Three‐dimensional computation for flow‐induced vibrations in in‐line and cross‐flow directions of a circular cylinder

We present numerical results for in‐line and cross‐flow vibrations of a circular cylinder, which is immersed in a uniform flow and is elastically supported by damper‐spring systems to compute vibrations of a rigid cylinder. In the case of a circular cylinder with a low Scruton number, it is well‐known that two types of self‐excited vibrations appear in the in‐line direction in the range of low reduced velocities. On the other hand, a cross‐flow vibration of the circular cylinder can be excited in the range of high reduced velocities. Therefore, we compute the flow‐induced vibrations of the circular cylinder in the wide range of the reduced velocities at low and high Scruton numbers and discuss about excitation mechanisms in the in‐line and cross‐flow directions. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantum superposition of localized and delocalized phases of photons

Based on a variant of 2-site Jaynes–Cummings–Hubbard model constructed using superconducting circuits, we propose a method to coherently superpose the localized and delocalized phases of microwave photons, which makes it possible to engineer the collective features of multiple photons in the quantum way using an individual two-level system. Our proposed architecture is also a promising candidate for implementing distributed quantum computation since it is capable of coupling remote qubits in separate resonators in a controllable way.     

Slope enclosures for functions given by two or more branches

In this paper, we consider the automatic computation of slope enclosures for continuous real functions given by two or more branches, such as piecewise defined nonsmooth functions. We show that a commonly used formula found in the literature does not always provide a slope enclosure for such functions. Furthermore, we prove a similar formula that always holds. AMS subject classification (2000)  65G20     

Evolutionary structural optimisation using an additive algorithm

The evolutionary structural optimisation (ESO) method has been under continuous development since 1992. Traditionally, the method was conceived from the engineering perspective that the topology and shape of structures were naturally conservative for safety reasons and therefore contained an excess of material. To move from the conservative design to a more optimum design would therefore involve the removal of material. Thus the ESO method started from a design space much bigger than the optimum and the final topology or shape emerged by a process of removal of unwanted/inefficient/lowly stresses material. The original algorithms allowed for two forms of evolution. One was there the understressed material could be removed from anywhere in the allowable design space, and with compensation for checker-boarding this produces an optimum topology under the prescribed environments. The second form only allows removal from the surface or parts of the surface (called nibbling in the ESO lexicon); this produces a Min–Max situation where the maximum surface stress is reduced to a minimum. It has been demonstrated that the ESO process produces a surface that is an iso-stress contour thus satisfying the Min–Max optimality criterion. The present paper addresses the opposite evolutionary process whereby the structure evolves from a base which is the minimum structural form required to carry the load regardless of the magnitude of the stress levels. Material is added in the proximity of high stress to ameliorate its effect and hence the final structural form emerges. Only singly connected regions are formed in the present analysis and thus the additive ESO process is the opposite of the nibbling SO, mentioned above, that produces optimum surface shapes. The paper presents a brief background to the current state of structural optimisation research. This is followed by a discussion of the strategies for the additive ESO (AESO) algorithm and two examples are presented.     

蒙特卡罗方法计算定积分的进一步讨论

介绍了蒙特卡罗方法计算定积分的原理和方法.给出了用蒙特卡罗方法计算定积分的一个简单证明,从而揭示了蒙特卡罗方法和定积分定义间的内在联系.针对蒙特卡罗方法收敛慢的特点,提出将蒙特卡罗方法与相应的数值计算方法相结合,提高计算结果的精度.此外,将蒙特卡罗方法推广到反常积分上去.     

A modular attachment mechanism for software network evolution

A modular attachment mechanism of software network evolution is presented in this paper. Compared with the previous models, our treatment of object-oriented software system as a network of modularity is inherently more realistic. To acquire incoming and outgoing links in directed networks when new nodes attach to the existing network, a new definition of asymmetric probabilities is given. Based on this, modular attachment instead of single node attachment in the previous models is then adopted. The proposed mechanism is demonstrated to be able to generate networks with features of power-law, small-world, and modularity, which represents more realistic properties of actual software networks. This work therefore contributes to a more accurate understanding of the evolutionary mechanism of software systems. What is more, explorations of the effects of various software development principles on the structure of software systems have been carried out, which are expected to be beneficial to the software engineering practices.     

符号和数值混合计算

符号计算和数值计算是两种不同的解决科学和技术发展中问题的计算方法.符号计算可以得到问题精确的完备解,但是计算量大且表达形式往往十分庞大;数值计算可以快速地处理很多实际应用中的问题,但是一般只能得到近似的局部解.特别地,数值计算处理病态问题时,收敛往往较慢且容易出错.着重介绍了符号计算和数值计算之间的密切联系,以及如何运用这两大领域的最新研究成果,探索和开发符号和数值混合计算算法和软件,使之兼备符号计算的完备化和数值计算的高效性.     

A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA), for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. FastPGA utilizes a new ranking strategy that utilizes more information about Pareto dominance among solutions and niching relations. New genetic operators are employed to enhance the proposed algorithm’s performance in terms of convergence behavior and computational effort as rapid convergence is of utmost concern and highly desired when solving expensive multiobjective optimization problems (MOPs). Computational results for a number of test problems indicate that FastPGA is a promising approach. FastPGA yields similar performance to that of the improved nondominated sorting genetic algorithm (NSGA-II), a widely-accepted benchmark in the MOEA research community. However, FastPGA outperforms NSGA-II when only a small number of solution evaluations are permitted, as would be the case when solving expensive MOPs.     

Quantum entanglement in the NMR implementation of the Deutsch-Jozsa algorithm

A scheme to execute an n-bit Deutsch-Jozsa (DJ) algorithm using n qubits has been implemented for up to three qubits on an NMR quantum computer. For the one- and the two-bit Deutsch problem, the qubits do not get entangled, and the NMR implementation is achieved without using spin-spin interactions. It is for the three-bit case, that the manipulation of entangled states becomes essential. The interactions through scalar J-couplings in NMR spin systems have been exploited to implement entangling transformations required for the three bit DJ algorithm.