Fastagent speeds up industrial fluid dynamics design with smart simulation control
FastAgent: A Physics-Aware FastFluent-to-Fluent Workflow for Rapid CFD Screening and Industrial Steam Dewaxing Design
Artificial Intelligence
Summary
Simulating complex fluid flows in industries like casting can be very slow and expensive because of detailed physics and many design options. The authors created FastAgent, a smart system that chooses how detailed simulations need to be and when to run them, based on what questions need answers. This approach makes simulations faster and more accurate at focusing on important parts of the design. They demonstrated it by improving steam dewaxing in casting, reducing compute time and helping find better designs to prevent cracks.
What this means in practice
- •For industrial process engineers: Use FastAgent to rapidly screen and optimize steam dewaxing designs in investment casting, reducing costly simulation time and improving design confidence.
- •For mechanical design teams: Apply the framework to identify structural risks and improve component durability by linking fluid behavior to stress analysis in complex industrial conditions.
Authors
Chen YanKai, Yu QiuHeng, Yuan Man, Zou Ting, Wu Lin, He Jian, Zhang QiongYuan, Jing Tao, Xu QingYan
Abstract
Industrial computational fluid dynamics (CFD) becomes costly when complex geometries, long transients and coupled physics must be evaluated across large design spaces. We introduce FastAgent, a physics-aware campaign controller built around FastFluent, a lightweight numerical engine for rapid physical screening. FastFluent supplies stage-specific fields and diagnostics; FastAgent allocates physical models, numerical resolution and simulation horizons by the active quantities of interest (QoIs), while returned Fluent discrepancies update subsequent decisions. Across six public CFD benchmark campaigns, the framework reduces normalised worst-QoI error by 5.68-99.14% and accelerates decision-relevant computation by factors of 1.53-15.36 relative to a matched Generic Agent control. Applied to high-pressure steam dewaxing in investment casting, FastAgent identifies nonuniform condensation heating as the dominant startup mechanism and separates rapid risk-window localisation from high-fidelity melt-completion confirmation. The rapid route requires 81.39% fewer cell-time updates than a fine-grid, small-time-step reference. Structural analysis localises fracture at the observed blade edge. Screening 131,072 outlet layouts and selectively evaluating candidates with Fluent and Abaqus identifies Sparse-6, which reduces simulated crack opening by 96.0% and connected crack extent by 66.7% relative to the Original design. These results demonstrate how rapid numerical evidence and adaptive fidelity control connect large CFD search spaces to mechanism-guided industrial design.