ABSTRACT The integration of particle image velocimetry (PIV) into undergraduate fluid mechanics laboratories is often hindered by high equipment costs, steep learning curves for analysis software, and limited classroom hours. To address these barriers, this study introduces a specialized, adaptive PIV software built upon the OpenPIV kernel, alongside a “Observation–Analysis–Verification” closed‐loop teaching framework. Designed for low‐cost setups utilizing smartphone imaging and continuous lasers, the software features an innovative image feature recognition algorithm. This algorithm automates the configuration of critical parameters—such as interrogation window size, overlap ratio, and signal‐to‐noise ratio (SNR) thresholds—thereby significantly reducing the technical expertise required of students. Validation through flow experiments over a cylinder and a vertical flat plate demonstrates that students can successfully capture vector fields across various Reynolds numbers. The system allows for quantitative verification of the Strouhal number–vortex shedding frequency relationship and visualization of flow evolution, vortex structures, and turbulence dissipation. Educational assessment confirms that this framework effectively shifts the pedagogical focus from tedious software debugging to the exploration of fundamental fluid physics. The proposed solution offers a cost‐effective and efficient pathway for the digital transformation of experimental fluid mechanics education.
Qin et al. (Mon,) studied this question.