Data and knowledge-enabled intelligent aerodynamic design for civil aircraft

With the rapid advancement of high-performance computing and artificial intelligence technologies, data-driven AI models have been extensively researched in the field of civil aircraft aerodynamic design, demonstrating significant potential in design space compression, key feature extraction, flow field prediction, and intelligent optimization design. However, the application of purely data-driven models in engineering design still faces many challenges, including the scarcity and high acquisition cost of domain-specific data, as well as deficiencies in model reliability, generality, interpretability, and usability. Integrating physical knowledge and aerodynamic design experience into model development has become a key approach to addressing these challenges, providing an important direction for advancing technology in this field. This paper, from the perspective of civil aircraft engineering design and supported by relevant practices in intelligent aerodynamic design, reviews recent theories and progress in data- and knowledge-driven AI models in the areas of knowledge embedding, knowledge correction, and knowledge discovery. It further explores the current state of research and application potential of data- and knowledge-driven methods in civil aircraft aerodynamic design, while offering insights into the future of new paradigms in intelligent aerodynamic design.