Dynamic properties of pultruded natural fibre reinforced composites using Split Hopkinson Pressure Bar technique

The paper presents results on dynamic mechanical properties of jute, and kenaf fibre reinforced composites at various strain rates using compression Split Hopkinson Pressure Bar technique. The stress–strain curves for both pultruded natural fibre reinforced composites at strain rates of nearly 1400 s⁻¹ are illustrated and then compared with statically determines stress–strain curve (1.0 × 10⁻³ s⁻¹). Results show that the strain rate does affect the value of dynamic compressive properties of both pultruded natural fibre composites. Higher dynamic compression modulus and 2.5% flow stress were recorded for higher strain rates as compared to lower strain rate over the range of strain rates investigated. Under dynamic loading, jute fibre reinforced composites recorded the highest value of dynamic response in terms of compression modulus, 2.5% flow stress and compressive strength than that of kenaf fibre reinforced composites. In addition, kenaf fibre reinforced composites is more severely damaged as compared to jute fibre reinforced composites for all tested strain rate.     

A Note on Nonproportional Damping

This note deals with three aspects of nonproportional damping in linear damped vibrating systems in which the stiffness and damping matrices are not restricted to being symmetric and positive definite. First, we give results on approximating a general damping matrix by one that commutes with the stiffness matrix when the stiffness matrix is a general diagonalizable matrix, and the damping and stiffness matrices do not commute. The criterion we use for carrying out this approximation is closeness in Euclidean norm between the actual damping matrix and its approximant. When the eigenvalues of the stiffness matrix are all distinct, the best approximant provides justification for the usual practice in structural analysis of disregarding the off-diagonal terms in the transformed damping matrix. However, when the eigenvalues of the stiffness matrix are not distinct, the best approximant to a general damping matrix turns out to be related to a block diagonal matrix, and the aforementioned approximation cannot be justified on the basis of the criterion used here. In this case, even when the damping and stiffness matrices commute, decoupling of the modes is not guaranteed. We show that for general matrices, even for symmetric ones, the response of the approximate system and the actual system can be widely different, in fact qualitatively so. Examples illustrating our results are provided. Second, we present some results related to the difficulty in handling general, nonproportionally damped systems, in which the damping matrix may be indefinite, by considering a simple example of a two degrees-of-freedom system. Last, we use this example to point out the nonintuitive response behavior of general nonproportionally damped systems when the damping matrix is indefinite. Our results point to the need for great caution in approximating nonproportionally damped systems by damping matrices that commute with the stiffness matrix, especially when considering general damping matrices. Such approximations could lead to qualitatively differing responses between the actual system and its proportionally damped approximation.     

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

Wind turbine gearbox diagnosis is a vital tool for maintaining wind turbine operation and safety. The gearbox vibration signal is invariably complex and variable, and useful information and features are difficulty of extraction. Recently, a new and adaptive signal decomposition method, known as variational mode decomposition (VMD), has been proposed, which helps to improve the efficiency and effectiveness of extracting features from gearbox vibration signals. However, the performance of the VMD method mainly depends on its input parameters, especially the mode number and balancing parameter (also called the quadratic penalty term). Hence, this paper proposes a selection method for an optimized VMD parameter using differential evolution algorithm (DEA), also called VMDEA. Firstly, the VMDEA is used to select optimized VMD input parameters for each of the vibration signals. Following this, VMD decomposes each vibration signal into sets of subsignals using the selected optimized parameter. Multidomain features are extracted from VMD reconstructed signals and are passed on to the extreme learning machine (ELM) for fault classification. This study can thus provide a good solution for determining an optimized VMD parameter for decomposing vibration signals and can also provide a more efficient and effective diagnostic approach to wind turbine gearbox maintenance.     

Improved Test Case Selection Algorithm to Reduce Time in Regression Testing

Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient test case selection (TCS) algorithm for RT. Our proposed technique decreases the execution time and redundancy of the duplicate test cases (TC) and detects only modified changes that appropriate to the modifications in test cases. To reduce execution time for TCS, evaluation results of our proposed approach are established on fault detection, redundancy and already executed test case. Results indicate that proposed technique decreases the inclusive testing time of TCS to execute modified test cases by, on average related to a method of Hybrid Whale Algorithm (HWOA), which is a progressive TCS approach in regression testing for a single product.     

Mechanical properties of nanosilica/polypropylene composites under dynamic compression loading

This article presents results on the dynamic mechanical properties of PP‐SiO₂ nanocomposites, with nanosilica contents of 1, 3, and 5% by weight, at various strain rates using a Split Hopkinson Pressure Bar (SHPB) apparatus. The specimens were prepared using a hot compression technique. The dynamic mechanical characteristics, of PP‐SiO₂ nanocomposites, are illustrated in terms of stress–strain curves, up to nearly 1100 s⁻¹ of strain rates. From the results, the yield stress, compression modulus, and compressive strength of the composites, were significantly influenced by the strain rates and nanosilica contents. The values of strain rate sensitivity, and dissipation energy of the composites at various strain rates, were also determined. It was found that the strain rate sensitivity, and the dissipation energy, increased with increasing strain rates. In addition, it was observed that the composites experienced more severe damage under a high strain rate loading, compared to a low strain rate loading. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers     

Evaluation of the performance of a micro gas turbine cogeneration system in a sewage treatment facility: Optimized configuration of a cogeneration system

In this study, efficient configuration of a biogas‐fuelled cogeneration system (CGS) in a sewage treatment facility was investigated. The efficient configuration of the CGS was clarified on the basis of the relationship between exhaust heat recovery efficiency (η_ehr) of the CGS and the ratio of yearly average heat demand to yearly average biogas production of the facility (Q_h.d/Q_b.p). The CGS was assumed to be used under Q_h.d/Q_b.p<η_ehr,Q_h.d/Q_b.p≈η_ehr, and Q_h.d/Q_b.p>η_ehr conditions. It was found that although the CGS was able to cover total heat demand of the facility by only consuming biogas produced, from the point of view of energy utilization, reduction of unutilized biogas and reduction of electricity demand efficiencies, the most efficient CGS was obtained under the Q_h.d/Q_b.p≈η_ehr condition. Under the Q_h.d/Q_b.p≈η_ehr condition, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were 0.64, 0.99, and 0.32, respectively, whereas under the Q_h.d/Q_b.p<η_ehr and Q_h.d/Q_b.p>η_ehr conditions, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were in ranges of 0.56–0.64, 0.43–0.99, and 0.16–0.20, respectively. A more efficient system can be obtained if a CGS with lower η_ehr such as a fuel cell is used under the Q_h.d/Q_b.p<η_ehr condition and if a CGS with higher η_ehr such as a steam turbine is used under the Q_h.d/Q_b.p>η_ehr condition. © 2011 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.20389     

Comparison of Fatty Acid Methyl and Ethyl Esters as Biodiesel Base Stock: a Review on Processing and Production Requirements

Fatty acid methyl esters (FAME) were the first fatty acid esters to be introduced for use as biodiesel. However, there is a growing interest in the use of fatty acid ethyl esters (FAEE) in biodiesel. Both FAME and FAEE have their own unique advantages and disadvantages. These differences are ultimately attributable to the structural differences imparted by the alcohols used in their production. Sources of reactants as well as their safety issues, are a focus of this review. Also reviewed are the comparative characteristics and properties of both biodiesel types in terms of physicochemical features and performance. Processing requirements, reaction times and molar ratios of alcohol to oil, together with problems and drawbacks, are discussed. Recent developments on improving the yield of biodiesel, include mixing methanol and ethanol in the same reaction with ethanol acting as a co-solvent, and enzymatic methanolysis and ethanolysis are also highlighted.     

Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.     

Use of the Phen‐NaDPO:Sn(SCN)2 Blend as Electron Transport Layer Results to Consistent Efficiency Improvements in Organic and Hybrid Perovskite Solar Cells

A simple approach that enables a consistent enhancement of the electron extracting properties of the widely used small‐molecule Phen‐NaDPO and its application in organic solar cells (OSCs) is reported. It is shown that addition of minute amounts of the inorganic molecule Sn(SCN)₂ into Phen‐NaDPO improves both the electron transport and its film‐forming properties. Use of Phen‐NaDPO:Sn(SCN)₂ blend as the electron transport layer (ETL) in binary PM6:IT‐4F OSCs leads to a remarkable increase in the cells' power conversion efficiency (PCE) from 12.6% (Phen‐NaDPO) to 13.5% (Phen‐NaDPO:Sn(SCN)₂). Combining the hybrid ETL with the best‐in‐class organic ternary PM6:Y6:PC₇₀BM systems results to a similarly remarkable PCE increase from 14.2% (Phen‐NaDPO) to 15.6% (Phen‐NaDPO:Sn(SCN)₂). The consistent PCE enhancement is attributed to reduced trap‐assisted carrier recombination at the bulk‐heterojunction/ETL interface due to the presence of new energy states formed upon chemical interaction of Phen‐NaDPO with Sn(SCN)₂. The versatility of this hybrid ETL is further demonstrated with its application in perovskite solar cells for which an increase in the PCE from 16.6% to 18.2% is also demonstrated.     

Ultra-Dense Networks: Integration with Device to Device (D2D) Communication

Wireless Personal Communications - The current network architecture scene can be a challenge due to the increasing number of user equipment and its diversity. Furthermore, the demand for greater...