Abstract: As one of the fastest-growing directions in artificial intelligence, the large model technology has achieved remarkable success in realms such as natural language processing and computer vision and is vigorously expanding its influence in empowering scientific research. It has also become a powerful tool in aerodynamics, possessing significant potential to expedite aerodynamic experiments and computations and assist aerodynamic theory and knowledge discovery. This paper begins by presenting an overview of large models for language processing, computer vision, and scientific computing. Subsequently, the paper outlines the conceptual framework of large models for scientific computing in aerodynamics, reviewing the current research progress from various perspectives, including flow field prediction, turbulence modeling, aerodynamic performance prediction, and aerodynamic configuration design. Furthermore, key techniques of large models in aerodynamics are discussed in-depth from the perspectives of model architecture, feedback alignment, and the generation of big aerodynamic data. Lastly, developing directions of large models in aerodynamics are prospected, including the construction of a unified pre-trained foundational model, the integration of aerodynamic knowledge to support scientific discoveries, and the development of discipline-specific agents.