An experimental study of forced convective heat transfer for fully developed Al 2 O 3 –water nanofluid in an annulus tube

Nanofluids have been known as practical materials to ameliorate heat transfer within diverse industrial systems. The current work presents an empirical study on forced convection effects of Al₂O₃–water nanofluid within an annulus tube. A laminar flow regime has been considered to perform the experiment in high Reynolds number range using several concentrations of nanofluid. Also, the boundary conditions include a constant uniform heat flux applied on the outer shell and an adiabatic condition to the inner tube. Nanofluid particle is visualized with transmission electron microscopy to figure out the nanofluid particles. Additionally, the pressure drop is obtained by measuring the inlet and outlet pressure with respect to the ambient condition. The experimental results showed that adding nanoparticles to the base fluid will increase the heat transfer coefficient (HTC) and average Nusselt number. In addition, by increasing viscosity effects at maximum Reynolds number of 1140 and increasing nanofluid concentration from 1% to 4% (maximum performance at 4%), HTC increases by 18%.     

Study of the morphology and mechanical properties of polypropylene composites with silica or rice‐husk

The mechanical, morphological behavior and water absorption characteristics of polypropylene (PP) and silica, or PP and rice‐husk, composites have been studied. The silica used in this study as filler was a commercial type produced from soluble glass or rice husks. The compatibilizing effect of PP grafted with monomethyl itaconate (PP‐g‐MMI) and/or with vinyltriethoxysilane (PP‐g‐VTES) as polar monomers on the mechanical properties and water absorption was also investigated. In general, a high loading of the studied fillers in the polymer matrix increases the stiffness and the water absorption capacity. This effect is more noticeable in the tensile modulus of the PP/silica composite with PP‐g‐VTES as compatibilizer. However, the increase of the rice‐husk charge as a natural filler in the PP matrix decreases the stiffness, and in the presence of PP‐g‐MMI as compatibilizer in PP/rice‐husk, the tensile modulus and water absorption of the composite were improved. The better adhesion and phase continuity in the PP/silica and PP/rice‐husk composites with different compatibilizers was confirmed by the morphological study. Copyright © 2004 Society of Chemical Industry     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

Pd/Cu-Free Cobalt-Catalyzed Suzuki and Heck Using Green Bio-Magnetic Hybrid and DFT-Based Theoretical Study

Catalysis Letters - Several highly efficient and magnetically recyclable cobalt catalytic systems were prepared using magnetic chitosan and some safe and available organic compounds...     

Influence of grafted polypropylene on the mechanical properties of mineral‐filled polypropylene composites

The purpose of this work was to study how mineral fillers would behave in a polypropylene (PP) matrix when PP modified with maleic anhydride (MA) and/or itaconic acid (IA) was used as a coupling agent in the preparation of mineral‐filled PP composites. The composites were characterized with tensile mechanical measurements and morphological analysis. The optimum amount of the coupling agent to be used to obtain composites with improved mechanical properties was established. The results indicated that these coupling agents enhanced the tensile strength of the composites significantly, and the extent of the coupling effect depended on the nature of the interface that formed. The incorporation of coupling agents enhanced the resistance to deformation of the composite. The behavior of IA‐modified PP as a coupling agent was similar to that of a commercial MA‐modified PP for the filled PP composites. Evidence of improved interfacial bonding was revealed by scanning electron microscopy studies, which examined the surfaces of fractured tensile test specimens; their microstructures confirmed the mechanical results with respect to the observed homogeneous or optimized dispersion of the mineral‐filler phase in these composites. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2343–2350, 2007     

Polynomial time approximation algorithms for proportionate open‐shop scheduling

This paper deals with the presentation of polynomial time (approximation) algorithms for a variant of open‐shop scheduling, where the processing times are only machine‐dependent. This variant of scheduling is called proportionate scheduling and its applications are used in many real‐world environments. This paper develops three polynomial time algorithms for the problem. First, we present a polynomial time algorithm that solves the problem optimally if , where n and m denote the numbers of jobs and machines, respectively. If, on the other hand, , we develop a polynomial time approximation algorithm with a worst‐case performance ratio of that improves the bound existing for general open‐shops. Next, in the case of , we take into account the problem under consideration as a master problem and convert it into a simpler secondary approximation problem. Furthermore, we formulate both the master and secondary problems, and compare their complexity sizes. We finally present another polynomial time algorithm that provides optimal solution for a special case of the problem where .     

Applying gravitational search algorithm in the QoS-based Web service selection problem

With the growing use of service-oriented architecture for designing next generation software systems,the service composition problem and its execution complexity have become even more important in resp...     

Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine

Neural Computing and Applications - This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain...     

Nadir compromise programming: A model for optimization of multi-objective portfolio problem

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes.