Abstract:

Traditional education in hydraulic engineering has been based on passive flow observation and manual calculation of parameters. This methodology is often insufficient to effectively link abstract theory and real-world fluid dynamics for students. This paper presents an innovative laboratory practice designed to transform traditional instruction into an autonomous learning model. The main objective is to facilitate student mastery of complex fluid behavior using advanced visual tools and digital processing applications, while promoting student engagement. Empowering learners as direct manipulators of the laboratory system, this approach promotes a hands-on understanding of hydraulic behaviors.

The experiment is conducted in a hydraulic installation with a pump, a control system, and manual valves to regulate the flow. To trace fluid motion, a laser is used to illuminate the area of study. This system employs a laser with a collimator lens to generate a light sheet, making tracer particles visible. The data are acquired using a high-speed camera synchronized with laser pulses through a PTU (Programmable Timing Unit).

The main innovation of this practice lies in the autonomy given to students. Instead of being provided with a step-by-step guide, students are responsible for the entire experiment. The only instruction provided is a basic manual for the usage of the installation. Key parameters such as flow rate and pulse frequency are defined by the students themselves, who must determine optimal values to obtain reliable results. This hands-on interaction enhances their ability to solve real-world engineering problems during experimental work.

The post-analysis phase constitutes a key element of the learning process. Once raw images are captured, they are processed within a specialized digital environment developed in MATLAB. This application allows students to manipulate their experimental data. Using PIV (Particle Image Velocimetry) technology, students transform high-frequency image sequences into velocity fields and streamline visualizations.

This tool enables the visualization of complex flow patterns that are otherwise difficult to detect, such as vortex formation or boundary layer separation. These phenomena are often challenging to understand through purely theoretical approaches, but their direct visualization allows students to grasp fluid behavior in a more intuitive way. Direct interaction with MATLAB processing parameters allows students to establish a clear relationship between digital processing and physical flow behavior. As they adjust processing filters in real time, they observe the immediate impact on flow visualization results.

The results suggest an improvement in student engagement, autonomy, and conceptual understanding, as well as in long-term knowledge retention, as observed through increased participation during laboratory sessions and improved interpretation of flow phenomena. In addition, the methodology promotes transversal skills such as technical problem-solving and digital literacy. While the use of post-processing tools reduces the need for repetitive mathematical calculations, the main objective is to shift the focus toward understanding and interpreting the results.

In conclusion, autonomous experimentation combined with digital analysis provides an effective framework for fluid mechanics education, while fostering critical and independent problem-solving skills.

Keywords:

Fluid Mechanics Education, Autonomous Learning, Laboratory Learning, Particle Image Velocimetry, Student Engagement, Engineering Education.