G-Agent: A Large Language Model-Driven Multi-Agent Framework for Automated Configuration Optimization of Scramjet Cavity Combustors

This paper presents a G-Agent, a large language model (LLM)-driven multi-agent framework that automates the optimization workflow for an axisymmetric cavity-based scramjet combustor. The framework integrates a supersonic compressible reacting flow solver with the Qwen3-VL-2B-Instruct model. It consists of four specialized agents: Interactor, Runner, Corrector, and Optimizer. The Interactor parses user requirements and generates simulation input files. The Runner manages mesh generation, solver configuration, and simulation execution. The Corrector diagnoses failed cases by analyzing fault logs and applies corrective actions. The Optimizer coordinates the optimization process using a trust-region response surface method to maximize thrust. The framework employs Latin hypercube sampling (LHS) to explore the design space defined by three key geometric parameters: isolator length, cavity depth, and fuel injector position. Without manual intervention beyond initial specifications, the G-Agent successfully conducted multiple optimization iterations. Starting from a baseline thrust of 489.4 N, the optimized configuration achieved 523.5 N after three iterations, reaching a relative improvement of 6.97%. Our proposed agent achieves closed-loop control of the entire optimization process, significantly lowering the technical barrier and human effort in scramjet combustor design.