Optimizing ductility and fracture of amorphous metal thin films on polyimide using multilayers

Aluminum–manganese (Al–Mn) thin films with manganese concentration up to 20.5 at.% were deposited on polyimide (PI) substrates. A variety of phases, including supersaturated fcc (5.2 at.% Mn), duplex fcc and amorphous (11.5 at.% Mn), and completely amorphous phase (20.5 at.% Mn) were obtained by adjusting alloying concentration in the film. Tensile deformation and subsequent fracture of strained Al–Mn films on PI were investigated experimentally and by finite element simulations. Compared with crystalline and dual phase counterparts, amorphous thin film exhibits the highest fracture stress and fracture toughness, but limited elongation. Based on the fracture mechanism model, a multilayer scheme was adopted to optimize the ductility and the fracture properties of the amorphous film/PI system. It was found that by sandwiching the amorphous film (20.5 at.% Mn) between two ductile Cu layers, the elongation can be improved by more than ten times, and the interfacial fracture toughness by more than twenty times. This design provides important guidelines to obtain optimized mechanical properties of future flexible electronics devices.     

Non-clairvoyant online scheduling of synchronized jobs on virtual clusters

Although virtualization technology is recently applied to next-generation distributed high-performance computing systems, theoretical aspects of scheduling jobs on these virtualized environments are not sufficiently studied, especially in online and non-clairvoyant cases. Virtualization of computing resources results in interference and virtualization overheads that negatively impact the load balancing objectives on commonly used cluster of multi-core physical machines. We present a technique for non-clairvoyant online scheduling of globally synchronized jobs, each of which spawns tasks to execute compute-intensive works. Our technique considers both load balancing of physical cores and per job synchronization cost minimization. We show that in the presence of arbitrary virtualization overheads, interference effects and synchronization cost, the problem can be reduced to an online unrelated parallel machine scheduling, which is solved using routing of virtual circuits. We present a new opportunity cost model to reduce the problem to the routing of virtual circuits and prove the effectiveness of our scheduling technique using mathematical analysis and simulative experiments.     

Modeling,analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches,based on finite element results

Recently, twist extrusion has found extensive applications as a novel method of severe plastic deformation for grain refining of materials. In this paper, two prominent predictive models, response surface method and artificial neural network (ANN) are employed together with results of finite element simulation to model twist extrusion process. Twist angle, friction factor and ram speed are selected as input variables and imposed effective plastic strain, strain homogeneity and maximum punch force are considered as output parameters. Comparison between results shows that ANN outperforms response surface method in modeling twist extrusion process. In addition, statistical analysis of response surface shows that twist extrusion and friction factor have the most and ram speed has the least effect on output parameters at room temperature. Also, optimization of twist extrusion process was carried out by a combination of neural network model and multi-objective meta-heuristic optimization algorithms. For this reason, three prominent multi-objective algorithms, non-dominated sorting genetic algorithm, strength pareto evolutionary algorithm and multi-objective particle swarm optimization (MOPSO) were utilized. Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points.     

Exploring Energy Efficient Architectures for RLWE Lattice-Based Cryptography

Journal of Signal Processing Systems - Quantum computers are imminent threat to secure signal processing because they can break the contemporary public-key cryptography schemes in polynomial time....     

Numerical prediction of elastic properties for carbon nanotubes reinforced composites using a multi-scale method

The main aim of this research was to investigate longitudinal elastic and effective modulus of composites reinforced with zigzag and armchair single-walled (CNT) and multi-walled carbon nanotubes (MWCNT) with different volume fractions and aspect ratios via finite element simulation. A three-phased volume element was adopted for the modeling of nanocomposite behavior and nonlinear spring elements were used to model interphase part joints and the effective force between nanotubes and resin were determined based on Lennard-Jones potential. After the evaluation and validation of the model, elastic modulus and Poisson’s ratio of composites reinforced with zigzag and armchair CNTs with different volume fractions and aspect ratios were extracted. It was found that by increasing volume fraction and aspect ratio, elastic modulus of representative volume element of composite was increased and its Poisson’s ratio was decreased. At similar aspect ratio and volume fraction, the elastic modulus of composites reinforced with armchair CNTs and Poisson’s ratio of those reinforced with zigzag CNTs were higher. Also, results showed that elastic modulus of composite was independent from elastic modulus of interphase.

     

Study of the effect of electric field and electroneutrality on transport of biomolecules in microreactors

In this article, the effect of electrophoresis on the transport of a sample (like biomolecules) in active microreactors is numerically investigated. Navier–Stokes equations are solved along with the equations of electrostatics, species mass transport in the buffer, and chemical reaction kinetics at reactive surfaces. Unlike previous studies, in which the effect of the charge of the sample molecules on the electric field has been neglected (i.e., the assumption of electroneutrality), here the space charge density is assumed to be nonzero and a function of biomolecule concentration. As a result, the governing equations become fully coupled. The validity of the assumption is examined against experimental results. Then, the effect of electroneutrality on the efficiency of the microreactor device is analyzed for the parallel plate open channel geometry, commonly used in biomolecule separation. It is shown that the electroneutrality assumption can drastically influence the final adsorbed concentration depending on the device configuration. Average adsorbed surface concentration and capture efficiency are compared as measures of the performance of the device for a wide range of physiochemical parameters. The sensitivity of the simulation with respect to the ionic concentration of the buffer is investigated. It is also discussed how the electric field and nonzero space charge density alter the bulk concentration profile and the velocity field inside the microreactor.     

Martensitic Transformation Behavior of B‐Bearing Steel During Isothermal Deformation

The purpose of the present research is to study the martensitic transformation in 22MnB5 steel under thermomechanical conditions by means of dilatation data. To reach this aim, the effects of deformation temperature and strain rate on the martensitic dilatation as well as martensite start temperature (M_s) were investigated. Thermomechanical treatments were performed in a deformation dilatometer including the isothermal deformation of samples in the temperature range of 550–900°C up to the final strain of 0.5 in three strain rates of 0.1, 1, and 10 s^?1. Finally, deformation temperatures were divided into two regimes of lower and higher than 800°C. In the former, strain‐induced phase transformations, while in the latter, occurrence of dynamic recovery against mechanical stabilization of austenite influenced martensitic transformation.     

Scalable anomaly detection in large homogeneous populations

Anomaly detection in large populations is a challenging but highly relevant problem. It is essentially a multi-hypothesis problem, with a hypothesis for every division of the systems into normal and anomalous systems. The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problem of practical interest. In this paper we take an optimization approach to this multi-hypothesis problem. It is first shown to be equivalent to a non-convex combinatorial optimization problem and then is relaxed to a convex optimization problem that can be solved distributively on the systems and that stays computationally tractable as the number of systems increase. An interesting property of the proposed method is that it can under certain conditions be shown to give exactly the same result as the combinatorial multi-hypothesis problem and the relaxation is hence tight.     

Erratum to: Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

Water Resources Management -