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图像测流技术在高拱坝廊道排水沟中的应用OA

Application of image-based flow measurement technology in drainage ditches of high-arch dam galleries

中文摘要英文摘要

[目的]图像测流技术是一种有效的非接触式流量测量方法,在明渠及天然河流中已得到广泛应用,但在高拱坝廊道排水沟等受限复杂环境中的适用性尚未得到充分论证,为系统性评估该技术在此特定场景下的应用效果.[方法]综合采用模型试验与现场试验进行验证,通过布设控制点进行相机标定,在模型试验中测试上游、下游、侧向三种拍摄视角,并控制流量变化,水位通过基于单目视觉的无水尺水位测量方法获取,表面流场采用粒子图像测速技术计算,并对比了有/无示踪粒子以及图像预处理前后的效果.现场试验在真实廊道排水沟中进行,以水尺与量水堰测量值为基准,验证该技术的工程适用性.[结果]结果显示:模型试验中,基于图像的水位识别方法相对误差均小于7%,在有示踪粒子条件下不同拍摄视角的测流误差均小于4%,侧向拍摄的流量测量结果准确性低于上下游视角,流量计算误差随流量增大而增加,从低流量(21.40 m3/h)时的5.77%增至高流量(65.10 m3/h)时的35.17%,经过图像预处理后误差可降至3.60%~28.74%.现场试验的水位测量误差为0.78%,在有示踪粒子及无示踪粒子但经预处理的条件下,流量计算相对误差分别达到1.17%和1.81%.[结论]结果表明:图像测流技术适用于高拱坝廊道排水沟的流量监测,其中上下游视角为最佳拍摄方位,在具备自然或人工示踪的条件下,该技术可达到较高的测量精度,图像预处理可提升测流效果,流量增大会导致测流算法的流场特征识别能力下降,从而降低图像测流的准确性.通过对图像测流技术在模型与现场的应用情况进行分析,证实了该技术在高拱坝廊道排水沟场景中具备良好的应用潜力,有望为算法优化与推广应用提供参考.

[Objective]Image-based flow measurement is an effective non-contact method for discharge measurement,which has been widely used in open channels and natural rivers.However,its applicability in confined and complex environments such as the drainage ditches of high-arch dam galleries has not been fully validated.The aim is to systematically evaluate the performance of this technology in this specific scenario.[Methods]Validation was conducted using a combination of model tests and field tests.Camera calibration was performed by placing control points.In the model tests,three camera perspectives(upstream,downstream,and lateral)were tested while controlling discharge variations.Water levels were obtained using a monocular vision-based method without a staff gauge,and surface flow fields were calculated using the particle image velocimetry(PIV)technique.Comparisons were made between conditions with and without tracer particles,as well as before and after image preprocessing.Field tests were conducted in a real gallery drainage ditch.Measurements from staff gauges and a measuring weir were used as benchmarks to validate the engineering applicability of this technology.[Results]The result showed that in the model tests,the relative errors of the image-based water level recognition method were all below 7%.Under conditions with tracer particles,the flow measurement errors across different camera perspectives were all less than 4%.The accuracy of the flow measurement result from the lateral perspective was lower than that from the upstream and downstream perspectives.The flow calculation error increased with the increase of discharge,rising from 5.77%at a low discharge(21.40 m3/h)to 35.17%at a high discharge(65.10m3/h).After image preprocessing,the errors could be reduced to the range of 3.60%~28.74%.In the field tests,the water level measurement error was 0.78%.Under conditions with tracer particles and under conditions without tracer particles but with image preprocessing,the relative errors of discharge calculation reached 1.17%and 1.81%,respectively.[Conclusion]The result show that image-based flow measurement technology is suitable for discharge monitoring in the drainage ditches of high-arch dam galleries,and the upstream and downstream perspectives are the optimal camera perspectives.Under conditions with natural or artificial tracers,this technology can achieve high measurement accuracy.Image preprocessing can enhance the flow measurement performance,while an increase in discharge can reduce the algorithm's ability to identify flow field features,thereby decreasing the accuracy of image-based flow measurement.By analyzing the application of image-based flow measurement technology in both model and field settings,it is confirmed that this technology has good potential for application in the drainage ditches of high-arch dam galleries and is expected to provide a valuable reference for algorithm optimization and broader application.

毛延翩;侯春尧;王子昂;段炎冲;谭大文;李永龙;李丹勋

中国长江电力股份有限公司,湖北宜昌 443002||清华大学 水利水电工程系,北京 100084中国长江电力股份有限公司,湖北宜昌 443002清华大学 水利水电工程系,北京 100084||清华大学 水圈科学与水利工程全国重点实验室,北京 100084清华大学 水利水电工程系,北京 100084||清华大学 水圈科学与水利工程全国重点实验室,北京 100084中国长江电力股份有限公司,湖北宜昌 443002清华四川能源互联网研究院,四川成都 610000清华大学 水利水电工程系,北京 100084||清华大学 水圈科学与水利工程全国重点实验室,北京 100084

建筑与水利

图像测流水位识别廊道排水沟高拱坝适用性流量示踪粒子相对误差

image-based flow measurementwater level recognitiongallery drainage ditcheshigh-arch damapplicabilitydischargetracer particlesrelative error

《水利水电技术(中英文)》 2026 (5)

147-165,19

三峡金沙江川云水电开发有限公司永善溪洛渡电厂科研项目(Z412302022)国家自然科学基金黄河水科学研究联合基金项目(U2243241)

10.13928/j.cnki.wrahe.2026.05.012

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