[1]郑天清,林毅熙,田振恒,等.水流参数对物体在水中掉落距离的影响及预测[J].福建理工大学学报,2025,23(04):389-394.[doi:10.3969/j.issn.2097-3853.2025.04.012]
　ZHENG Tianqing,LIN Yixi,TIAN Zhenheng,et al.Influence and prediction of water flow parameters on fall distance of objects in water[J].Journal of Fujian University of Technology;,2025,23(04):389-394.[doi:10.3969/j.issn.2097-3853.2025.04.012]
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水流参数对物体在水中掉落距离的影响及预测()
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《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第23卷
期数:
2025年04期
页码:
389-394
栏目:
出版日期:
2025-08-25

文章信息/Info

Title:
Influence and prediction of water flow parameters on fall distance of objects in water
作者:
郑天清; 林毅熙; 田振恒; 魏盛军; 张露; 吴晋泽; 徐继璇
(福建理工大学机械汽车工程学院)福建省智能加工技术及装备重点实验室
Author(s):
ZHENG Tianqing;  LIN Yixi;  TIAN Zhenheng;  WEI Shengjun;  ZHANG Lu;  WU Jinze;  XU Jixuan
Fujian Key Laboratory of Intelligent Machining Technology and Equipment, Fujian University of Technology
关键词:
水流参数; 掉落距离; 神经网络; 鲍养殖; 湾外海域
Keywords:
water flow parameters;  fall distance;  neural network;  abalone farming;  bay area
分类号:
TH12
DOI:
10.3969/j.issn.2097-3853.2025.04.012
文献标志码:
A
摘要:
当前海面上的藻类掉落海底的运动规律尚未明确,无法开展坐底式鲍养殖沉箱的捕饵口设计。为此,论文以玻璃瓶为研究对象,重点探究水流流速、水深、玻璃瓶质量对玻璃瓶从水面掉落在水底距离的影响,进而揭示玻璃瓶从水面掉落在水底距离的运动规律;并构建掉落距离预测模型,预测玻璃瓶掉落在水底的距离。研究发现,随着水流流速的加快或回型槽内的水深增加,玻璃瓶从水面掉落在水底的距离增大;随着玻璃瓶的质量或倾角增大,玻璃瓶从水面掉落在水底的距离减小;所构建的掉落距离预测模型可较准确地预测玻璃瓶与海带从水面掉落在水底的距离,其平均预测偏差分别为2.79%与4.12%。
Abstract:
The motion law of seaweed falling from the surface of sea to the seafloor has not been well-defined, making it difficult to design appropriate bait holes in bottom-sitting caissons used for abalone farming. This study uses a glass bottle as the research object and investigates how factors such as water flow velocity, water depth, and the mass of the glass bottle affect the distance of glass bottle falling from water surface to water bottom. It aims to uncover the motion law of the glass bottle and develop a predictive model for the fall distance so as to predict the distance of the glass bottle falling to the bottom of the water. Findings show that the fall distance increases with water flow velocity and water depth in the return channel. In contrast, as the mass or tilt angle of the glass bottle increases, the fall distance decreases. The predictive model developed can accurately forecast the distance of glass bottle and seaweed falling from the water surface to water bottom, with an average prediction error of 2.79% and 4.12%, respectively.

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更新日期/Last Update: 2025-08-25