A Taguchi analysis on structural properties of polypropylene microcellular nanocomposite foams containing Fe2O3 nanoparticles in batch process

Polypropylene (PP) as a thermoplastic polymer has been foamed using batch foaming process. CO₂ is used as the blowing agent of the foaming. Ferrous oxide nanoparticles (nano Fe₂O₃) are also added as reinforcement. Effect of different parameters including nanoparticle weight percentage, foaming temperature and time on the structural properties of PP/nano Fe₂O₃ nanocomposites is investigated using Taguchi approach. Scanning electron microscope results depict that an appropriate microcellular structure is obtained with the cell density of 10⁹ cells/cm³ and almost 1 μm of cell size. Analysis of variance results indicated that foaming temperature is the most significant parameter on the structural properties. Cell density and expansion ratio are decreased by increasing foaming temperature. This phenomenon could be due to the reducing melt strength of polymer/gas mixture. It was also inferred that adding 2 wt% of nanoparticles leads to 80% improvement in cell density while cell size and expansion ratio was decreased.     

Thermal,mechanical, and acoustic properties of silica‐aerogel/UPVC composites

The properties of silica‐aerogel/UPVC composites have been investigated with emphasis on sound and heat insulation. UPVC is a material of construction for window profiles and drainage pipes. Hydrophobic silica aerogels were synthesized using silicate sodium as a precursor through a two‐step sol–gel process. The physical and textural properties of the synthesized silica aerogels such as density, surface area, and particle size were analyzed using SEM and BET analysis. Then, the synthesized aerogels were mixed with Unplastisized Polyvinyl Chloride (UPVC) compound at five different weight ratios in an internal mixer to find out the effects of silica aerogels on the thermal, mechanical, and acoustical characteristics. The prepared UPVC/aerogel composites were characterized for tensile properties, impact strength, hardness, Vicat softening temperature, thermal conductivity, sound absorption, and sound transmission loss. The results revealed that adding silica aerogel in to the matrix of UPVC increases its hardness and softening temperature while decreases impact strength. The thermal conductivity of UPVC was decreased by up to 50% using silica aerogel. The sound absorption property of UPVC was increased up to three times by using silica aerogels due to its high porosity. Silica aerogel increased the maximum sound transmission loss of UPVC in the low frequency range. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44685.     

Design,Construction, and Installation of Movable Electrode Biasing System on Tokamak

The first results of the movable electrode biasing experiments performed on the IR-T1 tokamak are presented. For this purpose, a movable electrode biasing system was designed, constructed, and installed on the IR-T1 tokamak, and then the positive voltage applied to an electrode inserted inside the tokamak limiter and the plasma current, poloidal and radial components of the magnetic fields, loop voltage, and diamagnetic flux in the absence and presence of the biased electrode were measured. Results compared and discussed.     

Model order reduction in hyperelasticity: a proper generalized decomposition approach

This paper deals with the extension of proper generalized decomposition methods to non‐linear problems, in particular, to hyperelasticity. Among the different approaches that can be considered for the linearization of the doubly weak form of the problem, we have implemented a new one that uses asymptotic numerical methods in conjunction with proper generalized decomposition to avoid complex consistent linearization schemes necessary in Newton strategies. This approach results in an approximation of the problem solution in the form of a series expansion. Each term of the series is expressed as a finite sum of separated functions. The advantage of this approach is the presence of only one tangent operator, identical for every term in the series. The resulting approach has proved to render very accurate results that can be stored in the form of a meta‐model in a very compact format. This opens the possibility to use these results in real‐time, reaching kHz feedback rates, or to be used in deployed, handheld devices such as smartphones and tablets. Copyright © 2013 John Wiley & Sons, Ltd.     

On the particle–particle contact effects on the hole cleaning process via a CFD–DEM model

ABSTRACT

The accurate and precise computational models in order to predict the hole cleaning process is one of the helpful assets in drilling industries. Besides the bulk properties such as the flow velocity, particles average size, cleaning fluid properties, etc., that will affect the cleaning process, there is an unanswered question about the microscopic properties of the particles, particularly those which determines the contact characteristics: Do those play a major role or not? The rudimentary answer is not. The first purpose of the present work is to answer this question via a developed computational fluid dynamics coupled with discrete element method (CFD–DEM) in which the six unknown rolling and sliding friction coefficients of particle–particle contact, particle–wall contact, and particle–drill contact are considered as the main microscopic properties of the contacts. The second purpose is to search for optimum values of these coefficients in order to calibrate the CFD–DEM model with the experimental data for a near horizontal well cleaning available in the literature. The verification of the calibrated CFD–DEM model is checked by simulation of the hole cleaning process for different inclination angles of the deviated well. The results indicate the pivotal role of the microscopic properties of the particles on the characteristics of the particle transport mechanism.     

Optimal design of real‐size building structures using quantum‐behaved developed swarm optimizer

In this paper, the quantum‐behaved developed swarm optimizer is proposed for optimal design of real‐size building structures in which the quantum computing is introduced into the standard developed swarm optimizer. In this method, the position‐updating process for the search agents is conducted by simultaneous utilization of the so far best position of all particles, center of mass of all particles, so far best position of each particle, and the mean best position of all particles in which the first two of these aspects satisfy the exploration phase of the algorithm, whereas the other two are utilized for improving the exploitation phase of the proposed method. In order to evaluate the capability of the proposed method in dealing with difficult optimization problems, three real‐size building structures are considered, namely, a 10‐story building with 1,026 structural members, a 20‐story building with 3,860 members, and a 60‐story building with 8,272 members. The overall performance of the proposed quantum‐behaved developed swarm optimizer is compared with that of the standard developed swarm optimizer and other approaches. The obtained results proved that the proposed method is capable of providing better results for the considered examples than are the other algorithms.     

A new approach for prioritization in fuzzy AHP with an application for selecting the best tunnel ventilation system

Tunnel design and construction consist of multiple sub-processes among which the selection of proper ventilation system is one of the most important issues. In this problem, several criteria are included in the process; therefore, multi-attribute decision making (MADM) tools should be used for the purpose. One of frequently used MADM methods is fuzzy analytic hierarchy process (AHP) in which uncertainty is also taken into account in the decision-making process. In this paper, using fuzzy data envelopment analysis, a new approach is presented for ranking the alternatives in fuzzy AHP. In this approach, the fuzzy nature of data is kept in all steps of the approach. Furthermore, to determine the consistency level of fuzzy pairwise comparison matrix, an appropriate index is used. Then, the proposed approach is used for selecting the best tunnel ventilation system.     

Design,modeling, and analysis of a high performance piezoelectric energy harvester for intelligent tires

Basic parameters affecting vehicle safety and performance such as pressure, temperature, friction coefficient, and contact‐patch dimensions are measured in intelligent tires via sensors that require electric power for operation and wireless communication to be synchronized to the vehicle monitoring and control system. Piezoelectric energy harvesters (PEHs) can extract a fraction of energy that is wasted as a result of deflection during rolling of tires, and this extracted energy can be used to power up sensors embedded in intelligent tires. A new design of PEH inspired from Cymbal PEHs is introduced, and its performance is evaluated in this paper. Cymbal PEHs are proven to be useful in vibration energy harvesting, and in this paper, for the first time, the modified shape of Cymbal energy harvester is used as strain‐based energy harvester for the tire application. The shape of the harvester is adjusted in a way that it can be safely embedded on the inner surface of tires. In addition to the high performance, ease of manufacturing is another advantage of this new design. A multiphysics model is developed and validated to determine the output voltage, power, and energy of the designed PEH. The modeling results indicated that the maximum output voltage, the maximum electric power, and the accumulated harvested energy are about 3.5 V, 2.8 mW, and 24 mJ/rev, respectively, which are sufficient to power two sensors. In addition, the possibility is shown to supply power to five sensors by increase in piezoelectric material thickness. The effect of rolling tire temperature on the performance of the proposed PEH is also studied.     

Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines

The development and implementation of better control strategies to improve the overall performance of a plant is often hampered by the lack of available measurements of key quality variables. One way to resolve this problem is to develop a soft sensor that is capable of providing process information as often as necessary for control. One potential area for implementation is in a hot steel rolling mill, where the final strip thickness is the most important variable to consider. Difficulties with this approach include the fact that the data may not be available when needed or that different conditions (operating points) will produce different process conditions. In this paper, a soft sensor is developed for the hot steel rolling mill process using least‐squares support vector machines and a properly designed bias update term. It is shown that the system can handle multiple different operating conditions (different strip thickness setpoints, and input conditions).     

Stealing PINs via mobile sensors: actual risk versus user perception

In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users’ perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.