Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows

TL;DR

FlamePilot is an LLM-based agent that automates and self-corrects complex combustion simulations, integrating literature knowledge with CFD tools to enhance research productivity and accuracy.

Contribution

This work introduces FlamePilot, the first autonomous LLM agent capable of managing end-to-end combustion modeling workflows with self-correction and literature integration.

Findings

Achieved perfect 1.0 executability score and 0.438 success rate, surpassing prior agents.

Successfully translated research papers into configured simulations with minimal human input.

Demonstrated effective autonomous multi-step combustion simulation and refinement.

Abstract

The rapid evolution of large language models (LLMs) is transforming artificial intelligence into autonomous research partners, yet a critical gap persists in complex scientific domains such as combustion modeling. Here, practical AI assistance requires the seamless integration of domain literature knowledge with robust execution capabilities for expertise-intensive tools such as computational fluid dynamics (CFD) codes. To bridge this gap, we introduce FlamePilot, an LLM agent designed to empower combustion modeling research through automated and self-corrective CFD workflows. FlamePilot differentiates itself through an architecture that leverages atomic tools to ensure the robust setup and execution of complex simulations in both OpenFOAM and extended frameworks such as DeepFlame. The system is also capable of learning from scientific articles, extracting key information to guide the simulation from initial setup to optimized results. Validation on a public benchmark shows FlamePilot achieved a perfect 1.0 executability score and a 0.438 success rate, surpassing the prior best reported agent scores of 0.625 and 0.250, respectively. Furthermore, a detailed case study on Moderate or Intense Low-oxygen Dilution (MILD) combustion simulation demonstrates its efficacy as a collaborative research copilot, where FlamePilot autonomously translated a research paper into a configured simulation, conducted the simulation, post-processed the results, proposed evidence-based refinements, and managed a multi-step parameter study to convergence under minimal human intervention. By adopting a transparent and interpretable paradigm, FlamePilot establishes a foundational framework for AI-empowered combustion modeling, fostering a collaborative partnership where the agent manages workflow orchestration, freeing the researcher for high-level analysis.