Target vector optimization of composite box beam using real-coded genetic algorithm: a decomposition approach

This paper aims to obtain the optimal composite box-beam design for a helicopter rotor blade. The cross-sectional dimensions and the ply angles of the box beam are considered as design variables. The objective is to optimize the box beam to attain a target vector of stiffness values and maximum elastic coupling. The target vector is the optimal stiffness values of helicopter rotor blade obtained from a previous aeroelastic optimization study. The elastic couplings introduced by the box beam have beneficial effects on the aeroelastic stability of helicopter. The optimization problem is addressed by decomposing the optimization into two levels, a global level and a local level. The box-beam cross-sectional dimensions are optimized at the global level. The local-level optimization is a subproblem which finds optimal ply angles for each cross-sectional dimension considered in the global level. Real-coded genetic algorithm (RCGA) is used as the optimization tool in both the levels of optimization. Hybrid operators are developed for the RCGA, thereby enhancing the efficiency of the algorithm. Min–max method is used to scalarize the multiobjective functions used in this study. Optimal geometry and ply angles are obtained for composite box-beam designs with ply angle discretization of 1010, 1515, and 45^o45^o.     

Development of Ni-Al2O3In-Situ Nanocomposite by Reactive Milling and Spark Plasma Sintering

In the present study, Ni-30 vol pct Al₂O₃ in-situ nanocomposite was developed by reactive milling of NiO-Al-Ni powder mixture followed by spark plasma sintering (SPS). During milling, fcc to hcp transformation was observed in Ni(Al) phase and it transformed back to fcc phase around 773 K (500 °C). The hardness and yield strength of Ni-30 vol pct Al₂O₃ nanocomposite are approximately two times higher than that of pure Ni of similar grain size. The improved mechanical properties of nanocomposite are attributed to the presence of alumina particles of nanometer size.     

Analysis of Stress Control on 33-kV Non-ceramic Insulators Using Finite-element Method

In this article, analysis of electric field stress and electric potential distributions of a 33-kV composite insulator and factors that affect the electric field are discussed. Accordingly, the article is classified into two parts. In the first part, the designs of three different configurations of non-ceramic (composite) insulator based on their geometry modification in end fittings and water shed are discussed. The electrical performances are analyzed using electric field and electrical potential distribution. In the second part, a reduction of the electric field near the end fittings is done to control the electric field stress intended for long-term performance. For that, the grading material is placed between the core and housing materials by fitting the arcing horn near the end fittings. A 33-kV composite insulator is modeled in two dimensions by the finite-element method to investigate the electric field and electric potential distribution under normal and polluted conditions. The results reveal that an optimum installation of an arcing horn at the high-voltage end in the composite insulator with silicone rubber overlapping the edges of metal end fittings made a significant reduction in electric field stress on 33-kV non-ceramic insulators.     

A simulation based heuristic discrete particle swarm algorithm for generating integrated production–distribution plan

Deciding the strategy for production and distribution in a stochastic demand scenario is important for the manufacturing industries. An integrated production–distribution plan considering regular, overtime and outsourced production costs along with inventory holding, backorder, hiring/laying-off and trip-wise distribution costs is developed for a renowned bearing manufacturing industry producing three types of products at three locations. Demand is assumed to vary uniformly and a novel simulation based heuristic discrete particle swarm optimization (DPSO) algorithm is used for obtaining the best production–distribution plan that serves as a trade-off between holding inventory and backordering products. The algorithm also uses an innovative regeneration type constraint handling method which does not require a penalty operator. In addition to the bearing manufacturing industry data set, two other test data sets are also solved. The simulation based optimization approach gives good approximate solutions for the stochastic demand problems.     

Soil Fulvic Acid Degradation By Ozone In Aqueous Medium

Armadale Armadale soil fulvic acid (SFA) was found to contain several loosely bound organic impurities which could be removed by ethyl acetate extraction. The ozonation of purified Armadale SFA at a variety of dosages was characterized by monitoring the UV absorbance, weight loss, pH changes, total acidity, molecular weight and elemental composition. SFA could only be partially degraded even under ozone dosages as high as SFA/O₃ (w/w) of 1:6. At high ozone dosages (SFA/O3 1:6) mostly aliphatic compounds rich in oxygen were produced, whereas at low ozone dosages (SFA/O₃ < 1:0.5) mainly benzene polycarboxylic acids and polyhydroxy benzene polycarboxylic acids were found. The solid material bisolated from the chlorination (0.5 mg/L residual level) of residues from the ozonation (SFA/O₃ 1:0.2 to 1:0.5) of Contech SFA did not contain any chlorinated products.     

Clustering and classification with inertia weight and elitism-based particle swarm optimization

Pattern Analysis and Applications - Clustering and classification-based pattern recognition techniques are widely used in various domains. While many approaches exist to perform these tasks, it...     

An exploratory investigation of the antecedents and impact of internet usage: an individual perspective

Internet usage in the US workplace is increasing at a phenomenal rate. This exploratory study examines factors influencing employee internet usage and individual perceptions of the consequences of such usage. Using the Theory of Reasoned Behaviour, a questionnaire was designed and circulated to part time MBA students in north-east United States. This preliminary study suggests that the personal factors of web skills and playfulness are associated with perceived internet usefulness, the degree of internet usage, and have both positive (enhanced job characteristics, job satisfaction) and negative (increased inefficiency) impacts. Neither the personal variables of age and gender nor any of the organizational variables are important antecedent variables. To those who perceive the internet as intimidating, there was, understandably, less internet usage. Perceived usefulness was positively related to increased time of use and internet impacts. In general, the findings indicate that extending the research on microcomputers to internet usage is a promising research focus. On the basis of this study, the leadership challenge is to harness the tremendous potential of the internet, working to control and improve inefficiencies while not discouraging internet usage.     

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.     

An AHP based heuristic DPSO algorithm for generating multi criteria production–distribution plan

Multi-criteria integrated production–distribution problems were solved by many researchers using different optimization techniques. A novel analytic hierarchy process (AHP) based heuristic discrete particle swarm optimization (DPSO) algorithm is proposed in this research for solving difficult production–distribution problems. A bearing manufacturing industry's case is considered in this paper and the mathematical model is formulated as mixed integer linear programming (MILP) problem considering multi-period, multi-product and multi-plant scenarios. The three major objectives considered are total cost reduction, minimization of change in labor levels and percentage under-utilization. The results of the AHP based heuristic DPSO algorithm are compared with the branch and bound algorithm results generated using LINGO software. The approach gives good near optimal solutions. In addition to the bearing manufacturing industry dataset, two other test datasets are also solved.