE architecture and its un-derlying interaction processes for the cross-platform and cross-game engine reuse of software components. The RAGE architecture thereby accommodates the large scale development and application of reusable software components for serious gaming.

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The aim of this work is to investigate an efficient CAD based adjoint process chain for calculating sensitivities of the objective function to the CAD parameter in time scales acceptable for industrial design processes.

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Exhaust emissions from diesel engines are a substantial source of air pollution in this country. In recognition of this fact, the Environmental Protection Agency has issued strict new regulations due to take effect -in 1991 and 1994 that will drastically reduce the amount of some pollutants these engines will be allowed to emit. The technology is not currently available to produce diesel engines that can meet these regulations without large penalties in engine performance and efficiency. One technique that offers promise of being able to reduce emissions from both existing engines and new engines is alcohol fumigation.

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In the last years the need to develop more environmentally friendly and efficient cars as led to the development of several technologies to improve the performance of internal combustion engines, a large part of the innovations are focused in the auxiliary systems of the engine, including, the oil pump, this is an element of great importance in the dynamics of the engine as well a considerable energy consumer. Most solutions for oil pumps to this day are fixed displacement, for medium and high speeds, the pump flow rate is higher than the needs of the engine, this excess flow leads to the need for recirculation of the fluid which represents a waste of energy. Recently, technological advances in this area have led to the creation of variable displacement oil pumps, these have become a 'must have' due to the numerous advantages they bring, although the working principle of vane or piston pumps is relatively well known, the application of this technology for the automotive industry is new and brings new challenges. The focus of this dissertation is to develop a new concept of variable displacement system for automotive oil pumps. The main objective is to obtain a concept that is totally adaptable to existing solutions on the market (engines), both dimensionally as in performance specifications, having at the same time an innovative mechanical system for obtaining variable displacement. The developed design is a vane pump with variable displacement going in line with existing commercial solutions, however, the variation of the eccentricity commonly used to provide an variable displacement delivery is not used, the variable displacement is achieved without varying the eccentricity of the system but with a variation of the length of the pumping chamber. The principle of operation of the pump is different to existing solutions while maintaining the ability to integrate standard parts such as control valves and mechanical safety valves, the pump is compatible with commercial solutions in terms of interfaces for connection between engine systems and pump. A concept prototype of the product was obtained in order to better evaluate the validity of the concept. The developed concept represents an innovation in oil pumps design, being unique in its mechanical system for variable displacement delivery.

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Valveless pulsejets are extremely simple aircraft engines; essentially cleverly designed tubes with no moving parts. These engines utilize pressure waves, instead of machinery, for thrust generation, and have demonstrated thrust-to-weight ratios over 8 and thrust specific fuel consumption levels below 1 lbm/lbf-hr – performance levels that can rival many gas turbines. Despite their simplicity and competitive performance, they have not seen widespread application due to extremely high noise and vibration levels, which have persisted as an unresolved challenge primarily due to a lack of fundamental insight into the operation of these engines. This thesis develops two theories for pulsejet operation (both based on electro-acoustic analogies) that predict measurements better than any previous theory reported in the literature, and then uses them to devise and experimentally validate effective noise reduction strategies. The first theory analyzes valveless pulsejets as acoustic ducts with axially varying area and temperature. An electro-acoustic analogy is used to calculate longitudinal mode frequencies and shapes for prescribed area and temperature distributions inside an engine. Predicted operating frequencies match experimental values to within 6% with the use of appropriate end corrections. Mode shapes are predicted and used to develop strategies for suppressing higher modes that are responsible for much of the perceived noise. These strategies are verified experimentally and via comparison to existing models/data for valveless pulsejets in the literature. The second theory analyzes valveless pulsejets as acoustic systems/circuits in which each engine component is represented by an acoustic impedance. These are assembled to form an equivalent circuit for the engine that is solved to find the frequency response. The theory is used to predict the behavior of two interacting pulsejet engines. It is validated via comparison to experiment and data in the literature. The technique is then used to develop and experimentally verify a method for operating two engines in anti-phase without interfering with thrust production. Finally, Helmholtz resonators are used to suppress higher order modes that inhibit noise suppression via anti-phasing. Experiments show that the acoustic output of two resonator-equipped pulsejets operating in anti-phase is 9 dBA less than the acoustic output of a single pulsejet.

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The role of computer modeling has grown recently to integrate itself as an inseparable tool to experimental studies for the optimization of automotive engines and the development of future fuels. Traditionally, computer models rely on simplified global reaction steps to simulate the combustion and pollutant formation inside the internal combustion engine. With the current interest in advanced combustion modes and injection strategies, this approach depends on arbitrary adjustment of model parameters that could reduce credibility of the predictions. The purpose of this study is to enhance the combustion model of KIVA, a computational fluid dynamics code, by coupling its fluid mechanics solution with detailed kinetic reactions solved by the chemistry solver, CHEMKIN. As a result, an engine-friendly reaction mechanism for n-heptane was selected to simulate diesel oxidation. Each cell in the computational domain is considered as a perfectly-stirred reactor which undergoes adiabatic constant- volume combustion. The model was applied to an ideally-prepared homogeneous- charge compression-ignition combustion (HCCI) and direct injection (DI) diesel combustion. Ignition and combustion results show that the code successfully simulates the premixed HCCI scenario when compared to traditional combustion models. Direct injection cases, on the other hand, do not offer a reliable prediction mainly due to the lack of turbulent-mixing model, inherent in the perfectly-stirred reactor formulation. In addition, the model is sensitive to intake conditions and experimental uncertainties which require implementation of enhanced predictive tools. It is recommended that future improvements consider turbulent-mixing effects as well as optimization techniques to accurately simulate actual in-cylinder process with reduced computational cost. Furthermore, the model requires the extension of existing fuel oxidation mechanisms to include pollutant formation kinetics for emission control studies.

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This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

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In this work results for the flexural strength and the thermal properties of interpenetrated graphite preforms infiltrated with Al-12wt%Si are discussed and compared to those for packed graphite particles. To make this comparison relevant, graphite particles of four sizes in the range 15–124 μm, were obtained by grinding the graphite preform. Effects of the pressure applied to infiltrate the liquid alloy on composite properties were investigated. In spite of the largely different reinforcement volume fractions (90% in volume in the preform and around 50% in particle compacts) most properties are similar. Only the Coefficient of Thermal Expansion is 50% smaller in the preform composites. Thermal conductivity of the preform composites (slightly below 100 W/m K), may be increased by reducing the graphite content, alloying, or increasing the infiltration pressure. The strength of particle composites follows Griffith criterion if the defect size is identified with the particle diameter. On the other hand, the composites strength remains increasing up to unusually high values of the infiltration pressure. This is consistent with the drainage curves measured in this work. Mg and Ti additions are those that produce the most significant improvements in performance. Although extensive development work remains to be done, it may be concluded that both mechanical and thermal properties make these materials suitable for the fabrication of piston engines.

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Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.

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For the past three decades the automotive industry is facing two main conflicting challenges to improve fuel economy and meet emissions standards. This has driven the engineers and researchers around the world to develop engines and powertrain which can meet these two daunting challenges. Focusing on the internal combustion engines there are very few options to enhance their performance beyond the current standards without increasing the price considerably. The Homogeneous Charge Compression Ignition (HCCI) engine technology is one of the combustion techniques which has the potential to partially meet the current critical challenges including CAFE standards and stringent EPA emissions standards. HCCI works on very lean mixtures compared to current SI engines, resulting in very low combustion temperatures and ultra-low NOx emissions. These engines when controlled accurately result in ultra-low soot formation. On the other hand HCCI engines face a problem of high unburnt hydrocarbon and carbon monoxide emissions. This technology also faces acute combustion controls problem, which if not dealt properly with yields highly unfavorable operating conditions and exhaust emissions. This thesis contains two main parts. One part deals in developing an HCCI experimental setup and the other focusses on developing a grey box modelling technique to control HCCI exhaust gas emissions. The experimental part gives the complete details on modification made on the stock engine to run in HCCI mode. This part also comprises details and specifications of all the sensors, actuators and other auxiliary parts attached to the conventional SI engine in order to run and monitor the engine in SI mode and future SI-HCCI mode switching studies. In the latter part around 600 data points from two different HCCI setups for two different engines are studied. A grey-box model for emission prediction is developed. The grey box model is trained with the use of 75% data and the remaining data is used for validation purpose. An average of 70% increase in accuracy for predicting engine performance is found while using the grey-box over an empirical (black box) model during this study. The grey-box model provides a solution for the difficulty faced for real time control of an HCCI engine. The grey-box model in this thesis is the first study in literature to develop a control oriented model for predicting HCCI engine emissions for control.