当前位置: X-MOL 学术Water › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV
Water ( IF 3.0 ) Pub Date : 2021-01-20 , DOI: 10.3390/w13030247
Dario Pumo , Francesco Alongi , Giuseppe Ciraolo , Leonardo V. Noto

Recent advances in image-based methods for environmental monitoring are opening new frontiers for remote streamflow measurements in natural environments. Such techniques offer numerous advantages compared to traditional approaches. Despite the wide availability of cost-effective devices and software for image processing, these techniques are still rarely systematically implemented in practical applications, probably due to the lack of consistent operational protocols for both phases of images acquisition and processing. In this work, the optimal experimental setup for LSPIV based flow velocity measurements under different conditions is explored using the software PIVlab, investigating performance and sensitivity to some key factors. Different synthetic image sequences, reproducing a river flow with a realistic velocity profile and uniformly distributed floating tracers, are generated under controlled conditions. Different parametric scenarios are created considering diverse combinations of flow velocity, tracer size, seeding density, and environmental conditions. Multiple replications per scenario are processed, using descriptive statistics to characterize errors in PIVlab estimates. Simulations highlight the crucial role of some parameters (e.g., seeding density) and demonstrate how appropriate video duration, frame-rate and parameters setting in relation to the hydraulic conditions can efficiently counterbalance many of the typical operative issues (i.e., scarce tracer concentration) and improve algorithms performance.

中文翻译:

河流监测的光学方法:一种基于模拟的方法探索LSPIV的最佳实验装置

基于图像的环境监测方法的最新进展为自然环境中远程流量测量打开了新的领域。与传统方法相比,此类技术具有许多优势。尽管具有成本效益的设备和用于图像处理的软件的广泛使用,但是在实际应用中仍很少系统地实施这些技术,这可能是由于在图像采集和处理的两个阶段都缺乏一致的操作协议。在这项工作中,使用PIVlab软件探索了在不同条件下基于LSPIV的流速测量的最佳实验设置,研究了性能和对某些关键因素的敏感性。不同的合成图像序列 在受控条件下生成具有逼真的速度剖面和均匀分布的浮动示踪剂的河流流再现。考虑到流速,示踪剂尺寸,接种密度和环境条件的各种组合,创建了不同的参数方案。使用描述性统计数据来表征PIVlab估计中的错误,从而处理每个方案的多个复制。仿真强调了某些参数(例如播种密度)的关键作用,并演示了与水力状况相关的适当视频时长,帧频和参数设置如何有效地抵消许多典型的操作问题(即,示踪剂浓度不足)和提高算法性能。考虑到流速,示踪剂尺寸,接种密度和环境条件的各种组合,创建了不同的参数方案。使用描述性统计数据来表征PIVlab估计中的错误,从而处理每个方案的多个复制。仿真强调了某些参数(例如播种密度)的关键作用,并演示了与水力状况相关的适当视频时长,帧频和参数设置如何有效地抵消许多典型的操作问题(即,示踪剂浓度不足)和提高算法性能。考虑到流速,示踪剂尺寸,接种密度和环境条件的各种组合,创建了不同的参数方案。使用描述性统计数据来表征PIVlab估计中的错误,从而处理每个方案的多个复制。仿真强调了某些参数(例如播种密度)的关键作用,并演示了与水力状况相关的适当视频时长,帧频和参数设置如何有效地抵消许多典型的操作问题(即,示踪剂浓度不足)和提高算法性能。使用描述性统计量来表征PIVlab估计中的错误。仿真强调了某些参数(例如播种密度)的关键作用,并演示了与水力状况相关的适当视频时长,帧频和参数设置如何有效地抵消许多典型的操作问题(即,示踪剂浓度不足)和提高算法性能。使用描述性统计量来表征PIVlab估计中的错误。仿真强调了某些参数(例如播种密度)的关键作用,并演示了与水力状况相关的适当视频时长,帧频和参数设置如何有效地抵消许多典型的操作问题(即,示踪剂浓度不足)和提高算法性能。
更新日期:2021-01-20
down
wechat
bug