Prediction of Particles-Air Movement in Silo during Filling Operation

Awareness of dust explosion hazards during silo filling operation is important for safety measures. Thus, information on particles-air flow field is required to assess the likelihood of the hazard. Flow field visualization via experimental investigation associated with difficulties and risks. Hence, in the present study, a modeling formulation using commercial computational fluid dynamics (CFD) code, FLUENT software was employed to predict an insight of flow field distribution, in terms of mean and root mean square (RMS) velocities vectors in cylindrical silo during axial filling. According to the simulation results, predicted flow field has a great influence to the silo height and distance to the silo wall due to gravitational force and movement of fugitive dust and re-circulation of air. The results showed that the predicted data were in very good agreement with experimental data obtained from the literature. The maximum error was around 10%. The study has gone some way towards enhancing our understanding of the particles-air behavior inside industrial equipments during filling operation.

基于扇区负荷约束的管制移交间隔多目标优化模型

繁忙扇区的容量随着交通复杂性的改变而动态变化,现有的基于确定容量的管制移交间隔管理策略不再适用,且动态容量下频繁改变移交间隔也不利于空中管制指挥。本文首先通过将交通复杂性转化为管制工作负荷参数,解决移交间隔改变导致的容量动态变化问题,建立基于扇区负荷的容量流量匹配模型。其次设计管制移交间隔策略稳定性表征函数,提出以策略稳定性和延误成本为目标的管制移交策略优化模型和智能算法。最后基于昆明机场进近扇区实际数据进行仿真验证,证明本方法可以获得一组帕累托最优解,能够为流量管理部门发布管制移交策略以供决策支持。

利用粮堆微气流熏蒸高大立筒仓储粮害虫的试验

根据粮堆内部微气流的运行方向，在高大立筒仓粮面或仓底施药，可以得到很好的杀虫效果，具有经济、安全、简单、快速的特点。

恶劣天气下基于MIT的扇区流量动态控制模型

针对恶劣天气下频繁发生的空中交通拥堵与航班延误问题,提出了一种基于尾随间隔(MIT)的扇区流量动态控制模型。提出以恶劣天气下扇区动态容量作为主要约束条件,航班最小累积延误成本为目标函数,建立了基于MIT扇区流量控制模型,并采用了遗传算法对该模型进行求解。结合三亚管制区内04号扇区某日16:00至17:00的流量数据对模型进行验证,并与无MIT策略情况进行了比较。结果表明:利用该模型生成的优化策略避免了扇区拥堵,实现了扇区流量与容量动态平衡,使扇区空域资源利用率提高了25%,同时使航班累积延误成本也减少了23%,验证了模型的有效性与实用性。