| 2 | Temporally resolving premixed turbulent flame structures using self-supervised adversarial reconstruction of CH-PLIF显示文摘Understanding the turbulence-flame interaction is crucial to model the low-emission combustors developed for energy and propulsion applications. To this end, a novel frame interpolation (FI) method is proposed to better resolve the spatiotemporal evolution of premixed turbulent flame structures. The framework is completely selfsupervised, agnostic to optical flow, and driven by leveraging transferrable feature knowledge at lower speeds and adversarial learning to statistically map the flame dynamics across frames. The method is successfully applied on a 10 kHz CH planar laser-induced fluorescence (PLIF) dataset of highly wrinkled premixed flames with turbulent Reynolds numbers (ReT ) of 1100, 1400, and 7900, by down-sampling the image sequence to 5 kHz and restoring the sequence back to 10 kHz via FI. All reconstructions recovered important flame events and displayed excellent resemblance of the corrugated CH-layer geometries to that of the ground truths, with average intersection over union (IoU) and structural similarity index (SSIM) scores of 0.49 and 0.82, which are above the high-similarity baselines of 0.36 and 0.75, respectively. The wrinkling parameters (WP) of the flames also matched the ground truths, wherein R2 was roughly 0.95 for ReT = 1100 and 1400 and 0.85 for ReT = 7900 (lower due to the turbulence-induced uncertainties). The FI is further iteratively repeated to 40 kHz on the ReT = 7900 flames to facilitate pocket analysis by confidently linking their origin of formation, thus, enabling distinction from 3D tunnels, and improving statistical characterization of their consumption speeds. Given that the object features do not exhibit highly turbulent motions with regard to the initial time step, the proposed FI method is shown to be highly accurate and useful to analyzing finite-resolution experimental image sets including, but not restricted to, CH-PLIF. | Ji-Hun Oh Aaron W.Skiba Stephen D.Hammack Constandinos M.Mitsingas Campbell D.Carter Tonghun Lee | 2023 | Energy and AI2023，11，1： | 0 |
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