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Michael Twardowski

Publications and source records attributed to Michael Twardowski.

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The influence of body anisotropy on wake characteristics and enstrophy production for prolate ellipsoids at $ \mathrm{Re}_{D} = 10,000 $

The flow around prolate ellipsoids is investigated using Large Eddy Simulation (LES) at a Reynolds number of $ \mathrm{Re}_{D} = 10,000 $. Five different aspect ratios are considered, with $ \mathit{AR} = H/D $ varying from 5:1 to 1:1, where $D$ and $H$ represent the minor- and major- axes, respectively. The major axes of the ellipsoids are set perpendicular to the freestream, and the influence of body anisotropy on boundary layer separation, shear layer behaviour, enstrophy production, and local flow topology is examined. Higher body anisotropy leads to early separation of the boundary layer in the equatorial plane, resulting in a wider wake and a monotonic increase in pressure drag and total drag. Positive enstrophy production reaches a maximum approximately $2.5D$ downstream of the ellipsoids independently of body anisotropy. High body anisotropy leads to sustained negative enstrophy production in the near-wake, specifically near the poles of the 5:1 ellipsoid. Negative production occurs due to the distinct behaviour of streamlines near the high curvature pole, where they undergo strong anisotropic contraction in the cross-stream plane. Interactions between the vorticity vector and the intermediate eigenvector of the strain rate tensor are shown to be the primary source of enstrophy production close to the pole, and the intermediate eigenvalue exhibits negative values in this region. The negative production region is shown to be dominated by the unstable focus / compressing (UF/C) topology, which is consistent with findings from other studies that report negative enstrophy production in turbulent flows.

physics.flu-dyn

Benchmarking Image Similarity Metrics for Novel View Synthesis Applications

Traditional image similarity metrics are ineffective at evaluating the similarity between a real image of a scene and an artificially generated version of that viewpoint [6, 9, 13, 14]. Our research evaluates the effectiveness of a new, perceptual-based similarity metric, DreamSim [2], and three popular image similarity metrics: Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) [18, 19] in novel view synthesis (NVS) applications. We create a corpus of artificially corrupted images to quantify the sensitivity and discriminative power of each of the image similarity metrics. These tests reveal that traditional metrics are unable to effectively differentiate between images with minor pixel-level changes and those with substantial corruption, whereas DreamSim is more robust to minor defects and can effectively evaluate the high-level similarity of the image. Additionally, our results demonstrate that DreamSim provides a more effective and useful evaluation of render quality, especially for evaluating NVS renders in real-world use cases where slight rendering corruptions are common, but do not affect image utility for human tasks.

cs.CV