摘要
为探究进水流量、矿化度、滤网孔径对微灌用微压过滤冲洗池水头损失的影响,开展微压过滤冲洗池运行特性的物理模型试验,建立了微压过滤冲洗池在微咸水条件下的水头损失预测模型。结果表明,在方差分析中仅流量对微压过滤冲洗池的水头损失影响显著,确定的影响因素排序由大到小为进水流量、矿化度、滤网孔径;该模型的决定系数R^(2)为0.955,均方根误差为0.0523,平均相对误差为3.20%。建立的模型可准确预测微咸水条件下的水头损失,研究成果可为泵前过滤设备的实际应用提供技术支撑。
The effects of inlet flow,salinity and filter aperture on the head loss of micro-pressure filtration and clean-ing tank for micro-irrigation were investigated.The physical model test of the influence of the operating characteristics of the micro-pressure filtration and cleaning tank was carried out,and the head loss prediction model under the condition of brackish water in the micro-pressure filtration and cleaning tank was established.The results show that in variance analy-sis,only the flow rate has a significant impact on the head loss of the micro-pressure filtration and cleaning tank.The or-der of the influencing factors was sorted as follows:inlet flow,salinity,filter aperture.The determination coefficient R^(2) was 0.955,the root mean square error was 0.0523,and the average relative error was 3.20%.The established model can accurately predict the head loss under brackish water conditions.The research results can provide technical support for the practical application of pre-pump filtration equipment.
作者
吴梓境
陶洪飞
喜炜
李巧
马合木江·艾合买提
姜有为
杨文新
魏建群
WU Zi-jing;TAO Hong-fei;XI Wei;LI Qiao;MAHEMUJIANG Ahmat;JIANG You-wei;YANG Wen-xin;WEI Jian-qun(a.College of Hydraulic and Civil Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Key Laboratory of Hydraulic Engineering Safety and Water Disaster Prevention,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Agricultural Vocational and Technical University,Changji 831100,China)
出处
《水电能源科学》
北大核心
2024年第11期204-208,共5页
Water Resources and Power
基金
新疆维吾尔自治区创新环境(人才、基地)建设专项--青年科学基金项目(2021D01B58)
国家自然科学基金项目(52369013)
2019年度新疆维吾尔自治区人民政府公派出国留学成组配套项目(2019Q075)
新疆维吾尔自治区重大科技专项(2022A02003-4)。
关键词
微压过滤冲洗池
水头损失
方差分析法
量纲分析法
预测模型
micro-pressure filtration and cleaning tank
head loss
variance analysis
dimensional analysis method
prediction model