Taking pure Mg, Mg-Al and Mg-Zn as prototypes, the effects of strain on the stacking fault energies (SFEs), dislocation core structure and Peierls stress were systematically investigated by means of density-functional theory and the semidiscrete variational Peierls-Nabarro model. Our results suggest that volumetric strain may significantly influence the values of SFEs of both pure Mg and its alloys, which will eventually modify the dislocation core structure, Peierls stress and preferred slip system, in agreement with recent experimental results. The so-called "strain factor" that was previously proposed for the solute strengthening could be justified as a major contribution to the strain effect on SFEs. Based on multivariate regression analysis, we proposed universal exponential relationships between the dislocation core structure, the Peierls stress and the stable or unstable SFEs. Electronic structure calculations suggest that the variations of these critical parameters controlling strength and ductility under strain can be attributed to the strain-induced electronic polarization and redistribution of valence charge density at hollow sites. These findings provide a fundamental basis for tuning the strain effect to design novel Mg alloys with both high strength and ductility.
To improve the endurance performance of long-endurance Unmanned Aerial Vehicles (UAVs), a smart morphing method to adjust the UAV and flight mode continuously during flight is proposed. Using this method as a starting point, a smart morphing long-endurance UAV design is conducted and the resulting improvement in the endurance performance studied. Firstly, the initial overall design of the smart morphing long-endurance UAV is carried out, then the morphing form is designed and various control parameters are selected. Secondly, based on multi-agent theory, an architecture for the smart morphing control system is built and the workflow of the smart morphing control system is planned. The morphing decision method is designed in detail based on the particle swarm optimisation algorithm. Finally, a simulation of the smart morphing approach in the climb and cruise stages is carried out to quantitatively verify the improvement in the endurance performance. The simulation results show that the smart morphing method can improve the cruise time by 4.1% with the same fuel consumption.
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