The maximum performance of a supersonic inlet will be achieved when operating as close as possible to its buzz boundary. In order to maintain high performance without crossing the buzz boundary, an active buzz margin predictor and controller is necessary. The goal of a control system is to acquire inlet buzz margin and maintain it as the designated value and predict inlet buzz before it occurs and then take some measures to buzz mitigation or inlet restart. The inlet buzz boundary and the margin of ducted rockets were first discussed and analysed. Then the dynamic mathematical model of a gas flow control system was established. Lastly, the inlet buzz margin controller of a ducted rocket was designed. Simulation results show that there exist antiregulation characteristics for gas flow control for ducted rockets, and it leads to the particularity of inlet buzz margin control for ducted rockets in contrast to liquid ramjet. The designed inlet buzz margin has to be bigger to avoid the appearing of inlet buzz because of anti-regulation characteristics; otherwise, the inlet buzz phenomenon probably appears. If the inlet buzz of the ducted rocket appears, the command of decreasing fuel flow could not be given immediately; otherwise, the inlet buzz will aggravate.
Assembly precision optimization is an important means to ensure product accuracy, including two aspects: on the one hand, the relevant deviations of out-of-tolerance key characteristics are reduced to the design tolerance range; on the other hand, the deviation fluctuation range of key characteristics with a large process capability index (Cp) can be extended to achieve the balance between accuracy, process capacity, and production cost. By virtue of the accumulated experience, a fast solution can be provided for the out-of-tolerance problem. Therefore, a semantic-based assembly precision optimization method considering process capacity is proposed in this paper. By constructing an ontology model between Cp and optimization strategy, a reasonable assembly precision optimization strategy can be pushed based on product accuracy analysis results. Firstly, an assembly precision optimization semantic model is established by association between analysis results, out-of-tolerance key characteristics, assembly process, and tolerance adjustment defined with Web Ontology Language (OWL) assertions. Furtherly, according to different Cp corresponding to different assembly success rates, Semantics Web Rule Language (SWRL) rules based on Cp are constructed to the push optimization strategy. Finally, the effectiveness of the model is illustrated by an aircraft inner flap.
With the rapid increase of urbanization level in China, the accidents related to urban water supply pipe network occurred frequently due to the impacts of pipeline aging, environmental change and human factors, thus the health evaluation of urban water supply network has become one of the hot topics in recent years. Related researches mostly refer to qualitative and static analysis, some quantitative studies consider the influence of only one aspect on the status of water supply pipe network such as hydraulic, water quality and static structure, and cannot objectively reflect the health status of pipe network due to neglecting or simplifying the influence of internal hydraulic condition on the health of the pipe network. Comprehensively considering the influencing factors and the hydraulic mechanism during the operation process of pipe network, this study constructs a health evaluation index system for the water supply pipe network, and introduces the triangular fuzzy number theory into the traditional Bayesian water supply network health status evaluation model taking the uncertainty of model structure and data into account. Through applying the method to Yunhe county of Zhejiang Province China, some suggestions for the optimization and transformation of the regional water supply network are proposed, which can provide a basis for ensuring the safety of regional water supply and scientifically determining the renewal sequence of the pipe network.
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