Skin-friction-based identification of the critical lines in a transonic, high reynolds number flow via temperature-sensitive paint
| Autor: | Costantini M., Henne U., Klein C., Miozzi M. |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Sensors (Basel) 21 (2021). doi:10.3390/s21155106 info:cnr-pdr/source/autori:Costantini M.; Henne U.; Klein C.; Miozzi M./titolo:Skin-friction-based identification of the critical lines in a transonic, high reynolds number flow via temperature-sensitive paint/doi:10.3390%2Fs21155106/rivista:Sensors (Basel)/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume:21 |
| Popis: | In this contribution, three methodologies based on temperature-sensitive paint (TSP) data were further developed and applied for the optical determination of the critical locations of flow separation and reattachment in compressible, high Reynolds number flows. The methodologies rely on skin-friction extraction approaches developed for low-speed flows, which were adapted in this work to study flow separation and reattachment in the presence of shockwave/boundary-layer in-teraction. In a first approach, skin-friction topological maps were obtained from time-averaged surface temperature distributions, thus enabling the identification of the critical lines as converging and diverging skin-friction lines. In the other two approaches, the critical lines were identified from the maps of the propagation celerity of temperature perturbations, which were determined from time-resolved TSP data. The experiments were conducted at a freestream Mach number of 0.72 and a chord Reynolds number of 9.7 million in the Transonic Wind Tunnel Göttingen on a VA-2 super-critical airfoil model, which was equipped with two exchangeable TSP modules specifically designed for transonic, high Reynolds number tests. The separation and reattachment lines identified via the three different TSP-based approaches were shown to be in mutual agreement, and were also found to be in agreement with reference experimental and numerical data. |
| Databáze: | OpenAIRE |
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