Xiaoxing Feng | Searcharxiv

arXiv2023

Central recirculation zone in a V-shaped premixed swirling flame

This paper presents an experimental study on the emergence of the central recirculation zone (CRZ) in a V-shaped premixed swirling flame, using simultaneous measurement of particle image velocimetry (PIV) and CH* chemiluminescence. The results show that either increasing the Reynolds number (Re) or decreasing the equivalence ratio (Φ) would facilitate the emergence of CRZ. Further analysis demonstrates that the CRZ characteristics and its emergence are strongly influenced by the inner shear layer (ISL) surrounding the CRZ, while the swirl intensity remains unchanged. Dimensional analysis is performed to understand the underlying mechanism, suggesting the CRZ emergence is controlled by a non-dimensional parameter, Re_s=|γ|_max D/ν_s, defined based on the maximum ISL intensity (|γ|_max), the exit diameter (D), and the kinematic viscosity (ν_s) of the burnt gas. By estimating the temperature and viscosity with a simple heat-loss model, we show in the |γ|_max D-ν_s regime diagram that the cases with and without CRZ are separated by a single boundary line, corresponding to a critical Re_s of about 424. This verifies the applicability of the proposed Re_s criterion to lean-premixed V-shaped swirling flames under various conditions. Unlike most previous works that attribute the CRZ of swirling flames to vortex breakdown, the present work reveals the non-negligible effect of the ISL, especially the CRZ suppression when the ISL is weakened by flame heating.

physics.flu-dyn↗

arXiv2022

Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression

This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor-based voxelized PCG. We call this compression method SparsePCGC. The proposed SparsePCGC is a low complexity solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale representation also allows us to compress scale-wise MP-POVs by exploiting cross-scale and same-scale correlations extensively and flexibly. The overall compression efficiency highly depends on the accuracy of estimated occupancy probability for each MP-POV. Thus, we first design the Sparse Convolution-based Neural Network (SparseCNN) which stacks sparse convolutions and voxel sampling to best characterize and embed spatial correlations. We then develop the SparseCNN-based Occupancy Probability Approximation (SOPA) model to estimate the occupancy probability either in a single-stage manner only using the cross-scale correlation, or in a multi-stage manner by exploiting stage-wise correlation among same-scale neighbors. Besides, we also suggest the SparseCNN based Local Neighborhood Embedding (SLNE) to aggregate local variations as spatial priors in feature attribute to improve the SOPA. Our unified approach not only shows state-of-the-art performance in both lossless and lossy compression modes across a variety of datasets including the dense object PCGs (8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with standardized MPEG G-PCC and other prevalent learning-based schemes, but also has low complexity which is attractive to practical applications.

cs.CV↗