Artificial intelligence empowering the teaching reform practice of the "Computational fluid dynamics and application" course
Yujie Zhu
Rocket Force University of Engineering
Zhensheng Sun
Rocket Force University of Engineering
Yu hu
Rocket Force University of Engineering
Zhiyu Zhao
Rocket Force University of Engineering
Jietang Zhu
Rocket Force University of Engineering
DOI: https://doi.org/10.59429/esta.v12i3.11355
Keywords: artificial intelligence; computational fluid dynamics; postgraduate education
Abstract
As a core course for graduate students in fields such as aerospace and energy power, Computational Fluid Dynamics (CFD) presents challenges for traditional teaching models due to its abstract theory, complex practice, and delayed feedback. These models struggle to meet the requirements of personalized parenting and industry demands. To address these issue, this paper systematically elaborates the implementation path of AI in theoretical teaching, practical training, and application expansion. It boosts students' efficiency in mastering core CFD knowledge, enhances programmatic and emulation practice competency, and provides a reference for teaching reform in graduate courses in science and engineering.
References
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