Influence of mechanical activation,ion implantation,and filler type on the formation of a transfer film in tribounits of UHMWPE-based composites

The supermolecular structure of composites obtained by reinforcing an ultrahigh-molecular-weight polyethylene with C and Al₂O₃ nanofibers, Cu and SiO₂ nanoparticles, and Al₂O₃ and AlO (OH) microparticles has been studied by means of X-ray diffraction analysis, infrared spectroscopy, and optical and electron microscopy. It is shown that the filler type (nano-, submicro-, or micro-) determines the character of the supermolecular structure, the evolution of friction transfer films, and the wear resistance of the composites.     

Numerical estimation of the thermal expansion of lamellar single domains in metal/ceramic composites

A porous ceramic preform, the pore structure of which is created via a freeze-casting technique, is infiltrated with a metal melt via sqeeze-casting. The microstructure of the obtained metal/ceramic composites has lamellar domains with geometrical characteristics which are dependent on the manufacturing parameters. The aim of our study is to predict the coefficients of thermal expansion of single domains of parallel Al₂O₃ platelets embedded in Al. For this purpose, a homogenization procedure was employed for microstructure based finite element and micromechanical modeling. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Al2O3-47 nm and Al2O3-36 nm characterizations of nonlinear differential equations for biomedical applications: Magnetized peristaltic transport

Current research models the Al₂O₃ 47nm and Al₂O₃ 36nm nanoparticles transportation through peristalsis with entropy optimization. Conservation laws for mass, momentum and energy are used to model the present flow situation. These equations elaborates the magnetohydrodynamics, Hall, thermal radiation, Joule heating, heat generation and absorption. Convective heat transfer impacts are studied at channel walls. Entropy is modeled in view of thermodynamics second law. Two different expressions for effective viscosity are accounted. Simplification of the modeled equations is done through lubrication assumptions. Solution for momentum equation is obtained analytically and for numerically for temperature equation. Built-in shooting procedure is utilized to obtain the desired numerical results. Later on these obtained results are used to sketch and discussed the flow quantities of interest for the influential parameters accounted in the problem.     

Influence of SiO2 in SiCp on the microstructure and impact strength of Al/SiCp composites fabricated by pressureless infiltration

The effect of SiO₂ in SiC_p and the following processing parameters on the microstructure and impact strength of Al/SiC_p composites fabricated by pressureless infiltration was investigated: Mg content in the aluminum alloy, SiC particle size, and holding time. Preforms of SiC_p in the form of rectangular bars (10 × 1 × 1 cm) were infiltrated at 1150°C in an argon→nitrogen atmosphere for 45 and 60 min by utilizing two aluminum alloys (Al-6 Mg-11 Si and Al-9 Mg-11 Si, wt.%). The results obtained show that the presence of SiO₂ in SiC affects the microstructure and impact strength of the composites significantly. When Al₄C₃ is formed, the impact strength decreases. However, a high proportion of SiC to SiO₂ limits the formation of the unwanted Al₄C₃ phase in the composites. Also, a higher content of Mg in the Al alloy lowers the residual porosity and, consequently, increases the composite strength. The impact strength grows with decrease in SiC particle size and increases considerably when the residual porosity is less than 1%. Russian translation published in Mekhanika Kompozitnykh Materialov, Vol. 42, No. 3, pp. 401–418, May–June, 2006.     

Numerical study of heat transfer enhancement in mixed convection flow along a vertical plate with heat source/sink utilizing nanofluids

Steady, mixed convection laminar boundary layer flow of incompressible nanofluid along the vertical plate with temperature dependent heat source/sink has been investigated numerically. The resulting non-linear governing equations (obtained with the Boussinesq approximation) are solved, using a robust, extensively validated, variational finite element method (FEM) for both spherical and cylindrical shaped nanoparticles with volume fraction ranging up to 4%, with associated boundary conditions and the effect of the parameters governing the problem are discussed. Different water-based nanofluids containing Cu, Ag, CuO, Al₂O₃, and TiO₂ are taken into consideration. The results show that the average Nusselt number is found to decrease for Ag, Cu, CuO, Al₂O₃, and TiO₂. The present study is of immediate interest in next-generation solar film collectors, heat exchangers technology, materials processing exploiting vertical surfaces, geothermal energy storage and all those processes which are highly affected with heat enhancement concept.     

Fibrous Filler-Matrix Interaction in Oxide Composites

The interaction between a fibrous alumina filler and a hydrated zirconia matrix during the fabrication of ceramic composites is studied. The influence of the crystal structure and porosity of the filler on the properties of the ZrO₂-Al₂O₃ composite material is investigated. It is found that, as a result of addition of alumina fibers of cubic and monoclinic structures (- and -phases), the ZrO₂ hydrogel interacts with fibers and solid ZrO₂ solutions of tetragonal structure are formed. These solutions exist over the range of 600 to 1200°C and, at annealing above 1200°C, due to crystallographic transitions the of - and -phases, they break down, namely Al₂O₃ transforms into -corundum and ZrO₂ into a monoclinic modification. However, the material does not fail and a stable porous ceramic composite is formed. To manufacture a composite with improved mechanical characteristics, fibrous alumina fillers with the structure of -corundum must be used.     

Effective conductivity of a random suspension of highly conducting spherical particles

Randomly distributed non-overlapping perfectly conducting spheres are embedded in a conducting matrix with the concentration of inclusions f. Jeffrey (1973) suggested an analytical formula valid up to O(f³) for macroscopically isotropic random composites. A conditionally convergent sum arose in the spatial averaging. In the present paper, we apply a method of functional equations to random composites and correct Jeffrey’s formula. The main revision concerns the proper investigation of the conditionally convergent sum and correction the f²-term. An algorithm for symbolic computations of the effective conductivity tensor is developed and performed up to $O\left(f^{\frac{10}{3}}\right)$. The obtained formulae explicitly demonstrate the dependence of the effective conductivity tensor on the deterministic and probabilistic distributions of inclusions in the f²-term, and in the f³-term. This leads to the conclusion that some previous formulae presented as universal, i.e., valid for all random composites, may be actually applied only to dilute or to special composites when interaction between inclusions do not matter.     

A rank based particle swarm optimization algorithm with dynamic adaptation

The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. This paper presents a variation on the standard PSO algorithm called the rank based particle swarm optimizer, or PSO_rank, employing cooperative behavior of the particles to significantly improve the performance of the original algorithm. In this method, in order to efficiently control the local search and convergence to global optimum solution, the γ best particles are taken to contribute to the updating of the position of a candidate particle. The contribution of each particle is proportional to its strength. The strength is a function of three parameters: strivness, immediacy and number of contributed particles. All particles are sorted according to their fitness values, and only the γ best particles will be selected. The value of γ decreases linearly as the iteration increases. A time-varying inertia weight decreasing non-linearly is introduced to improve the performance. PSO_rank is tested on a commonly used set of optimization problems and is compared to other variants of the PSO algorithm presented in the literature. As a real application, PSO_rank is used for neural network training. The PSO_rank strategy outperformed all the methods considered in this investigation for most of the functions. Experimental results show the suitability of the proposed algorithm in terms of effectiveness and robustness.     

Finite element simulation of the poling process in BaTiO3–CoFe2O4 multiferroic composites

The theoretical background of nonlinear constitutive magneto-ferroelectric behavior as well as the Finite Element implementation are presented. On this basis the polarization in the ferroelectric matrix (BaTiO₃) with embedded dielectric-magnetostrictive particels (CoFe2O₄) is simulated and the resulting effects are analyzed. Numerical simulations focus on the prediction of local crystal orientations and residual stress going along with the poling process, in the future supplying information on favorable electric-magnetic loading sequences. Further, multifield homogenization procedures enable the prediction of the electromagnetomechanical properties of smart multiferroic composites and supply useful means for their optimization. The resulting final state of a poling simulation can be implemented as a starting condition for approximate linear simulations. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Tunneling planer Hall effect in Ni81Fe19/ Al2O3/NixFe1− x junction

Tunneling planer Hall (TPH) effect in Ni₈₁ Fe₁₉/Al₂O₃/Ni_xFe_1−x trilayer junction is different from general planer Hall effect in single-layer film or two-layer junction. This effect concerns the spin-polarized transport, so that the TPH voltage depends on the angle between magnetic vectors of two ferromagnetic layers. The TPH effect is reported to be influenced by composition and magnetic properties of FM layers and the thickness of the insulating layer.     

Am×n矩阵的加边矩阵的奇异性问题

给定m×n阶矩阵A,我们给出了它的加边矩阵M=[A B C O] (1)为非奇的充分必要条件。其中O为r₁×r₂阶零矩阵。把M的逆矩阵记为分块形式M^-1=[A₁ B₂ C₃ O₄]其中C₁为n×m、C₂为n×r₁、C₃为r₂×m、C₄为r₂×r₁阶矩阵。在一定条件下,我们证明了其中的C₁为A的广义逆矩阵A⁺。     

Computing an Eigenvector of a Monge Matrix in Max-Plus Algebra

The problem of finding one eigenvector of a given Monge matrix A in a max-plus algebra is considered. For a general matrix, the problem can be solved in O(n ³) time by computing one column of the corresponding metric matrix Δ(A _λ), where λ is the eigenvalue of A. An algorithm is presented, which computes an eigenvector of a Monge matrix in O(n ²) time.     

Sterological modeling and micromechanical analysis of rubber particle-reinforced epoxy composite materials

Some brittle epoxies can be said to be toughened significantly by a dispersion of rubber particles. Several models have been proposed to explained the role of the rubber particles in toughening. In the present research, by introducing the stereological concept based upon the statistical geometry to the microstructural modeling of composites and considering the interfacial phenomena between a matrix and a particle, the micromechanical modeling by means of the generalized equivalent inclusion method is made of the rubber particle-reinforced epoxy composite having the crack bridging particles whose radius are various in size. By analyzing the micromechanical model, the critical length a_c of the matrix crack whose propagation will be arrested, which implies the fracture toughness of such a composite, can be calculated. The effects of the volume fraction of the particles and the variance of the radii of the particles on the toughness can be evaluated. The results obtained are consistent with the common experimental findings.Presented at the Ninth International Conference on the Mechanics of Composite Materials. Riga. October, 1995.Published in Mekhanika Kompozitnykh Materialov, Vol. 32, No. 3, pp. 317–329, May–June, 1996.     

Polyethylene with improved environmental stress cracking resistance

The stress cracking resistance of low-pressure polyethylene can be improved by introducing finely dispersed mineral additives (TiO₂, SiO₂, Al₂O₃ · SiO₂) and surface-active agents (anthranilic, adipic, and sebacic acids; mannitol). The amounts needed to effectively increase the cracking resistance without detriment to the principal physicomechanical and other characteristics of the material have been determined.Mekhanika Polimerov, Vol. 4, No. 1, pp. 67–71, 1968Communication V of the series "Modification of the Structure and Properties of Polyolefins."     

Evaluation of fully fuzzy matrix equations by fuzzy neural network

In this paper, a new hybrid method based on fuzzy neural network for approximate solution of fully fuzzy matrix equations of the form AX=D$\mathit{AX}=D$, where A and D are two fuzzy number matrices and the unknown matrix X is a fuzzy number matrix, is presented. Then, we propose some definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy matrix equations and fuzzy neural network. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution of fuzzy matrix equations that supposedly has a unique fuzzy solution, a simple algorithm from the cost function of the fuzzy neural network is proposed. To illustrate the easy application of the proposed method, numerical examples are given and the obtained results are discussed.     

Nonasymptotic Bounds on the L 2 Error of Neural Network Regression Estimates

The estimation of multivariate regression functions from bounded i.i.d. data is considered. The L ₂ error with integration with respect to the design measure is used as an error criterion. The distribution of the design is assumed to be concentrated on a finite set. Neural network estimates are defined by minimizing the empirical L ₂ risk over various sets of feedforward neural networks. Nonasymptotic bounds on the L ₂ error of these estimates are presented. The results imply that neural networks are able to adapt to additive regression functions and to regression functions which are a sum of ridge functions, and hence are able to circumvent the curse of dimensionality in these cases.     

Mathematical modeling on the effect of equivalence ratio in emission characteristics of compression ignition engine with hydrogen substitution

The investigation presented in this paper concerns on the computational simulation of emissions characteristics in compression ignition engine with hydrogen substitution. Combustion process has been modeled based on Equilibrium Constants Method (ECM) with MATLAB program to calculate the mole fractions of 18 combustion products when hydrogen is burnt along with diesel fuel at variable equivalence ratios. It can be observed that hydrogen substitution causes significant increase in NH₃, H₂, atom H emissions during rich combustion and OH, NO₂, HNO₃ emissions during lean combustion. As the equivalence ratio increases during rich combustion, mole fractions of HCN, CH₄, CO and atom C decreases with increment of hydrogen substitution. N₂, atom N and CO₂ emissions decrease whereas no significant changes in O₂, NO, O₃ and atom O emissions throughout all equivalence ratios as hydrogen is added to the combustion.     

Fracture in Ceramic-Matrix Composites Reinforced with Strongly Bonded Metal Particles

The resistance of a ceramic matrix composite to the cleavage cracking across a field of strongly bonded, uniformly distributed metal particles is studied. The crack trapping and bridging effects of the metal particles are analyzed by means of calculating the strain energy and the traction work. An explicit expression for the critical energy release rate as a function of particle volume fraction has been obtained. The fracture resistance is independent of elastic properties of the matrix and the sample geometry and is predominantly determined by the size/spacing ratio of the particles. It is shown that the theoretical curves agree with experimental data quite well. The methodology developed in this article can be used in studying the fracture resistances of composites with high filler contents and irregular filler geometries.__________Russian translation published in Mekhanika Kompozitnykh Materialov, Vol. 41, No. 3, pp. 303–318, May–June, 2005.     

An investigation of synchrony in transport networks

The cumulative degree distributions of transport networks, such as air transportation networks and respiratory neuronal networks, follow power laws. The significance of power laws with respect to other network performance measures, such as throughput and synchronization, remains an open question. Evolving methods for the analysis and design of air transportation networks must be able to address network performance in the face of increasing demands and the need to contain and control local network disturbances, such as congestion. Toward this end, we investigate functional relationships that govern the performance of transport networks; for example, the links between the first nontrivial eigenvalue, λ₂, of a network's Laplacian matrix—a quantitative measure of network synchronizability—and other global network parameters. In particular, among networks with a fixed degree distribution and fixed network assortativity (a measure of a network's preference to attach nodes based on a similarity or difference), those with small λ₂ are shown to be poor synchronizers, to have much longer shortest paths and to have greater clustering in comparison to those with large λ₂. A simulation of a respiratory network adds data to our investigation. This study is a beginning step in developing metrics and design variables for the analysis and active design of air transport networks. © 2008 Wiley Periodicals, Inc. Complexity, 2009     

Reducing network and computation complexities in neural based real-time scheduling scheme

Multiprocessor real-time scheduling is an important issue in many applications. A neural network provides a highly effective method to obtain good solutions for real-time scheduling problems. However, multiprocessor real-time scheduling problems include multiple variables; processor, process and time, and the neural networks have to be presented in three dimensions with these variables. Hence, the corresponding neural networks have more neurons, and synaptic weights, and thus associated network and computational complexities increase. Meanwhile, a neural network using the competitive scheme can provide a highly effective method with less network complexity. Therefore, in this study, a simplified two-dimensional Hopfield-type neural network using competitive rule is introduced for solving three-dimensional multiprocessor real-time scheduling problems. Restated, a two-dimensional network is proposed to lower the neural network dimensions and decrease the number of neurons and hence reduce the network complexity; an M-out-of-N competitive scheme is suggested to greatly reduce the computational complexity. Simulation results reveal that the proposed scheme imposed on the derived energy function with respect to process time and deadline constraints is an appropriate approach to solving these class scheduling problems. Moreover, the computational complexity of the proposed scheme is greatly lowered to O(N × T²).