Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

Spark ignition (SI) engines have a nonlinear dynamic system with inherent uncertainties and unpredictable disturbances. The identification of a nonlinear system is vital in many fields of engineering. In this study, SI engine torque is identified from an input–output measurement. This study aims to propose a dynamic nonlinear model that uses an adaptive neuro‐fuzzy inference system and a nonlinear auto‐regressive with exogenous input structure to identify the dynamic nonlinear behavior of an SI engine. Considerable good performance is achieved using the adaptive neuro‐fuzzy inference system nonlinear auto‐regressive with exogenous input method. For model validation, the proposed method is compared with the more conventional identification approach called the Hammerstein method. The results show that the two methods are in excellent agreement. The Hammerstein model was chosen because its identification result of the SI system was studied previously by the author. Validation results prove that the ability of the proposed model can capture the highly nonlinear behavior of the SI system.     

One-information suboptimal control repercussion on the fine structure of wall turbulence

The suboptimal control with the cost function directly connected to the wall shear and introduced for a while has been revisited through direct numerical simulations of high temporal and spatial resolution. Its effect on the fine structure of the wall turbulence has been analyzed in details, essentially through the spanwise vorticity transport mechanism. It is shown that only half of the viscous sublayer is mainly affected by the control. The actuation efficiency is limited in terms of the wall shear stress reduction, but is high as long as the turbulent wall activity is concerned. The wall shear stress is reduced due both to the reduction of the shear production in the viscous sublayer and to the contribution of the turbulent body force. The dissipation involving in the streamwise vorticity fluctuations transport equation increases significantly and overcomes the production in a thin layer near the wall leading to a drastic diminution of the turbulent wall shear stress fluctuations.     

Large-solid-core square-lattice photonic crystal fibers

A large-solid-core fiber design comprised by omitting nine air-holes at the center of square-lattice photonic crystal is investigated for the constant air-hole diameter and for the constant pitch length of air-holes. The fiber characteristics are calculated for both structures, and the results are compared. In the calculations, the plane-wave expansion method is used for effective indices, and we utilized the step-index fiber and Gaussian mode profile approximations for obtaining the effective V-number and the effective mode area respectively.     

A quantitative method of photoadsorption determination for irradiated catalyst in liquid–solid system

A method for determining the amount of substrate adsorbed onto the catalyst surface in liquid–solid photocatalytic system under reaction conditions (photoadsorption) is proposed. The method has been applied to the photocatalytic oxidation of benzyl alcohol carried out in aqueous suspensions of a home prepared TiO₂ catalyst. The influence of alcohol concentration, catalyst amount and irradiation power on photoadsorption was investigated. The photoadsorption per unit mass of catalyst increased by increasing concentration of benzyl alcohol and irradiation power but it decreased, increasing the catalyst amount. By taking into account the photoadsorption phenomena, the kinetic modeling of the photooxidation process has been carried out and the parameters of the model have been determined. The results indicate that all these parameters are favorably affected by an increase of the photon flow absorbed per unit mass of catalyst.     

Production of anorthite refractory insulating firebrick from mixtures of clay and recycled paper waste with sawdust addition

Production of porous anorthite refractory insulating firebricks from mixtures of two different clays (K244 clay and fireclay), recycled paper processing waste and sawdust addition are investigated. Suitability of alkali-containing-clay, low-alkali fireclay, pore-making paper waste and sawdust in the products was evaluated. Prepared slurry mixtures were shaped, dried and fired. Highly porous anorthite ceramics from the mixtures with up to 30% sawdust addition were successfully produced. Physical properties such as bulk density, apparent porosity, percent linear change were investigated as well as the mechanical strengths and thermal conductivity values of the samples. Thermal conductivities of the samples produced from fireclay and recycled paper waste decreased from 0.25 W/mK (1.12 g/cm³) to 0.13 W/mK (0.64 g/cm³) with decreasing density. Samples were stable at high temperatures up to 1100 °C, and their cold strength was sufficiently high. The porous anorthite ceramics produced in this study can be used for insulation in high temperature applications.     

Thermoelectric Properties of Triple-Filled BaxYbyInzCo4Sb12 Skutterudites

Indium-filled skutterudites are promising power generation thermoelectric materials due to the presence of an InSb nanostructure that lowers the thermal conductivity. In this work, we have investigated thermoelectric properties of triple-filled Ba _x Yb _y In _z Co₄Sb₁₂ (0 ≤ x, y, z ≤ 0.14 actual) compounds by measuring their Seebeck coefficient, electrical conductivity, thermal conductivity, and Hall coefficient. All samples were prepared by a melting–annealing–spark plasma sintering method, and their structure was characterized by x-ray diffraction and transmission electron microscopy (TEM). TEM results show the development of an InSb nanostructure with a grain size of 30 nm to 500 nm. The nanostructure is present in all samples containing In and is also detected by specific heat measurements. The Seebeck and Hall coefficients indicate that the compounds are n-type semiconductors. Electrical conductivity increases with increasing Ba content. Thermal conductivity is strongly suppressed upon the presence of In in the skutterudite structure, likely due to enhanced boundary scattering of phonons on the nanometer-scale InSb inclusions. The highest thermoelectric figure of merit is achieved with Ba_0.09Yb_0.07In_0.06Co₄Sb_11.97, reaching ZT = 1.25 at 800 K.     

Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.     

Identification of materials with magnetic characteristics by neural networks

In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises.