物理学报
物理學報
물이학보
2013年
11期
116-126
,共11页
高忠科%胡沥丹%周婷婷%金宁德?
高忠科%鬍瀝丹%週婷婷%金寧德?
고충과%호력단%주정정%금저덕?
两相流%复杂网络%有限穿越可视图%网络异速生长指数
兩相流%複雜網絡%有限穿越可視圖%網絡異速生長指數
량상류%복잡망락%유한천월가시도%망락이속생장지수
two-phase flow%complex network%limited penetrable visibility graph%allometric scaling exponent
针对小管径两相流流动特性,全新优化设计弧形对壁式电导传感器.通过动态实验在获取传感器测量信号的基础上,采用有限穿越可视图理论构建对应于不同流型的两相流复杂网络.通过分析发现,有限穿越可视图网络异速生长指数和网络平均度值的联合分布可实现对小管径两相流的流型辨识;有限穿越可视图度分布曲线峰值可有效刻画与泡径大小分布相关的流动物理结构细节特征;网络平均度值可表征流动结构的宏观特性;网络异速生长指数对流体动力学复杂性十分敏感,可揭示不同流型演化过程中的细节演化动力学特性.两相流测量信号的有限穿越可视图分析为揭示两相流流型的形成及演化动力学机理提供了新途径.
針對小管徑兩相流流動特性,全新優化設計弧形對壁式電導傳感器.通過動態實驗在穫取傳感器測量信號的基礎上,採用有限穿越可視圖理論構建對應于不同流型的兩相流複雜網絡.通過分析髮現,有限穿越可視圖網絡異速生長指數和網絡平均度值的聯閤分佈可實現對小管徑兩相流的流型辨識;有限穿越可視圖度分佈麯線峰值可有效刻畫與泡徑大小分佈相關的流動物理結構細節特徵;網絡平均度值可錶徵流動結構的宏觀特性;網絡異速生長指數對流體動力學複雜性十分敏感,可揭示不同流型縯化過程中的細節縯化動力學特性.兩相流測量信號的有限穿越可視圖分析為揭示兩相流流型的形成及縯化動力學機理提供瞭新途徑.
침대소관경량상류류동특성,전신우화설계호형대벽식전도전감기.통과동태실험재획취전감기측량신호적기출상,채용유한천월가시도이론구건대응우불동류형적량상류복잡망락.통과분석발현,유한천월가시도망락이속생장지수화망락평균도치적연합분포가실현대소관경량상류적류형변식;유한천월가시도도분포곡선봉치가유효각화여포경대소분포상관적류동물리결구세절특정;망락평균도치가표정류동결구적굉관특성;망락이속생장지수대류체동역학복잡성십분민감,가게시불동류형연화과정중적세절연화동역학특성.량상류측량신호적유한천월가시도분석위게시량상류류형적형성급연화동역학궤리제공료신도경.
We optimize and design a new half-ring conductance sensor for measuring two-phase flow in a small diameter pipe. Based on the experimental signals measured from the designed sensor, we using the limited penetrable visibility graph we proposed construct complex networks for different flow patterns. Through analyzing the constructed networks, we find that the joint distribution of the allometric scaling exponent and the average degree of the network allows distinguishing different gas-liquid flow patterns in a small diameter pipe. The curve peak of the degree distribution allows uncovering the detailed features of the flow structure associated with the size of gas bubbles, the average degree of the network can reflect the macroscopic property of the flow behavior, The allometric scaling exponent is very sensitive to the complexity of fluid dynamics and allows characterizing the dynamic behaviors in the evolution of different flow patterns. In this regard, limited penetrable visibility graph analysis of fluid signals can provide a new perspective and a novel tool for uncovering the dynamical mechanisms governing the formation and evolution of different flow patterns.