SUMMARYThis paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are proposed, including binary and float coding, integer and float coding. Surrogate function is applied to unify the constraints into single one; moreover surrogate reproduction is developed to select good individuals to mating pool on the basis of constraint and fitness values, which completely considers the character of constrained optimization. This paper proposes a new strategy of creating next population by competing between parent and offspring population based on constraint and fitness values; so that lifetime of excellent gene is prolonged. Because the initial population is created randomly and three operators of GA are also indeterminable, it is necessary to check whether the structural topology is desirable. An improved restart operator is proposed to introduce new gene and explore new space, so that the reliability of GA is enhanced. Selected examples are solved; the improved numerical results demonstrate that the enhanced GA scheme is feasible and effective.

Aeroelastic energy harvesting can be used to power wireless sensors embedded into bridges, ducts, high-altitude buildings, etc. One challenging issue is that the wind speed in some application environments is low, which leads to an inefficiency of aeroelastic energy harvesters. This paper presents a novel nonlinear magnetic-coupled flutter-based aeroelastic energy harvester to enhance energy harvesting at low wind speeds.The presented harvester mainly consists of a piezoelectric beam, a two-dimensional airfoil, two tip magnets and two external magnets. The function of magnets is to reduce the cut-in wind speed of the flutter-based aeroelastic energy harvester and enhance energy harvesting performance at low wind speeds. A theoretical model is deduced based on Hamilton's principle, theory of aeroelasticity, Kirchhoff's laws and experimental measurements, etc. A good agreement is found between numerical simulation and experimental results, which verifies the accuracy of the theoretical model. Stability analysis is provided to determine the characteristics of the presented harvester. More importantly, it is numerically and experimentally verified that the presented harvester has a much lower cut-in wind speed (about 1.0 m/s) and has a better energy harvesting performance at a low wind speed range from 1.0 m/s to 2.9 m/s, when compared with traditional flutter-based aeroelastic energy harvesters.

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Zeolite coated fibers with compact and full deposits have been fabricated by electrophoretic deposition of nanozeolites onto carbon fibers, whereafter hollow zeolite fibers have been obtained by calcination to remove the substrates.

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The normal grain growth in volume-conserved two-phase nanocrystalline materials is studied using a modified Potts model, in which the grain boundary migration is driven by the interfacial energy between two phases and the grain boundary energy inside each phase. Monte Carlo simulation results show that the grain growth of one phase is constrained by the presence of the other phase. The power-law grain growth kinetics with an almost temperature-independent exponent of 0.16±0.01 (0.5 in a pure single-phase system) is predicted for two immiscible phases, which is in agreement with experimental observations.

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