Aerodynamic forces and power requirements in forward flight in a bumblebee （Bombus terrestris） were studied using the method of computational fluid dynamics. Actual wing kinematic data of free flight were used in th...Aerodynamic forces and power requirements in forward flight in a bumblebee （Bombus terrestris） were studied using the method of computational fluid dynamics. Actual wing kinematic data of free flight were used in the study （the speed ranges from 0 m/s to 4.5 m/s; advance ratio ranges from 0-0.66）. The bumblebee employs the delayed stall mechanism and the fast pitching-up rotation mechanism to produce vertical force and thrust. The leading-edge vortex does not shed in the translatory phase of the half-strokes and is much more concentrated than that of the fruit fly in a previous study. At hovering and low-speed flight, the vertical force is produced by both the half-strokes and is contributed by wing lift; at medium and high speeds, the vertical force is mainly produced during the downstroke and is contributed by both wing lift and wing drag. At all speeds the thrust is mainly produced in the upstroke and is contributed by wing drag. The power requirement at low to medium speeds is not very different from that of hovering and is relatively large at the highest speed （advance ratio 0.66）, i.e. the power curve is Jshaped. Except at the highest flight speed, storing energy elastically can save power up to 20%-30%. At the highest speed, because of the large increase of aerodynamic torque and the slight decrease of inertial torque （due to the smaller stroke amplitude and stroke frequency used）, the power requirement is dominated by aerodynamic power and the effect of elastic storage of energy on power requirement is limited.展开更多
光伏发电阵列板在局部遮阴下会产生多个功率峰值,传统算法难以准确快速追踪光伏最大功率点(maximum power point,MPP),该文提出一种基于莱维飞行灰狼算法(Levy grey wolf optimization,LGWO)与电导增量法(incremental conductance,INC)...光伏发电阵列板在局部遮阴下会产生多个功率峰值,传统算法难以准确快速追踪光伏最大功率点(maximum power point,MPP),该文提出一种基于莱维飞行灰狼算法(Levy grey wolf optimization,LGWO)与电导增量法(incremental conductance,INC)结合的复合算法追寻MPP,莱维飞行帮助灰狼算法跳出局部最优,搜寻MPP附近时,切换电导增量算法减少系统振荡,在静态与动态局部遮阴下通过Simulink进行光伏并网仿真验证。研究结果显示,所提复合算法收敛效果快速精确,并且符合并网谐波(total harmonic distortion,THD)含量要求,可保证系统的稳定运行。展开更多
针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提...针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。展开更多
The choices of insect wing kinematic programs is not well understood,particularly the mechanism by which an insect selects a distortion to achieve flight control.A methodology to evaluate prospective kinematic control...The choices of insect wing kinematic programs is not well understood,particularly the mechanism by which an insect selects a distortion to achieve flight control.A methodology to evaluate prospective kinematic control inputs is presented based on the reachable states when control actuation was constrained to a unit of power.The method implements a computationally-derived reduced order model of the insect’s flight dynamics combined with calculation of power requirement.Four kinematic inputs are evaluated based on this criterion for a Drosophila size insect in forward flight.Stroke bias is shown to be the dominant control input using this power normalized evaluation measure.展开更多
基金The project supported by the National Natural Science Foundation of China(10232010)the National Aeronautic Science fund of China(03A51049)
文摘Aerodynamic forces and power requirements in forward flight in a bumblebee （Bombus terrestris） were studied using the method of computational fluid dynamics. Actual wing kinematic data of free flight were used in the study （the speed ranges from 0 m/s to 4.5 m/s; advance ratio ranges from 0-0.66）. The bumblebee employs the delayed stall mechanism and the fast pitching-up rotation mechanism to produce vertical force and thrust. The leading-edge vortex does not shed in the translatory phase of the half-strokes and is much more concentrated than that of the fruit fly in a previous study. At hovering and low-speed flight, the vertical force is produced by both the half-strokes and is contributed by wing lift; at medium and high speeds, the vertical force is mainly produced during the downstroke and is contributed by both wing lift and wing drag. At all speeds the thrust is mainly produced in the upstroke and is contributed by wing drag. The power requirement at low to medium speeds is not very different from that of hovering and is relatively large at the highest speed （advance ratio 0.66）, i.e. the power curve is Jshaped. Except at the highest flight speed, storing energy elastically can save power up to 20%-30%. At the highest speed, because of the large increase of aerodynamic torque and the slight decrease of inertial torque （due to the smaller stroke amplitude and stroke frequency used）, the power requirement is dominated by aerodynamic power and the effect of elastic storage of energy on power requirement is limited.
文摘光伏发电阵列板在局部遮阴下会产生多个功率峰值,传统算法难以准确快速追踪光伏最大功率点(maximum power point,MPP),该文提出一种基于莱维飞行灰狼算法(Levy grey wolf optimization,LGWO)与电导增量法(incremental conductance,INC)结合的复合算法追寻MPP,莱维飞行帮助灰狼算法跳出局部最优,搜寻MPP附近时,切换电导增量算法减少系统振荡,在静态与动态局部遮阴下通过Simulink进行光伏并网仿真验证。研究结果显示,所提复合算法收敛效果快速精确,并且符合并网谐波(total harmonic distortion,THD)含量要求,可保证系统的稳定运行。
文摘针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
基金supported by the Micro Autonomous Systems and Technology(MAST)CTA
文摘The choices of insect wing kinematic programs is not well understood,particularly the mechanism by which an insect selects a distortion to achieve flight control.A methodology to evaluate prospective kinematic control inputs is presented based on the reachable states when control actuation was constrained to a unit of power.The method implements a computationally-derived reduced order model of the insect’s flight dynamics combined with calculation of power requirement.Four kinematic inputs are evaluated based on this criterion for a Drosophila size insect in forward flight.Stroke bias is shown to be the dominant control input using this power normalized evaluation measure.
