Estimated temperature-dependent interfacial heat transfer coefficient during gas cooling based on firefly algorithm and finite element method

• Published in: Heat and mass transfer Vol. 55; no. 9; pp. 2545 - 2558
• Main Authors: Wang, Xiaowei, Li, Huiping, He, Lianfang, Li, Zhichao, Wang, Zhaozhi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019; Springer Nature B.V
• Subjects: Algorithms; Austenitic stainless steels; Computer simulation; Conduction heating; Conductive heat transfer; Engineering; Engineering Thermodynamics; Experiments; Finite element analysis; Finite element method; Gas cooling; Global optimization; Heat; Heat and Mass Transfer; Heat transfer coefficients; Heuristic methods; Induction heating; Industrial Chemistry/Chemical Engineering; Nonlinear programming; Normal distribution; Original; Physical properties; Temperature dependence; Thermodynamics
• ISSN: 0947-7411; 1432-1181
• DOI: 10.1007/s00231-019-02608-y

The interfacial heat transfer coefficient (IHTC) is one of the most important thermal-physical parameters in heat conduction problem. To solve the IHTC in gas cooling, a 304 stainless steel sample is heated up to 800 °C by an induction heating device and then cooled by a high-pressure gas source. The IHTC between the high-pressure gas and the sample is evaluated by ZFA-FEM (normal distribution method, firefly algorithm (FA) and finite element method (FEM)) and ZGFA-FEM (normal distribution method, global optimization factor (G), firefly algorithm and finite element method) according to the temperature curve attained in the experiment. The research results show that, these IHTCs attained in the solution of IHCP according to those temperature curves of CFD simulation and the experiment are consistent, and the trend of IHTC attained in the experiment is consistent with that in the literature. The group scale of fireflies in ZGFA is much smaller than that in ZFA. Only 20 fireflies in ZGFA can ensure all fireflies move to the optimal position due to global optimization factor used in ZGFA. The convergence, iteration and CPU time of ZGFA are better than ZFA.