首页|New Machine Learning Study Results from Huazhong University of Science and Techn ology Described (Multi-objective Optimization Study of Airfoil Fin Printed Circu it Heat Exchanger With Tip Gap Based On Machine Learning)
New Machine Learning Study Results from Huazhong University of Science and Techn ology Described (Multi-objective Optimization Study of Airfoil Fin Printed Circu it Heat Exchanger With Tip Gap Based On Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating in Wuhan, Peopl e's Republic of China, by NewsRx journalists, research stated, "The printed circ uit heat exchanger (PCHE), a compact and highly efficient device, is capable of operating effectively under demanding conditions, which makes it ideal for super critical CO2 Brayton cycles. In this study, we present a novel airfoil fin PCHE with tip gap, and conduct multi-objective optimization on the tip gap and arrang ement of airfoil fins based on the analysis of the supercritical CO2 flow and he at transfer characteristics." The news reporters obtained a quote from the research from the Huazhong Universi ty of Science and Technology, "We conduct a parameter design using Design of Exp eriment to examine the impact of the dimensionless design parameters, including the horizontal number (Ch), staggered number (Cs), vertical number (Cv), and gap number (Cg). To forecast the flow and heat transfer performance, we utilize a n eural network model called Particle Swarm Optimization-Back Propagation (PSO-BP) . We employ the non-dominated sorting genetic algorithm II to obtain the Pareto optimal front by utilizing Ch, Cs, Cv, and Cg as variables for optimization, and the volumetric heat transfer coefficient (hv) and Fanning friction factor (f) a s objectives for optimization. The results find that the utilization of tip gap can enhance heat transfer while reducing flow resistance. The PSO-BPNN model exh ibits higher prediction accuracy and excellent generalization ability compared w ith traditional BPNN model. The VIKOR and TOPSIS methods identify compromise sch emes with excellent thermal-hydraulic performance."
WuhanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHuazhong University of Scienc e and Technology
NETL
NSTL
