The Effect of a Magnetic Field on the Melting of Gallium in a Rectangular Cavity

The role of magnetic field and natural convection on the solid–liquid interface motion, flow, and heat transfer during melting of gallium on a vertical wall is reported in this paper. The classical geometry consisting of a rectangular cavity with uniform but different temperatures imposed at two opposite side walls, insulated top, and bottom walls is considered. The magnetic field is imposed in the horizontal direction. A numerical code is developed to solve for natural convection coupled to solid–liquid phase transition and magnetic effects. The corresponding streamlines and isotherms predicted by the numerical model serve to visualize the complicated flow and temperature field. The interplay between the conduction and convection modes of heat transfer stimulated by the combination of the buoyancy-driven flow and the Lorentz force on the fluid due to the magnetic field are studied. The results show that the increase of Rayleigh number promotes heat transfer by convection, while the increase of Hartmann number dampens the strength of circulating convective currents and the heat transfer is then mainly due to heat conduction. These results are applicable in general to electrically conducting fluids and we show that magnetic field is a vital external control parameter in solid–liquid interface motion.     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.     

A novel fixture for mixed mode I/II/III fracture testing of brittle materials

A novel test‐loading device was suggested in order to study the fracture behavior of brittle materials under mixed mode I/II/III loading conditions. A version of the compact tension shear specimen was used as the test configuration. Using a three‐dimensional finite element analysis, the influence of mode mixity on the stress intensity factors, the T‐stress, and 3‐D plastic zone around the crack tip was investigated. In addition, an experimental study was performed on an epoxy polymer using the proposed setup. Finally, the fracture toughness of pure epoxy was measured under several loading conditions. The numerical and experimental results manifested that the proposed setup is able to determine a full range of mixed mode I/II/III fracture properties. At the end, the fracture envelope obtained using the practical study was compared with various three‐dimensional fracture criteria. A negligible discrepancy was concluded between the practical data and the theoretical data estimated by the maximum mean principle stress criterion.     

Dynamic risk assessment of complex systems using FCM

Analysing risk of today’s complex systems is challenging due to the complex and dynamic nature of systems. The current risk analysis tools are not able to take the complex interactions among risks into account and therefore they can’t predict the behaviour of risks accurately. In an attempt to overcome this shortcoming, this paper proposes an integrated generalised decision support tool using fuzzy cognitive maps for dynamic risk assessment of complex systems. The proposed approach has the ability to prioritise risk factors and more importantly predict and analysis the influences of each individual risk factor/risk set on the other risks or on the outcomes of complex and critical systems by taking into account probability of occurrence and consequences of risks and also considering the complex dependencies between risk factors. These features could provide practitioners with realistic results in critical industries and able them to manage risks more efficiently.     

Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.     

Design of RF MEMS Based Oscillatory Neural Network for Ultra High Speed Associative Memories

Microelectromechanical systems are utilized alongside with transistor amplifiers and resistive connections for implementing of oscillatory associative memories. Phase locking is studied in such a network and all requirements of the circuit level implementation are satisfied. A very high gain trans-impedance amplifier operating in 1 GHz in addition to a novel automatic amplitude control circuit is employed to remove amplitude dynamics of the system. Requiring resonator characteristics are extracted and calculated as well. A new method for initialization of the network is proposed. Each neuron consumes 1.08 mW from a 1.8 V power supply. The convergence time of a typical network trained by Hebbian rule is less than 1.5 ns which results in an ultra high speed analog signal processing system.     

Analysis and suppression of spurious modes of the ring shape anchored RF MEMS contour mode disk resonator

One of the most encumbering issues in RF MEMS resonators is spurious modes. The problem of spurious modes becomes more critical, when the ring type resonators are used. In the ring shape anchored contour mode disk resonator, for achieving a low serial resistance, the inner radius of the disk must be increased. This causes the spurious modes to become too close to the desired mode and degrade the operation of the resonator. In this work, spurious modes of before-mentioned device are introduced and their characteristics are evaluated by exact analytical approach. Based on those analytical approaches, we introduce two techniques for spurious mode suppression. The first technique is based on exciting the desired mode by proper electrode engineering and hence is an electrical approach. The second technique is reconfiguring of the anchor from a continuous ring to crossed ring segments and locating the segments on the phase discriminating lines to increase the insertion loss for spurious modes and decrease the losses for the fundamental mode. The final harmonic analysis verifies that the proposed techniques result a resonator with a pure frequency spectrum and spurious modes excluded over a very wide frequency range.     

Prediction of heavy oil viscosity using a radial basis function neural network

Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data.