A computational fluid dynamic analysis of gas and particle flow in flame spraying

The flame spraying process, which is a common industrial thermal spraying application, has been analyzed by means of three-dimensional computational fluid dynamics (CFD) simulations. The process used at the Volvo Aero Corporation for the coating of fan and compressor housings has been modeled. The process uses the Metco 6P torch (Metco, Westbury, NY), which ejects a mixture of acetylene and oxygen at high speed through a ring of 16 orifices to form the flame. A stream of argon gas flowing through an orifice in the center of the ring carries a powder of nickel-covered bentonite through the flame to the spray substrate. The torch is cooled by a flow of air through an outer ring of 9 orifices. The simulation emulated reality closely by including the individual inlets for fuel, cooling air, and injected particles. The gas combustion was simulated as a turbulent, multicomponent chemically reacting flow. The standard, two-equation k-ε turbulence model was used. The chemical reaction rates appeared as source terms in the species transport equations. They were computed from the contributions of the Arrhenius rate expressions and the Magnussen and Hjertager eddy dissipation model. The first simulations included several intermediate chemical substances whose predicted concentration agreed favorably with measurements. Later, more simplified simulations incorporated only the global chemical reaction involving the initial and the final products, with corrections to the thermal properties being made to account for the missing intermediaries. The gas velocity and temperature fields predicted by the later simulations compared satisfactorily to those predicted by the earlier, more elaborate, ones. Therefore, the final simulations, which incorporated injected particles, were conducted employing the simplified model with only the global reaction. An in-house finite difference code was developed to calculate particle properties. Allowance was made for elliptical shapes, phase changes, and internal heat transfer with regard to the composite material. The particle velocities and temperatures predicted by the final simulations compared fairly well with experimental results obtained with the optical DPV2000 system.     

Investigation of the grain coarsening behavior of some Aluminum alloys

Grain coarsening tests were carried out on Al-4.5 pct Cu and Al-4.5 pct Si alloys. The effects of three variables, melt composition, pour temperature, and mold temperature, were determined. It was found that the macrostructure generally coarsened with increased pour and mold temperatures. Coarsening was extreme in the unrefined alloys but was retarded by the active grain refiners like titanium and columbium. The effect of boron was spectacular in suppressing coarsening tendencies. The results of the investigation support the carbide theory of nucleation as opposed to the peritectic theory.     

Effects of solid solution alloying on creep deformation of aluminum

The effects of solid solution alloying on the creep-rupture properties, deformation characteristics, ductility, and fracture of pure aluminum were studied by means of creep-rupture tests on polished specimens of three alloys each of Al-Cu (0.24, 0.79, and 2.05 pct Cu), Al-Zn (4.93, 9.89, and 19.78 pct Zn), and Al-Mg (0.94, 1.92, and 5.10 pct Mg) at 500°, 700°, and 900°F.     

Chromium distribution between liquid iron and molten basic slags

An equilibrium study was made of the distribution of chromium and oxygen between liquid iron, containing less than 1 pct Cr, and simple slags of the CaO(MgO)-SiO₂-FeO-Cr₂O₃ type in the temperature range 1526° to 1734°C The effects of slag oxidation, temperature, and basicity were observed.     

External sorting on flash storage: reducing cell wearing and increasing efficiency by avoiding intermediate writes

This paper studies the problem of how to conduct external sorting on flash drives while avoiding intermediate writes to the disk. The focus is on sort in portable electronic devices, where relations are only larger than the main memory by a small factor, and on sort as part of distributed processes where relations are frequently partially sorted. In such cases, sort algorithms that refrain from writing intermediate results to the disk have three advantages over algorithms that perform intermediate writes. First, on devices in which read operations are much faster than writes, such methods are efficient and frequently outperform Merge Sort. Secondly, they reduce flash cell degradation caused by writes. Thirdly, they can be used in cases where there is not enough disk space for the intermediate results. Novel sort algorithms that avoid intermediate writes to the disk are presented. An experimental evaluation, on different flash storage devices, shows that in many cases the new algorithms can extend the lifespan of the devices by avoiding unnecessary writes to the disk, while maintaining efficiency, in comparison with Merge Sort.     

Strong Convergence Analysis of Batch Gradient-Based Learning Algorithm for Training Pi-Sigma Network Based on TSK Fuzzy Models

By combining of the benefits of high-order network and TSK (Tagaki-Sugeno-Kang) inference system, Pi-Sigma network is capable to dispose with the nonlinear problems much more effectively, which means it has a compacter construction, and quicker computational speed. The aim of this paper is to present a gradient-based learning method for Pi-Sigma network to train TSK fuzzy inference system. Moreover, some strong convergence results are established based on the weak convergence outcomes, which indicates that the sequence of weighted fuzzy parameters gets to a fixed point. Simulation results show the modified learning algorithm is effective to support the theoretical results.     

Assessing the impact of real-time machine translation on multilingual meetings in global software projects

Communication in global software development is hindered by language differences in countries with a lack of English speaking professionals. Machine translation is a technology that uses software to translate from one natural language to another. The progress of machine translation systems has been steady in the last decade. As for now, machine translation technology is particularly appealing because it might be used, in the form of cross-language chat services, in countries that are entering into global software projects. However, despite the recent progress of the technology, we still lack a thorough understanding of how real-time machine translation affects communication. In this paper, we present a set of empirical studies with the goal of assessing to what extent real-time machine translation can be used in distributed, multilingual requirements meetings instead of English. Results suggest that, despite far from 100 % accurate, real-time machine translation is not disruptive of the conversation flow and, therefore, is accepted with favor by participants. However, stronger effects can be expected to emerge when language barriers are more critical. Our findings add to the evidence about the recent advances of machine translation technology and provide some guidance to global software engineering practitioners in regarding the losses and gains of using English as a lingua franca in multilingual group communication, as in the case of computer-mediated requirements meetings.     

Endogenous games with goals: side-payments among goal-directed agents

Boolean games have been developed as a paradigm for modelling societies of goal-directed agents. In boolean games agents exercise control over propositional variables and strive to achieve a goal formula whose realization might require the opponents’ cooperation. The presence of agents that are goal-directed makes it difficult for an external authority to be able to remove undesirable properties that are inconsistent with agents’ goals, as shown by recent contributions in the multi-agent literature. What this paper does is to analyse the problem of regulation of goal-direct agents from within the system, i.e., what happens when agents themselves are given the chance to negotiate the strategies to be played with one another. Concretely, we introduce endogenous games with goals, obtained coupling a general model of goal-directed agents (strategic games with goals) with a general model of pre-play negotiations (endogenous games) coming from game theory. Strategic games with goals are shown to have a direct correspondence with strategic games (Proposition 1) but, when side-payments are allowed in the pre-play phase, display a striking imbalance (Proposition 4). The effect of side-payments can be fully simulated by taxation mechanisms studied in the literature (Proposition 7), yet we show sufficient conditions under which outcomes can be rationally sustained without external intervention (Proposition 5). Also, integrating taxation mechanisms and side-payments, we are able to transform our starting models in such a way that outcomes that are theoretically sustainable thanks to a pre-play phase can be actually sustained even with limited resources (Proposition 8). Finally, we show how an external authority incentivising a group of agents can be studied as a special agent of an appropriately extended endogenous game with goals (Proposition 11).     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Band selection for hyperspectral images using probabilistic memetic algorithm

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two recently proposed state-of-the-art MAs for band selection are presented.