| 引用本文: | 陶洪飞,刘姚,陶娟琴,等.基于正交结果分析的内镶贴片式滴灌带性能优化设计[J].灌溉排水学报,0,():-. |
| TAO Hong-Fei,LIU Yao,TAO Juanqin,et al.基于正交结果分析的内镶贴片式滴灌带性能优化设计[J].灌溉排水学报,0,():-. |
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| 摘要: |
| 【目的】探究铺设坡度、含沙量、工作压力对内镶贴片式滴灌带的灌水均匀度和流量降幅的影响。【方法】开展了三因素三水平的均匀正交试验，采用极差分析和层次分析研究了铺设坡度、含沙量、工作压力对内镶贴片式滴灌带的灌水均匀度和流量降幅影响的主次顺序，运用PPR模型、NSGA-Ⅱ模型、线性回归模型进行对比分析。【结果】各因素对灌水均匀度的影响排序为含沙量>铺设坡度>工作压力；对流量降幅的影响程度排序为含沙量>工作压力>铺设坡度；层次分析结果与极差分析结果一致，均表明含沙量对灌水均匀度和流量降幅的影响大。基于试验数据构建灌水均匀度和流量降幅的线性回归模型，灌水均匀度线性回归模型的标准均方根误差为19.81%，流量降幅线性回归模型的标准均方根误差为14.17%，均小于20%，则模型表现效果好，灌水均匀度与工作压力和铺设坡度的p值均大于0.05，含沙量的p值小于0.05，可能存在非线性关系；构建灌水均匀度和流量降幅的投影寻踪回归模型，灌水均匀度投影寻踪回归模型的标准均方根误差为2.98%，流量降幅投影寻踪回归模型的标准均方根误差为2.42%，均小于10%，则模型表现效果极好，同时PPR模型预测效果优于线性回归模型，且PPR模型与NSGA-Ⅱ预测结果较为一致。【结论】最优工况为：铺设坡度为0%，含沙量为1 g/L，工作压力为96 kPa，灌水均匀度为0.958 5，流量降幅为0.083 5(8.35%)。 |
| 关键词: 内镶贴片式滴灌带；灌水均匀度；流量降幅；线性回归分析；投影寻踪回归；多目标遗传算法 |
| DOI： |
| 分类号:S275.6 |
| 基金项目: |
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| Performance Optimization Design of In-line Patch Drip Tape Based on Orthogonal Result Ahnalysis |
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TAO Hong-Fei1, LIU Yao1, TAO Juanqin1, ZHOU Liang2, LI Qiao1, MA Hemujiang·aihemaiti1, JIANG Youwei1
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1.Xinjiang Agricultural University;2.China Construction Xinjiang Construction Engineering
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| Abstract: |
| 【Objective】To explore the influence of the irrigation uniformity and flow reduction of the laying slope, sand content and working pressure in the inner patch drip irrigation belt. 【Methods】The uniform orthogonal test were used to study the influence of the three factors, and PPR model, NSGA-II model and linear regression models were used for comparative analysis.【Result】The influence of each factor on irrigation uniformity was sorted as sand content> laying slope > working pressure. The degree of influence on the flow rate reduction was sorted as sand content> working pressure > laying slope; The results of the analytic hierarchy were consistent with the results of the range analysis, indicating that the sand content had a great influence on the uniformity of irrigation and the decrease of flow. Based on the experimental data, a linear regression model of irrigation uniformity and flow reduction was constructed, and the standard rms error of the linear regression model of irrigation uniformity was 19.81%, and the standard rms error of the flow reduction linear regression model was 14.17%, both less than 20%, then the model performance was good. The p-value of irrigation uniformity, working pressure and laying slope are greater than 0.05, and the p-value of sand content is less than 0.05, which may have a nonlinear relationship; the projection tracking regression model of irrigation uniformity and flow reduction is constructed, the standard rms error of the irrigation uniformity projection tracing regression model is 2.98%, and the standard rms error of the flow reduction projection tracing regression model is 2.42%, both less than 10%, then the model performance effect is excellent. and at the same time The the prediction effect of PPR model was better than that of multiple regression model, and the prediction results of PPR model were consistent with NSGA-II.【Conclusion】Through the prediction model to optimize the main influencing factors, tThe optimal working condition is: the laying slope is 0%, the sand content is 1 g/L, and the working pressure is 96 kPa. In this optimal condition, the value of the irrigation uniformity is 0.958 5, and the flow drop is 0.083 5(8.35%). |
| Key words: Inner patch drip irrigation belt; Irrigation uniformity; Flow reduction; SPSS; PPR; NSGA-Ⅱ |
