Thermal-hydraulic-dynamic investigation of an inverted self-fluttering vortex generator

Abstract

Static vortex generators initiate vortical structures; an active generator intensifies the vortices through local agitations. Flow-induced vibrations present a hybrid approach to enhance thermal mixing: the role of vortical redistribution. To further our series of study regarding the self-fluttering vortex generator, machine learning was utilized in this work to analyze a large set of experimental data which includes a broader range of design parameters in order to find a general rule of the onset of the large flapping motion and its effect on the overall thermal transport and frictional pressure loss characteristics. In addition, this paper presented the differences in thermal hydraulic efficiencies of a flapping vortex generator to an aggregation of traditional ones of the same configuration. Through machine-learning-aided analysis, we captured a striking behavior of the generator: the power-law regime spanning across different designs, bridging a shorter flow speed range each time. Such findings highlight the localization and specialization of such setups to certain flow conditions. We observe a saturation effect of the vortical redistribution process and the eventual convergence between pumping power and thermal performance. Among all interactions, we see a propensity toward lower-speed flows: higher efficiencies and better thermal transport– stands out as the characteristic of this flapping vortex generator.