In this technical report, we extensively investigate the accuracy of outputs
from well-known generative artificial intelligence (AI) applications in
response to prompts describing common fluid motion phenomena familiar to the
fluid mechanics community. We examine a range of applications, including
Midjourney, Dall-E, Runway ML, Microsoft Designer, Gemini, Meta AI, and
Leonardo AI, introduced by prominent companies such as Google, OpenAI, Meta,
and Microsoft. Our text prompts for generating images or videos include
examples such as "Von Karman vortex street", "flow past an airfoil",
"Kelvin-Helmholtz instability", "shock waves on a sharp-nosed supersonic body",
etc. We compare the images generated by these applications with real images
from laboratory experiments and numerical software. Our findings indicate that
these generative AI models are not adequately trained in fluid dynamics
imagery, leading to potentially misleading outputs. Beyond text-to-image/video
generation, we further explore the transition from image/video to text
generation using these AI tools, aiming to investigate the accuracy of their
descriptions of fluid motion phenomena. This report serves as a cautionary note
for educators in academic institutions, highlighting the potential for these
tools to mislead students. It also aims to inform researchers at these renowned
companies, encouraging them to address this issue. We conjecture that a primary
reason for this shortcoming is the limited access to copyright-protected fluid
motion images from scientific journals.

我们对几个知名公司推出的生成式人工智能应用程序的输出准确性进行了全面调查，发现这些应用在描述流体力学领域常见的流体运动现象时存在着不足，可能会产生误导性的结果。这份报告向学术机构的教育者提出了一个警示，强调这些工具可能误导学生，并鼓励这些著名公司的研究人员解决这个问题。我们推测，这一不足的主要原因是无法获得受版权保护的流体运动图像。