Characterization of structure and energy of TixCy cluster at early formation stage in iron matrix by molecular dynamics

The structure, energy and bonding property of TixCy clusters formed in iron matrix were studied through molecular dynamics (MD) simulation method. The initial clusters with 1D-linear, 2D-ring, and 3D-tetrahedral structures were determined and their stability was calculated. The effect of temperature on the stability of the clusters was also discussed. In addition, the dissociation path of TiC clusters in iron matrix and the corresponding energy variation were analyzed. © 2014 Elsevier B.V. All rights reserved.

Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems

In this study, we proposed an adaptive fuzzy multi-surface sliding control (AFMSSC) for trajectory tracking of 6 degrees of freedom inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). It is shown that an adaptive fuzzy logic-based function approximator can be used to estimate the system uncertainties and an iterative multi-surface sliding control design can be carried out to control flight. Using AFMSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. It is proved that the AFMSSC system guarantees asymptotic output tracking and ultimate uniform boundedness of the tracking error. Simulation results are presented to validate the analysis.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

Study of growth mechanism of TiC cluster in ferrite via molecular dynamics simulation

Abstract Atomistic simulations were used to investigate the evolution process of titanium carbide clusters to mature precipitates in ferrite. The typical kinetic of carbide cluster growth was studied in detail through analyzing the atomic interactions of a carbide cluster with scattered carbon atoms. The driving force required for cluster growth was calculated along with the atomic diffusivity in the iron matrix, exploring the change in response as two main growth steps. The growth kinetic improved the understanding of precipitate evolution at the atomic level.