Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

2026-07-31

AI Summary

This study investigates the use of large language models (LLMs) to automatically translate neutral graph representations of fluid systems into high-quality, functionally correct code executable in mainstream simulation environments such as WNTR and Modelica. The authors systematically evaluate ten state-of-the-art LLMs combined with six prompting strategies across multiple benchmark scenarios, assessing generated code through software quality metrics and simulation fidelity. This work presents the first systematic comparison in the domain of fluid system modeling that examines how different LLMs and prompt engineering techniques influence both syntactic correctness and functional fidelity of generated simulation code, offering empirical guidance for model-driven code generation. Experimental results demonstrate that optimal configurations can produce syntactically valid code; however, a significant gap remains in achieving high simulation fidelity, highlighting key directions for future improvement.

Abstract

Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications. In this study, we explore how LLMs can be harnessed to automatically translate a neutral graph representation of fluid system models into executable code for two widely adopted simulation environments: the Python library WNTR and the Modelica Standard Library. We conduct a systematic comparison of ten state-of-the-art LLMs and six prompting strategies that differ in the contextual information supplied (e.g., code or documentation). For each configuration we assess the generated code using a suite of software-quality metrics and we validate the functional fidelity of the resulting simulation models by reproducing benchmark fluid system scenarios. Our findings offer concrete guidance for researchers and engineers seeking to integrate LLM-driven code synthesis into model-based design pipelines. While the best-performing configurations achieve acceptable syntactic quality, we observe substantial gaps remain in simulation fidelity.

Problem

• simulation code generation
• fluid systems
• large language models
• code synthesis
• model-based design

Innovation

• Large Language Models
• Code Generation
• Fluid System Simulation
• Prompting Strategies
• Modelica