摘要
提出一种新的管网阻力系数辨识方法,利用管网中少量节点处的压力或流量测量值对所有管段内壁粗糙度进行有效辨识,达到获得管段阻力系数的目的.该方法的辨识模型不仅能灵活利用工程手册所推荐的管道粗糙度等先导信息,还可综合利用多种工况下的辨识值,获得更高精度的辨识结果.基于这种辨识模型,提出一种结合遗传算法(GA)和有效集法(AS)的混合算法求解最优化问题.混合算法改善了局部搜索效率,同时具备较好的全局寻优能力.通过对2个案例管网参数的辨识计算,表明新方法可获得精度较高的辨识结果,并且对较大的管网也能得到稳定的计算结果.
An approach is proposed for calibrating hydraulic network models.The proposed procedure could reconcile results of all scenarios under various operation conditions and utilize prior information selectively.To achieve more accurate roughness with a faster processing,a hybridoptimization technique was developed to exploit advantages of genetic algorithm and active set method.The algorithm was applied to two sample networks,and resulting calibrated model was compared to counterpart in previous literature.The proposed approach was shown stable and estimated parameters were more accurate.
出处
《计算物理》
CSCD
北大核心
2013年第3期422-432,共11页
Chinese Journal of Computational Physics
基金
高等学校博士学科点专项科研基金(20090072120031)
海洋能勘查与评价标准研究与制定(GHME2010ZC07)资助项目
关键词
辨识方法
阻力系数
最优化方法
供热管网
供水管网
calibration
optimization
pipe network
district heat supply
water distribution system
