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基于阻力规律视角的水流能态区辨识OA北大核心CSTPCD

A discriminative method to flow regimes based on the flow resistance law

中文摘要英文摘要

高、低水流能态区形成条件不同,其识别对于预测动床阻力具有重要意义.基于量纲一阻力方程推导了一个新的高、低水流能态区判别函数,包含悬浮数和水流作用数2 个量纲一参数,提出了能态转变角和能态转移路径的概念,从流动阻力角度重新审视了水流能态区分问题.通过国内外4 176 组水槽和野外实测数据对该函数进行率定,确定了高、低能态区的临界方程.研究结果表明:该方法低能态区预测精度约为 91%,高能态区约为 85%,综合精度约为89%;在细沙河流中,随着水温的降低,更易发生水流低能态向高能态的转变.本文建立的判别函数不仅可高精度识别水流能态区,还可分析水温对能态区过渡的影响.

Upper-and lower-flow regimes are formed under different conditions.To identify them is important for predicting mobile bed resistance.Based on the dimensionless resistance equation,a new discriminant function to flow regimes is deduced,which contains two dimensionless parameters,namely,the suspension number and the flow action number.The concepts of the flow regime transition angle and the flow regime transition path are proposed.The problem of the discriminant flow regime is revisited from the perspective of flow resistance.The discriminant function is calibrated by 4 176 sets of flume and field measurement data at home and abroad,and the critical equation between the upper-and lower-flow regimes is determined.The results show that the prediction accuracy of the proposed critical equation is approximately 91%in the lower regime and 85%in the upper regime,and the comprehensive accuracy is approximately 89%.In fine-sand rivers,when the water temperature decreases,the transition from the lower-flow regime to the upper-flow regime is more likely to occur.The discriminant function proposed in this study can not only identify the flow regime with high accuracy but also analyze the effect of water temperature on the flow regime transition.

刘军政;张金良;黄哲;徐海珏;白玉川

天津大学河流海岸工程泥沙研究所, 天津 300072黄河勘测规划设计研究院有限公司, 河南 郑州 450003

水利科学

动床阻力水流能态判别函数悬浮数水流作用数水温

mobile bed resistanceflow regimediscriminant functionsuspension numberflow action numberwater temperature

《水科学进展》 2024 (002)

渗流诱发土体内部侵蚀输沙机制及致灾临界条件研究

265-273 / 9

国家重点研发计划资助项目(2023YFC3208601);国家自然科学基金资助项目(52109097) The study is financially supported by the National Key R&D Program of China(No.2023YFC3208601)and the National Natural Science Foundation of China(No.52109097).

10.14042/j.cnki.32.1309.2024.02.008

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