Treatment of Checkerboard Pressure in the Collocated Unstructured Finite-Volume Scheme

In this article, a new concept, “local triangle,” is described. A smoother pressure field is then obtained through the utilization of local triangles in a multiblock local triangulation method. The cell face velocity is corrected with a pressure-correction term which is usually underrelaxed. It is demonstrated that this relaxation factor has to be one, and it cannot be the general relaxation factor. It is indicated that the checkerboard oscillations are more significant at higher cell Reynolds numbers and, in turn, in this article, numerical simulations are mainly performed in turbulent flow.     

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.     

Experimental study of remaining creep life of SA-304L stainless steel using small punch creep test

In this paper, the remaining creep life of SA-304L stainless steel at elevated temperatures is studied. The small punch creep tests (SPCT) were performed on SA-304L virgin and aged materials at constant loads. Times to fracture and the minimum deflection rate in SPCTs were recorded, and the time-temperature parametric analysis was performed, based on experimental results. The constants of the Larson–Miller parameter and the Monkman–Grant relationship were obtained for different consumed creep life ratios, and eventually the equations were established to explain the variation of constants of the Larson–Miller parameter and the Monkman-Grant relationship with respect to the consumed creep life ratio.     

Three-dimensional free vibration of variable thickness thick circular and annular isotropic and functionally graded plates on Pasternak foundation

This paper describes a study of three-dimensional free vibration analysis of thick circular and annular isotropic and functionally graded (FG) plates with variable thickness along the radial direction, resting on Pasternak foundation. The formulation is based on the linear, small strain and exact elasticity theory. Plates with different boundary conditions are considered and the material properties of the FG plate are assumed to vary continuously through the thickness according to power law. The kinematic and the potential energy of the plate-foundation system are formulated and the polynomial-Ritz method is used to solve the eigenvalue problem. Convergence and comparison studies are done to demonstrate the correctness and accuracy of the present method. With respect to geometric parameters, elastic coefficients of foundation and different boundary conditions some new results are reported which may be used as benchmark solutions for future researches.     

Objectification theory and deaf cultural identity attitudes: Roles in deaf women's eating disorder symptomatology.

This study examined the generalizability of direct and mediated links posited in objectification theory among internalization of sociocultural standards of beauty, body surveillance, body shame, and eating disorder symptoms with a sample of Deaf women. The study also examined the role of marginal Deaf cultural identity attitudes within this framework. Data from 177 Deaf women indicated positive relations among internalization, body surveillance, body shame, and eating disorder symptomatology. Consistent with tenets of objectification theory, body shame mediated the links of internalization and body surveillance with eating disorder symptoms. In addition, marginal Deaf identity attitudes (but not hearing, immersion, or bicultural attitudes) were linked uniquely with eating disorder constructs and had significant indirect relations through internalization with body surveillance, body shame, and eating disorder symptoms. Implications for practice and future research are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.     

Evaluation of MODIS data for improved monitoring of the Caspian Sea

Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and infrared data are used to monitor changes in the Caspian Sea coastline. The information extracted from MODIS images is converted into total water volume and mean lake level by combining a digital elevation model (DEM) with remote-sensing data. The elevation estimates were enhanced by reprocessing the MODIS data at the sub-pixel scale. The water volume variations estimated from MODIS data along with DEM are compared to other estimations derived from altimetry data sets, and show fair agreement.     

Preparation of a novel potassium ion imprinted polymeric nanoparticles based on dicyclohexyl 18C6 for selective determination of K+ ion in different water samples

This work reports the first application of the ion imprinting technology for determination of potassium ion by precipitation polymerization method. Ion imprinted polymeric (IIP) nanoparticles were prepared by using dicyclohexyl 18C6 (DC18C6) as a K⁺ ion selective crown ether, in the acetonitrile–dimethylsulfoxide (3:1; v/v) mixture as porogen. The imprint potassium ion was removed from the polymeric matrix using 0.5 M HNO₃. The scanning electron microscopy (SEM) micrographs showed colloidal nanoparticles of 60–90 nm in diameter and slightly irregular in shape. The obtained ion-imprinted particles for K⁺ showed selective recognition with rapid adsorption and desorption processes. It was found that imprinting results in increased affinity of the material toward K⁺ ion over other competitor metal ions with the same charge and/or close ionic radius. The synthesized IIP nanobeads were shown to be promising for solid-phase extraction coupled with flame photometry for determination of trace K⁺ ion in different water samples.