Integrating Artificial Intelligence and CFD to Analyze Inlet Parameter Effects in Vortex Pilot Gas Heaters

Abstract

The Vortex Pilot Gas Heater (VPGH) represents an innovative application of vortex tube technology within natural gas transmission and distribution pressure regulation systems. By sealing the hot end of the vortex tube and integrating an external vortex heater, a self-heating mechanism for pilot gas and vacuum nozzles is achieved, effectively preventing freezing blockages caused by the Joule–Thomson effect. In this study, computational fluid dynamics (CFD) simulations based on the standard k-epsilon turbulence model are conducted to analyze the internal flow field. Furthermore, by adopting a single-variable control method, the effects of key inlet parameters on the operational performance of the VPGH are systematically evaluated. In addition, this work discusses the potential of integrating AI-driven design optimization frameworks with CFD modeling to enhance predictive accuracy and design adaptability in complex flow systems.