Application of a low-cost and high-efficiency PIV integrated measurement system in fluid mechanics

Particle image velocimetry (PIV) is a common noncontact technique for flow-field visualization and measurement. However, its application in educational contexts is often limited by the high cost of professional equipment, complex operation procedures, and substantial time delays between image acquisition and analysis. To address these challenges, this study develops an integrated, low-cost, and real-time PIV measurement system specifically designed for teaching fluid mechanics. The system provides a convenient and efficient image analysis platform, enabling students to quickly observe changes in flow patterns during experiments and deepen their understanding of fundamental fluid phenomena.

The proposed system integrates a smartphone camera as the image acquisition device with a custom Python-based analysis platform. The platform has a layered architecture comprising a user interface layer, a business logic layer, a data processing layer, and an infrastructure layer. The four key functional innovations include (1) real-time video stream acquisition via the IP camera application, enabling immediate capture and display of flow images; (2) an adaptive parameter module that automatically configures PIV analysis parameters (e.g., interrogation window size, overlap ratio, signal-to-noise threshold, and outlier replacement) based on image quality, thereby reducing the need for manual tuning; (3) direct processing of video inputs without preframe extraction, thereby streamlining the workflow; and (4) automated generation of multiple flow visualization outputs, including velocity vector plots, vorticity contour plots, and streamwise fluctuation intensity plots, eliminating additional postprocessing steps. The experimental setup consists of a small-scale circulating water channel, a low-power continuous wave laser sheet for illumination, a cylindrical obstacle, and seeded tracer particles. The system was evaluated in a cylinder flow experiment at low Reynolds numbers (Re = 100–400), with a fixed frame rate of 30 fps to ensure image stability under low-velocity conditions.

The system successfully captured the evolution of flow patterns around a cylinder across the Reynolds number range. At Re = 100, a stable laminar vortex pair was observed behind the cylinder with symmetrical vorticity distribution and low fluctuation intensity. As the Re increased to 200, periodic vortex shedding emerged, indicating the initial formation of a Kármán vortex street. At Re = 300 and Re = 400, the vortex shedding frequency markedly increased, the wake region broadened, and the vorticity distribution became more alternating and complex. Streamwise fluctuation intensity also increased markedly, reflecting enhanced flow instability. These observed stages—from steady vortex pairs to developed vortex streets—closely match classical fluid dynamics theories, confirming the system’s accuracy and reliability in capturing key transitional phenomena. Beyond the physical results, the system demonstrated clear educational benefits: it lowered the operational barrier for students, provided immediate visual feedback during experiments, and supported interactive learning through real-time analysis and multiformat outputs.

This work presents an integrated, smartphone-based PIV measurement system that effectively overcomes cost, complexity, and latency limitations associated with conventional PIV setups in teaching environments. By combining accessible hardware with intelligent, adaptive software, the system enables real-time flow-field capture, processing, and visualization. Experimental validation in a cylinder flow study shows that the system can accurately resolve flow evolution at low Reynolds numbers, making it a practical and effective tool for fluid mechanics education. The system not only enhances teaching efficiency and student engagement but also encourages hands-on experimentation and deeper conceptual understandings. Its low-cost and user-friendly design holds strong potential for widespread adoption in academic laboratories and instructional settings.