Influence of different parameters and their coupled effects on the stability of alumina nanofluids by a fractional factorial design approach

Nanofluids have been introduced as new-generation fluids able to improve energy efficiency in heat exchangers. However, stability problems related to both agglomeration and sedimentation of nanoparticles have limited industrial-level scaling. A fractional factorial experimental 2^k?1 design was applied in order to evaluate the effects of nanoparticle concentration, surfactant type and concentration, ultrasonic amplitude as well as ultrasonic time on the stability of alumina (Al₂O₃) nanofluids. Commercial alumina nanoparticles (particle diameter <50 nm) were dispersed in deionized water using ultrasonic probe dispersion equipment. Sodium dodecylbenzenesulfonate (SDBS) and cetyltrimethylammonium bromide (CTAB) were used as surfactants. The stability of the nanofluids in static mode was monitored by visual inspection and UV visible spectroscopy. The results of the experimental design showed that the coupled effects between surfactant type and surfactant concentration and between ultrasonication tip amplitude and ultrasonication time had the most pronounced effects on nanofluid stability. The experimental conditions providing the best stability were 0.5 wt% of Al₂O₃, CTAB, critical micelle surfactant concentration, 30% ultrasonic amplitude and 30 min of ultrasonication.     

A viscoelastic Unitary Crack-Opening strain tensor for crack width assessment in fractured concrete structures

A post-processing technique which allows computing crack width in concrete is proposed for a viscoelastic damage model. Concrete creep is modeled by means of a Kelvin–Voight cell while the damage model is that of Mazars in its local form. Due to the local damage approach, the constitutive model is regularized with respect to finite element mesh to avoid mesh dependency in the computed solution (regularization is based on fracture energy).The presented method is an extension to viscoelasticity of the approach proposed by Matallah et al. (Int. J. Numer. Anal. Methods Geomech. 34(15):1615–1633, 2010) for a purely elastic damage model. The viscoelastic Unitary Crack-Opening (UCO) strain tensor is computed accounting for evolution with time of surplus of stress related to damage; this stress is obtained from decomposition of the effective stress tensor. From UCO the normal crack width is then derived accounting for finite element characteristic length in the direction orthogonal to crack. This extension is quite natural and allows for accounting of creep impact on opening/closing of cracks in time dependent problems. A graphical interpretation of the viscoelastic UCO using Mohr’s circles is proposed and application cases together with a theoretical validation are presented to show physical consistency of computed viscoelastic UCO.     

A new two-step gradient-type method for large-scale unconstrained optimization

In this paper, we propose some improvements on a new gradient-type method for solving large-scale unconstrained optimization problems, in which we use data from two previous steps to revise the current approximate Hessian. The new method which we considered, resembles to that of Barzilai and Borwein (BB) method. The innovation features of this approach consist in using approximation of the Hessian in diagonal matrix form based on the modified weak secant equation rather than the multiple of the identity matrix in the BB method. Using this approach, we can obtain a higher order accuracy of Hessian approximation when compares to other existing BB-type method. By incorporating a simple monotone strategy, the global convergence of the new method is achieved. Practical insights into the effectiveness of the proposed method are given by numerical comparison with the BB method and its variant.     

Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and λ-interchange mechanism. The algorithm is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature.     

Adaptive tracking and asymptotic rejection of unknown but bounded disturbances in nonlinear MIMO systems

The goal of this paper is global disturbance rejection in nonlinear systems. An output feedback controller with disturbance rejection is developed for a class of nonlinear multi input-multi output (MIMO) systems. The availability of state variables and the bound of disturbances are not required to be known in advance and reference tracking will is guaranteed. By the aid of designing an adaptive observer, a robust adaptive nonlinear state feedback controller using the estimated states is proposed. For tracking problem, an adaptive pre-compensator is used. The control methodology is robust against both constant and time varying bounded disturbances, maintaining effective performance. The adaptive laws are derived based on the Lyapunov synthesis method, therefore closed-loop asymptotic stability is also guaranteed. Moreover, for chattering reduction we use a low-pass filter. Consequently, small gain theorem is adopted to prove the stability of the closed-loop system. Simulation results are employed to illustrate the effectiveness of the proposed controller.     

An imperialist competitive algorithm for virtual machine placement in cloud computing

Abstract

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user’s applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.     

Permanent magnet configuration in design of an eddy current damper

This paper discusses the design of an eddy current passive damper using different configurations of permanent magnets. Motional eddy current damping effect is used for the development of a passive damper. Eddy currents are generated in a conductor in a time-varying magnetic field. They are induced either by movement of the conductor in a static field or by changing the strength of the magnetic field, initiating motional and transformer electromotive forces, respectively. The conceived eddy current damper consists of a conductor as an outer tube, and an array of axially magnetized, ring-shaped permanent magnets (PMs), separated by iron pole pieces as a mover. The relative movement of the magnets and the conductor causes the conductor to undergo motional eddy currents. Using this concept, damping characteristics of the new damper is obtained through analytical modeling, and verified by experimental analysis. The optimum PMs’ size and configuration are also derived using analytical and finite element analysis, respectively. A damping coefficient as high as 53 kg/s is achievable with the proposed design specifications.     

Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study

Neural Processing Letters - This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase....     

Defending against Wormhole Attack in MANET Using an Artificial Immune System

MANETs are mobile networks that are spontaneously deployed over a geographically limited area without requiring any pre-existing infrastructure. Typically, nodes are both autonomous and self-organized without requiring a central administration or a fixed network infrastructure. Due to their distributed nature, MANET is vulnerable to a specific routing misbehavior, called wormhole attack. In a wormhole attack, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer hop count. If the source node follows this fake route, malicious nodes have the option of delivering the packets or dropping them. This article aims at removing these attacks. For this purpose, it investigates the use of an Artificial Immune System (AIS) to defend against wormhole attack. The proposed approach learns rapidly how to detect and bypass the wormhole nodes without affecting the overall performance of the network. The proposed approach is evaluated in comparison with other existing solutions in terms of dropped packet count, packet loss ratio, throughput, packet delivery ratio, and end-to-end delay. A simulation result shows that the proposed approach offers better performance than other schemes defending against the wormhole attack.