Can Large Language Models Revolutionize Fluid Simulation in Engineering?

Fluid flow simulation is a major challenge for sectors such as aeronautics, chemistry, and the environment. While traditional methods are effective, they face limitations due to the increasing complexity of industrial problems. They often require heavy computations, specialized expertise, and struggle to model phenomena such as turbulence or interactions between multiple phases. Artificial intelligence, and more specifically large language models, now offers new perspectives for automating and optimizing these simulations.

These models are capable of analyzing complex data and generating real-time predictions. For example, they can anticipate the behavior of a fluid in a pipe or improve the accuracy of turbulence models by relying on data from detailed simulations. Their strength lies in their ability to process diverse information, such as natural language instructions or design histories, enabling a more intuitive and accessible approach.

In the field of optimization, large language models facilitate the adjustment of simulation parameters, the design of optimal geometric shapes, or the tuning of operating conditions. They thereby reduce costs and accelerate processes, whereas classical methods, such as genetic algorithms, required significant time and expertise. By combining these models with machine learning techniques, it becomes possible to optimize equipment like aircraft wings or chemical reactors by finding compromises between multiple conflicting objectives.

Automation of simulations is another major advantage. Through natural language interfaces, these models help automatically configure initial parameters, generate meshes, or select appropriate solvers. Frameworks like OpenFOAMGPT or MetaOpenFOAM illustrate this advancement by enabling complete execution of simulations with less human intervention. However, their adoption still presents challenges, particularly in terms of physical reliability and adaptation to the real-world constraints of engineers.

To overcome these obstacles, researchers are exploring avenues such as integrating physical knowledge into models or developing specialized databases. The goal is to make these tools more robust and transparent so that they can become industry standards. In the long term, this approach could democratize access to high-performance fluid simulations while fostering innovation in demanding fields.

Resources and References

Official Reference

DOI: https://doi.org/10.53941/sce.2026.100003

Title: Large Language Models for Automating Computational Fluid Dynamics (CFD): From Predictive Modeling and Optimization to Execution Scheduling

Journal: Smart Chemical Engineering

Publisher: Scilight Press Pty Ltd

Authors: Guodong Gai; Pei-Zhong Ma; Jiankun Li; Zheng-Hong Luo; Li-Tao Zhu