A hybrid AI-CFD framework for optimizing heat transfer of a premixed methane-air flame jet on inclined surfaces
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Kamma P.; Loksupapaiboon K.; Phromjan J.; Suvanjumrat C.
Publication year: 2025
Volume number: 27
ISSN: 2666-2027
Languages: English-Great Britain (EN-GB)
Abstract
This study presents a novel integration of artificial intelligence (AI) and computational fluid dynamics (CFD) simulations to investigate and optimize the heat transfer characteristics of a premixed methane-air flame jet impinging on an inclined surface. Key parameters—including the mixture equivalence ratio (ϕ = 0.8–2.0), burner-to-plate distance (H/d = 2–6), Reynolds number (Re = 400–1200), and plate inclination angle (θ = 0°–90°)—were systematically analyzed to evaluate their effects on heat flux distribution and thermal efficiency. Using OpenFOAM, the laminar flame behavior was modeled under diverse conditions, revealing strong agreement with experimental data, with average errors of 6.23 % for flame height and 6.47 % for thermal efficiency. To reduce the computational expense of these simulations, a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model was developed. The ANN accurately predicted thermal efficiency based on operational parameters, while the GA optimized these inputs to achieve maximum thermal efficiency of 76.9955 %, closely matching the CFD-predicted value of 70.86 % (discrepancy:6.1355 %). The ANN-GA model demonstrated a low absolute error of 7.97 %, confirming its reliability and precision. This research is the first to establish a robust AI-driven framework for optimizing flame jet heat transfer performance on inclined surfaces, offering valuable insights for improving industrial heating processes and advancing the application of AI in thermal system design. © 2025 The Author(s)
Keywords
Computational Fluid Dynamics (CFD), Heat transfer
Contact Information