4,159 research outputs found

    Review of alternative fuels data bases

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    Based on an analysis of the interaction of fuel physical and chemical properties with combustion characteristics and indicators, a ranking of the importance of various fuel properties with respect to the combustion process was established. This ranking was used to define a suite of specific experiments whose objective is the development of an alternative fuels design data base. Combustion characteristics and indicators examined include droplet and spray formation, droplet vaporization and burning, ignition and flame stabilization, flame temperature, laminar flame speed, combustion completion, soot emissions, NOx and SOx emissions, and the fuels' thermal and oxidative stability and fouling and corrosion characteristics. Key fuel property data is found to include composition, thermochemical data, chemical kinetic rate information, and certain physical properties

    Analytical Study of Gravity Effects on Laminar Diffusion Flames

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    A mathematical model is presented for the description of axisymmetric laminar-jet diffusion flames. The analysis includes the effects of inertia, viscosity, diffusion, gravity and combustion. These mechanisms are coupled in a boundary layer type formulation and solutions are obtained by an explicit finite difference technique. A dimensional analysis shows that the maximum flame width radius, velocity and thermodynamic state characterize the flame structure. Comparisons with experimental data showed excellent agreement for normal gravity flames and fair agreement for steady state low Reynolds number zero gravity flames. Kinetics effects and radiation are shown to be the primary mechanisms responsible for this discrepancy. Additional factors are discussed including elipticity and transient effects

    Multiple-scale turbulence modeling of boundary layer flows for scramjet applications

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    As part of an investigation into the application of turbulence models to the computation of flows in advanced scramjet combustors, the multiple-scale turbulence model was applied to a variety of flowfield predictions. The model appears to have a potential for improved predictions in a variety of areas relevant to combustor problems. This potential exists because of the partition of the turbulence energy spectrum that is the major feature of the model and which allows the turbulence energy dissipation rate to be out of phase with turbulent energy production. The computations were made using a consistent method of generating experimentally unavailable initial conditions. An appreciable overall improvement in the generality of the predictions is observed, as compared to those of the basic two-equation turbulence model. A Mach number-related correction is found to be necessary to satisfactorily predict the spreading rate of the supersonic jet and mixing layer

    A mathematical model for jet engine combustor pollutant emissions

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    Mathematical modeling for the description of the origin and disposition of combustion-generated pollutants in gas turbines is presented. A unified model in modular form is proposed which includes kinetics, recirculation, turbulent mixing, multiphase flow effects, swirl and secondary air injection. Subelements of the overall model were applied to data relevant to laboratory reactors and practical combustor configurations. Comparisons between the theory and available data show excellent agreement for basic CO/H2/Air chemical systems. For hydrocarbons the trends are predicted well including higher-than-equilibrium NO levels within the fuel rich regime. Although the need for improved accuracy in fuel rich combustion is indicated, comparisons with actual jet engine data in terms of the effect of combustor-inlet temperature is excellent. In addition, excellent agreement with data is obtained regarding reduced NO emissions with water droplet and steam injection

    A mathematical model of a large open fire

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    A mathematical model capable of predicting the detailed characteristics of large, liquid fuel, axisymmetric, pool fires is described. The predicted characteristics include spatial distributions of flame gas velocity, soot concentration and chemical specie concentrations including carbon monoxide, carbon dioxide, water, unreacted oxygen, unreacted fuel and nitrogen. Comparisons of the predictions with experimental values are also given

    Report of conference evaluation committee

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    A general classification is made of a number of approaches used for the prediction of turbulent shear flows. The sensitivity of these prediction methods to parameter values and initial data are discussed in terms of variable density, pressure fluctuation, gradient diffusion, low Reynolds number, and influence of geometry

    Characterization of the space shuttle reaction control system engine

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    A computer program was developed and written in FORTRAN 5 which predicts the transient and steady state performance and heat transfer characteristics of a pulsing GO2/GH2 rocket engine. This program predicts the dynamic flow and ignition characteristics which, when combined in a quasi-steady state manner with the combustion and mixing analysis program, will provide the thrust and specific impulse of the engine as a function of time. The program also predicts the transient and steady state heat transfer characteristics of the engine using various cooling concepts. The computer program, test case, and documentation are presented. The program is applicable to any system capable of utilizing the FORTRAN 4 or FORTRAN 5 language

    Convex Independence in Permutation Graphs

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    A set C of vertices of a graph is P_3-convex if every vertex outside C has at most one neighbor in C. The convex hull \sigma(A) of a set A is the smallest P_3-convex set that contains A. A set M is convexly independent if for every vertex x \in M, x \notin \sigma(M-x). We show that the maximal number of vertices that a convexly independent set in a permutation graph can have, can be computed in polynomial time

    Probability of local bifurcation type from a fixed point: A random matrix perspective

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    Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

    Statistical Arbitrage Mining for Display Advertising

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    We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015
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