Automated Measurement Technology for Hydraulic Model Testing
To enhance the efficiency and measurement precision of hydraulic model tests, this Branch initiated the "Automated Measurement Technology for Hydraulic Model Testing." This project upgrades traditional invasive measurement methods to automated, non-contact technologies, preventing measuring instruments from interfering with the flow field. Furthermore, it establishes a more comprehensive capability for collecting hydraulic data, providing more accurate technical support for hydraulic testing and engineering planning.
Technical Features
This research focuses on the introduction and transfer of advanced measurement techniques, applying well-established Large-Scale Particle Image Velocimetry (LS-PIV) and non-contact acoustic water-depth measurement technology to indoor hydraulic model tests.
LS-PIV can capture two-dimensional flow velocity field information through image analysis. It helps overcome the limitations of traditional, sparse measurement points as well as image distortion issues. Combined with multi-angle imaging technology, it significantly improves flow field analysis capabilities. Non-contact acoustic measurement features high precision, low power consumption, and real-time monitoring, allowing rapid acquisition of water depth data and integration with Internet of Things (IoT) technology for remote monitoring applications.
Compared with traditional point-based measurements, automated measurement technology can simultaneously acquire line, surface, and multi-dimensional flow velocity and water depth data. This provides a more complete understanding of flow-topography interactions, significantly improving hydraulic model testing efficiency and data quality.
Applications & Results
This research has been successfully applied to multiple hydraulic model tests, including the Daan River model, Dajia River model, levee foundation scour model, and Tamsui River Erchong Floodway model, completing automated image-based measurements of flow velocity and water depth.
During testing, scale calibration was performed using multiple control points. The image measurement results were cross-validated against existing measurement data from the Planning Branch and acoustic measurement results, confirming the high accuracy and reliability of this automated technology.
The research outcomes have established a standardized workflow for automated measurement in hydraulic model tests. This workflow can be broadly applied to river management, reservoir engineering, flood control structures, and scour experiments. Beyond improving testing efficiency and accuracy, it lays a vital technical foundation for smart hydraulic testing, digital twins, and the the development of smart hydraulic engineering.