Self-Diffusion of Iron in Molten Fe-C Alloys

Self-diffusion coefficients of iron in molten Fe-C alloys have been measured by using the capillary method. In addition, the samples have been autoradiographed and sectioned to insure that no significant convection has occurred during the diffusion. The results can be represented by the equation D = 4.3×10^?3 exp (—12.200/RT) for carbon = 4.6 pct and T = 1513° to 1633°K; and D = 1.0×10^?2 exp (—15,700/RT) for carbon = 2.5 pct and T = 1613° to 1673°K. The D values are higher and the heat of activation for diffusion lower in alloys containing more carbon. Calculation based on the Einstein-Stokes equation indicates that the diffusing species is iron ion.     

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.     

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.     

Scheduling evacuation operations

In this paper, we consider a large-scale evacuation problem after a major disaster. The evacuation is assumed to occur by means of a fleet of buses, thus leading to scheduling the evacuation operations by buses [(bus evacuation problem (BEP)]. We propose time-indexed formulations as well as heuristic algorithms such as greedy algorithms and a matheuristic. This matheuristic uses the former formulation to improve the best solution obtained by the greedy heuristics. In computational experiments, we analyze and evaluate the efficiency of the proposed solution algorithms.     

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.