Kavli Affiliate: Long Zhang
| First 5 Authors: Yuxuan Chen, Long Zhang, Xu Zhu, Hua Zhou, Zhuyin Ren
| Summary:
Merging natural language interfaces with computational fluid dynamics (CFD)
workflows presents transformative opportunities for both industry and research.
In this study, we introduce OptMetaOpenFOAM – a novel framework that bridges
MetaOpenFOAM with external analysis and optimization tool libraries through a
large language model (LLM)-driven chain-of-thought (COT) methodology. By
automating complex CFD tasks via natural language inputs, the framework
empowers non-expert users to perform sensitivity analyses and parameter
optimizations with markedly improved efficiency. The test dataset comprises 11
distinct CFD analysis or optimization tasks, including a baseline simulation
task derived from an OpenFOAM tutorial covering fluid dynamics, combustion, and
heat transfer. Results confirm that OptMetaOpenFOAM can accurately interpret
user requirements expressed in natural language and effectively invoke external
tool libraries alongside MetaOpenFOAM to complete the tasks. Furthermore,
validation on a non-OpenFOAM tutorial case – namely, a hydrogen combustion
chamber – demonstrates that a mere 200-character natural language input can
trigger a sequence of simulation, postprocessing, analysis, and optimization
tasks spanning over 2,000 lines of code. These findings underscore the
transformative potential of LLM-driven COT methodologies in linking external
tool for advanced analysis and optimization, positioning OptMetaOpenFOAM as an
effective tool that streamlines CFD simulations and enhances their convenience
and efficiency for both industrial and research applications. Code is available
at https://github.com/Terry-cyx/MetaOpenFOAM.
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