CFD and artificial neural network modeling of two-phase flow pressure drop

A large number of experiments in a 2 cm diameter and 6 m length tube were carried out in order to study the two-phase flow regimes and pressure drops in it. The two-phase flow in the experimental tube was modeled using commercial CFD code, Fluent 6.2. An Artificial Neural Network (ANN) with three inputs including gas and liquid velocities and tube slope was designed and trained to predict average pressure drop across the tube. The comparison between CFD and ANN predictions of pressure drops with experimental measurements shows that the CFD results are more accurate than the ANN evaluations for new conditions.     

Real-time performance models for photovoltaic water pumping systems

This purpose of this paper is to develop and validate a model to accurately predict the cell temperature of a photovoltaic (PV) module that adapts to various mounting configurations, mounting locations, and climates while only requiring readily available data from the module manufacturer. Results from this model are also compared with results from published cell temperature models. The models were used to predict real-time performance from a PV water pumping systems in the desert of Medenine, south of Tunisia using 60-min intervals of measured performance data during one complete year. Statistical analysis of the predicted results and measured data highlights possible sources of errors and the limitations and/or adequacy of existing models, to describe the temperature and efficiency of PV-cells and consequently, the accuracy of performance of PV water pumping systems’ prediction models.     

Rubber's dissipated energy quantification used in vibratory insulation and protection systems

In vibratory protection and insulation systems, the major problem consist to choose suitable passive elements (spring, damper, others), which are inserted between the resonator and the exciter which have the role of preventing or reducing the transmission of dynamic forces. This work consists of characterizing a synthetic rubber (SR) sample of hollow circular shape (design requirement) by determining the coefficient of energy dissipation at an average ambient temperature of 20°C with a humidity of 25%. The mechanical load and discharge tests make it possible to draw hysteresis curves through which the dissipation coefficient will be determined, the load values is between 500 and 1000 N, with 30, 60, and 90 mm/min loading speeds and a number of cycles 2, 3, and 5, these values have been chosen so as not to cause the effect of cyclic hardening and softening and also to take into consideration, that the vibration limits movement at 3 cycles, which makes this test different from other tests such as fatigue. The processing of different curves, allows to determine energy dissipation coefficient of rubber specimen and also to examine its variation as a function of load, loading speed, and number of cycles; it is possible to determine other characteristics from this coefficient, such as, damping ratio, dynamic amplification factor, and so on, necessary to study efficiency of protection systems, design, and manufacture, based on the curve of transmissibility of dynamic forces to evaluate performance rubber conditions use.     

Formulation and characterization of hybrid polymeric/ZnO nanocomposite coatings with remarkable anti-corrosion and hydrophobic characteristics

In this study, ZnO nanoparticles ranging from 1 to 8% (w/w) were introduced into a hybrid acrylic-silicone polymeric matrix to achieve nanocomposite coating systems with remarkable anti-corrosion and hydrophobic characteristics. The chemical structure of the developed hybrid polymeric matrix was investigated using Fourier transform infrared spectroscopy, whereas the morphology and wettability of the developed surfaces were determined using field emission scanning electron microscopy and contact angle measurements (CA), respectively. Furthermore, the anti-corrosion performance and the barrier properties over 60 days of exposure to a 3.5% NaCl solution were studied by electrochemical impedance spectroscopy. All the obtained results revealed that the presence of ZnO nanoparticles significantly influences the performance of the acrylic-silicone polymeric matrix. A coating system with 3% wt ZnO nanoparticles demonstrated the most pronounced anti-corrosion properties and also achieved the highest CA, with a value of 95.6°.     

Habituation effects in computer security warning

Security warning is a form of computer dialog communication used to inform the users on the risks of allowing random applications to run on a computer system. Accordingly, it is specifically designed to impersonate a legitimate security alerting function (e.g., notify, warn, and advice) to a user about the consequence effect of an action. However, most of the computer users tend to ignore those security warnings conveying the same message over and over again. This eventually leads to habituation. Considering the fact that there is a significant lack of focus paid to address this issue, the main objective of this article is to describe and summarize the related studies on users’ habituation to the security warnings. This article presents a systematic literature review to explore the current key issues, challenges, and the possible solutions related to habituation effects in security warnings. It is expected that this article could contribute to a more complete understanding of the habituation effects in security warnings and eventually bring benefits to the research communities or general publics.     

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15–70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

On the Convergence of a Greedy Rank-One Update Algorithm for a Class of Linear Systems

In this paper we study the convergence of the well-known Greedy Rank-One Update Algorithm. It is used to construct the rank-one series solution for full-rank linear systems. The existence of the rank one approximations is also not new, but surprisingly the focus there has been more on the applications side more that in the convergence analysis. Our main contribution is to prove the convergence of the algorithm and also we study the required rank one approximation in each step. We also give some numerical examples and describe its relationship with the Finite Element Method for High-Dimensional Partial Differential Equations based on the tensorial product of one-dimensional bases. We illustrate this situation taking as a model problem the multidimensional Poisson equation with homogeneous Dirichlet boundary condition.     

Obtaining a linear combination of the principal components of a matrix on quantum computers

Principal component analysis is a multivariate statistical method frequently used in science and engineering to reduce the dimension of a problem or extract the most significant features from a dataset. In this paper, using a similar notion to the quantum counting, we show how to apply the amplitude amplification together with the phase estimation algorithm to an operator in order to procure the eigenvectors of the operator associated to the eigenvalues defined in the range \(\left[ a, b\right] \), where a and b are real and \(0 \le a \le b \le 1\). This makes possible to obtain a combination of the eigenvectors associated with the largest eigenvalues and so can be used to do principal component analysis on quantum computers.