Investigation of cohesive FE modeling to predict crack depth during deep-scratching on optical glasses

Optical glass scratching can induce various types of cracks, among which median cracks are extremely detrimental and penetrate deeply under the surface. Due to deep-scratching process complexity, it is challenging to devise a method to predict median crack depth. Indentation testing has been examined comprehensively in prior research works. It has been found that using the correlation between scratch and indentation testing can simplify predictive method development. In this research, a numerical method based on indentation testing is proposed to determine median crack depth during deep scratching. In the first step, an FE model is configured to simulate the indentation testing process and the Cohesive Zone Method is applied to describe median crack behavior. The cohesive parameters calibrated through experimental indentation testing are implemented in the FE scratch model, and the results are compared with the experimental scratch test results. According to the results, the FE scratch model was enhanced by mode II fracture energy and the modeled friction coefficient. The indentation and scratch experiments were conducted with BK7, F2, Fused silica, K5, Pyrex, Quartz, SF6, and SF19. The experimental results prove that the nonlinearity of the median crack depth curve correlates with K_Ic. A comparison of the experimental and numerical results demonstrates the model is virtually functional for materials with K_Ic below 1000?kPa?m^1/2. Comparisons between the current findings and other studies infer the model and experimental results are accurate and reliable.     

Synthesis and characterization of alumina supported sub-nanoporous SiO2–10 wt%TiO2 membrane for nitrogen separation

Membrane of SiO₂–10TiO₂ was produced by dip coating on mesoporous-titania-coated macroporous alumina support. The amorphous SiO₂–10TiO₂ top layer with thickness of 0.9 μm was obtained as a homogenous and defect free surface. Gas permeation tests show a decrease in permeation in order of CH₄ > N₂ for the support, and N₂ > CH₄ for the multilayer membrane. The membrane delivering promised single gas separation factor for N₂/CH₄ (1.75). It was also observed that the membrane separation factor in binary gas mixture was 12–15% of the single gas separation factor, while the permeation revealed a decrease in gas mixture.     

An innovative method of die design and evaluation of flow balance for thermoplastics extrusion profiles

In this article, a computational and experimental method for flow balancing of a U‐shaped die profile with nonuniform thicknesses is presented. The approach was to implement a flow restricting mechanism along the melt flow path. A parametric study based on the restrictor dimensions was carried out to attain a preliminary optimal design. Simulations were performed using Fluent software to analyze the flow velocity at the die exit. Experimental study was then carried out at various restrictor positions for the purpose of attaining a desirable flow balance. The velocity at various segments of the die exit was measured utilizing an innovative procedure by implementing the “separating blades.” Experimental findings were compared with those of simulations which showed an acceptable agreement. The results suggest that a flexible die can be designed to achieve a flow balance under various processing conditions. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers     

Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

A NEW DISTRIBUTED JOB SCHEDULING ALGORITHM FOR GRID SYSTEMS

Job scheduling is one of the key issues in the design of grid environments. The performance of the grid system severely degrades if a method does not exist to efficiently schedule the user jobs. In this article, a fully distributed, learning automata–based job scheduling algorithm is proposed for grid environments. The proposed method is composed of two types of procedures: in the first, a procedure is run at the grid nodes and in the second, the procedure is run at the schedulers. The proposed algorithm synchronizes the performance of the schedulers by the learning automata that select their actions using the pseudo-random number generators with the same seed. In this method, the grid computational capacity that is allocated to each scheduler is proportional to its workload. To show the efficiency of the proposed method, several simulation experiments were conducted under different grid scenarios. The obtained results show that the proposed algorithm outperforms several well-known methods in terms of makespan, flow time, and load balancing.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.     

Estimating botnet virulence within mathematical models of botnet propagation dynamics

Mathematical models of botnet propagation dynamics are increasingly deemed to have potential for significant contribution to botnet mitigation. Botnet virulence, which comprises network vulnerability rate and network infection rate, is a key factor in those models. In this paper we discuss a practical approach that draws on epidemiological models in biology to estimate the botnet virulence in a network. Our research provides mathematical models of botnet propagation dynamics with concrete measures of botnet virulence, which make those models practical and hence employable in mitigation of real world botnets in a timely fashion. The approach is based on random sampling and follows a novel application of statistical learning and inference in a botnet-versus-network setting. We have implemented this research in the Matlab programming language. In this paper, we discuss an experimental evaluation of the effectiveness of this research with respect to botnet propagation dynamics realistically simulated in a GTNetS network simulation platform.     

Two-dimensional modeling of selective CO oxidation in a hydrogen-rich stream

In this study, carbon monoxide removal by preferential oxidation in a hydrogen-rich stream is simulated between two parallel infinite plates of 150 μm distance. A three-step kinetic is considered that includes carbon monoxide oxidation, hydrogen oxidation and water–gas shift reaction. The walls temperature is in the range of 80–120 °C. The function of this microreactor is to reduce carbon monoxide content from about 2% to below 10 ppm, suitable for use in a PEM fuel cell. Based on the problem conditions, the flow is in the continuum regime and application of the Navier–Stokes equations is admissible. In order to simulate the reacting flow, continuity, conservations of x- & y-momentum, conservation of energy, conservation of species, state equation and reaction rates are simultaneously solved through SIMPLE algorithm by utilizing power-law scheme. Effects of important parameters including walls temperature, steam content, CO content and O₂/CO are assessed. It is observed that increasing walls temperature or oxygen content will increase both CO selectivity and conversion. It is also found that by steam addition, CO conversion is improved without significant change of CO selectivity. These results are in good agreement with previous published data.