Compressible Aerodynamics is a core course in the undergraduate curriculum for Aircraft Design and Engineering. The course focuses on aerodynamic phenomena and governing principles under high-speed flow conditions and serves as a critical foundation for the education and training of future aircraft designers. However, instruction in compressible aerodynamics currently suffers from a severe shortage of experimental components, which limits students' ability to connect theoretical concepts with physical flow behavior. To address this deficiency, this study develops an experimental teaching platform for compressible aerodynamics based on a newly constructed supersonic Ludwieg tube tunnel. By systematically integrating theoretical instruction with hands-on experimentation, the platform establishes a solid experimental foundation for cultivating high-level talent in Aircraft Design and Engineering.

Using Mach number measurement in a hypersonic wind tunnel as a representative example, this paper presents the fundamental operating principles of the Ludwieg tube and the theoretical basis and formula derivations for determining the incoming-flow Mach number using Pitot probes. A dedicated hypersonic wind tunnel experiment was designed to guide students through the measurement process, enabling them to develop a deeper understanding of normal shock wave theory and the correct application of isentropic relations in compressible aerodynamics through experimental design and practice.

High-speed schlieren visualization techniques were employed to observe the formation and evolution of detached shock waves ahead of Pitot probes, thereby rendering otherwise invisible aerodynamic phenomena directly observable. This visualization intuitively demonstrates the complete process of flow establishment within a hypersonic wind tunnel and significantly enhances students' conceptual understanding of key topics in compressible aerodynamics. Using Pitot probes in combination with pressure sensors, total pressure measurements were obtained upstream and downstream of shock waves at different incoming flow Reynolds numbers. Based on the Pitot–Rayleigh relationship derived from normal shock theory, the free-stream Mach number distribution in the test section was calculated for each case. The experimental results indicate that increasing the incoming flow Reynolds number leads to a thinner boundary layer at the nozzle exit, an increased effective area ratio between the nozzle exit and throat, and consequently a higher Mach number in the wind tunnel test section.

The Ludwieg tube hypersonic wind tunnel experimental teaching platform has been successfully implemented in undergraduate education at our institution and has since been widely used in both undergraduate and graduate experimental teaching. By overcoming the inherent limitations of conventional hypersonic wind tunnels—namely, prohibitive construction costs, high operational expenses, and limited accessibility—this platform provides a practical model for experimental instruction in compressible aerodynamics and offers a viable approach for training students in hypersonic experimental aerodynamics in China.

The experimental teaching of semiconductor physics, an important practical link in majors such as microelectronics and optoelectronic information, directly affects the students’ understanding of the physical essence of semiconductor devices and the cultivation of innovative abilities. Recently, with the rapid development of cutting-edge fields such as third-generation semiconductors and quantum devices, the inconsistency between traditional experimental teaching models and industry demand has become increasingly prominent. The current semiconductor physics experiment teaching mostly adopts the principle-verification single-point experiment mode. This approach lacks a systematic correlation between various experimental projects. This makes it difficult for students to construct a systematic cognitive framework of “material properties interface behavior device functionality,” which is significantly different from the emphasis on cultivating “systematic thinking” in engineering education certification.

To effectively solve the problem of poor connection between various knowledge modules and overcome students’ difficulty in building systematic cognition in semiconductor physics teaching, the School of Integrated Circuits at Huazhong University of Science and Technology systematically reconstructed the semiconductor physics curriculum and experiments with the core teaching philosophy of “crossing potential barriers.” Through carefully designed experimental teaching content updates and thematic connections, we focused on bridging the knowledge gap and established a modular experimental reform plan using thin-film solar cells as a carrier. This design breaks through the traditional linear structure of experiments and integrates eight key experiments into a knowledge loop through a four-dimensional advanced module of “semiconductor characteristics interface engineering device physics innovation expansion.” We innovatively adopted the “4-for-1+collaborative learning” model based on constructivist learning theory. This model not only resolves the contradiction between class hour limitations and knowledge breadth but also incorporates cutting-edge technologies such as AI data analysis and Raspberry Pi intelligent development to enhance teaching challenges.

The core of the innovative experimental system is in the construction of a closed-loop ability cultivation chain. The semiconductor basic module realizes a coupling analysis of PL spectroscopy and conductivity experiments, allowing students to independently establish a quantitative relationship between carrier concentration, mobility, and film quality. In the device research stage, the volt–ampere characteristic curve of solar cells is correlated and modeled with the previous material parameters, such as the interface defect density inferred by fitting the series resistance. This continuous design concretizes theoretical knowledge into actionable engineering indicators. The innovation challenge module introduces real scientific research scenarios that enable students to apply their ability to use open-source hardware to build testing systems and develop AI analysis algorithms.

(1) Integration of the knowledge system: Using thin-film solar cells as a carrier, knowledge of semiconductor physics was organically integrated through a four-dimensional module design (material properties interface engineering device research innovation challenges). The data-sharing mechanism enables students to master the key knowledge points of eight experiments while completing four, thus doubling the teaching capacity. (2) Cutting-edge technology integration: The introduction of TRPL, AI data analysis (such as convolutional neural network processing of photoconductive attenuation curves), and Raspberry Pi intelligent hardware enables breaking through the technical limitations of traditional silicon-based experiments. (3) Advanced ability cultivation: Through the design of “basic challenge” gradient tasks, students are transformed from passive operators to active creators, helping them achieve breakthroughs in their own technology transfer abilities. (4) Efficient resource construction: Dynamic update mechanisms (such as the rapid introduction of research on perovskite/organic stacked devices) ensure the synchronization of experimental content with cutting-edge technological achievements in the semiconductor field.