Vacuum degassing of chromium-nickel-molybdenum steel at the Magnitogorsk Metallurgical Combine

The Magnitogorsk Metallurgical Combine has conducted a study of the effect of technological factors on the hydrogen content of chromium-nickel-molybdenum steel after vacuum degassing. It was established that the most important factor is the hydrogen content of the steel before the degassing operation. The study also determined the effects of the circulation coefficient, the duration of the degassing operation, and the vacuum used in the treatment. __________ Translated from Metallurg, No. 7, pp. 68–69, July, 2006.     

Assessing the Market Stability of Chemical Industry Enterprises

Breakdown of a company is examined and the concept of its stability is given. A system of indexes for characterizing the market position of an enterprise in the chemical industry and predicting its dynamics is proposed. An integral index can be an effective tool in the hands of managers in making managerial decisions related to effectively reacting to changes in the company's market position.     

Comparison of Two Statistical Models for the Calculation of Collisions of Particles in Isotropic Turbulence

Two models are given for determining the collision rate of inertial particles in a homogeneous isotropic turbulence. The advantages and disadvantages of the known models of particle collisions are discussed. One of the models suggested by us is based on the assumption that the joint probability density function of gas and particle velocities is a Gaussian distribution. The second model follows from the kinetic equation for the probability density of the relative velocity of two particles. These two models are compared with each other and with the available data of direct numerical simulation.     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

An Integrated Approach to Thermal Management in High-Level Synthesis

Thermal effects are becoming an important factor in the design of integrated circuits due to the adverse impact of temperature on performance, reliability, leakage, and chip packaging costs. Making all phases of the design flow aware of this physical phenomenon helps in reaching faster design closure. In this paper, we present an integrated approach to thermal management in architectural synthesis. Our synthesis flow combines temperature-aware scheduling and binding based on feedback from thermal simulation. We show that our flow is effective in preventing hotspot formation and creating an even thermal profile of the resources. Our integrated thermal management technique on average reduces the peak temperature of the resources by 7.34 degC when compared to a thermal unaware flow without increasing the number of resources across our set of benchmarks     

Selection of structural parameters of a cylindrical-conical hydrocyclone for separation of suspensions by dual-stage flotation

A mathematical model for separation of suspensions with a non-Newtonian disperse medium by dual-stage pressurized flotation in a cylindrical-conical hydrocyclone is developed. A system of differential equations of the convective diffusion and movement of a complex of particles-bubbles is solved by a numerical method. The concentration field is modeled, and integral separation indicators are determined. Values of structural parameters of the hydrocyclone for which the separation indicators depend heavily on the taper angle of the conical section of the housing and the rheological properties of the disperse medium are established. It is demonstrated that dual-stage pressurized flotation makes it possible to reduce considerably the residual concentration of solid-phase particles as compared with single-stage flotation.     

A multigrid accelerated hybrid unstructured mesh method for 3D compressible turbulent flow

 A cell vertex finite volume method for the solution of steady compressible turbulent flow problems on unstructured hybrid meshes of tetrahedra, prisms, pyramids and hexahedra is described. These hybrid meshes are constructed by firstly discretising the computational domain using tetrahedral elements and then by merging certain tetrahedra. A one equation turbulence model is employed and the solution of the steady flow equations is obtained by explicit relaxation. The solution process is accelerated by the addition of a multigrid method, in which the coarse meshes are generated by agglomeration, and by parallelisation. The approach is shown to be effective for the simulation of a number of 3D flows of current practical interest. Sponsored by The Research Council of Norway, project number 125676/410 Dedicated to the memory of Prof. Mike Crisfield, a respected colleague     

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.     

The Shunting of Current and the Resultant Variation of Voltage in Channels of Plasmatrons with Self-Adjusting Length of Electric Arc

The characteristics of pulsation processes in dc plasmatrons with the cathode located in the channel center and the anode in the form of a cylindrical or a conical channel wall are investigated in plasmas of argon and nitrogen at atmospheric pressure. The fast Fourier transform of signals and their subsequent computer processing are used to obtain the dependences of the frequency of fluctuations of the arcing voltage on the working parameters of the plasmatron, namely, the electric arc current, the flow rate of plasma-forming gas, and the channel diameter. For plasmatrons with the self-adjusting length of the electric arc, analysis is performed of the mechanism of reclosing of the anode region of the arc, i.e., of the electric arc shunting associated with the stretching of the current filament by a flow of gas and with the electrodynamic interaction of different filament regions. A formula is derived which defines the dependence of the characteristic frequency of fluctuations of the arcing voltage on the external parameters of the problem, namely, the arc current, the flow rate of the working gas, and the characteristic channel diameter. It is demonstrated that the pattern of the dependence of the frequency of voltage fluctuations on the gas flow rate may vary with the values of the parameter of magnetohydrodynamic interaction. The formula generalizes the experimental results of numerous researchers obtained in a wide range of variation of the external parameters.