No abstract

Previous studies have shown that the flow in porous media can be affected by the structure of microchannels. In this paper, the capillary bundle model is used to simplify the complex and irregular structure of porous media, which is assumed to be comprised of tortuous capillaries covered with statistical self-similar conical rough elements. Considering the boundary layer effect, a fractal geometry-based quantitative model has been proposed to investigate the relationship between the hydraulic roughness of capillaries and the micro-flow properties. According to the proposed model, the influence of roughened surfaces on the tortuosity of Stokes flow can be considered as the combination of every individual rough element in 2D flow fields, which is extended to 3D flow fields based on a series of assumptions and approximations. Analytical expressions of tortuosity and permeability of Stokes flow through roughened capillaries are derived. According to the results, the tortuosity of capillary flow increases with a higher relative roughness, while the permeability is inversely proportional to it. Predictions of permeability by the present model are compared with the previous models and experiment data, which show good agreement.

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l = 40 ⋅ ° 7 to 43 ⋅ ° 3 and b = − 2 ⋅ ° 3 to 0 ⋅ ° 3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.

In this study, we analyzed the optical observations of a subluminous Type Ia supernova (SN Ia) 2017fzw, which exhibited high photospheric velocity (HV) at B-band maximum light. The absolute B-band peak magnitude was determined to be MmaxB=−18.65±0.13 mag, similar to 91bg-like SNe Ia. An estimation of the rate of decline for the B-band light curve was determined to be Δm15(B)=1.60±0.06 mag. The spectra of SN 2017fzw were similar to those of 91bg-like SNe Ia, with prominent Ti ii and Si ii λ5972 features at early phases, gradually transitioning to spectra resembling normal (mainly HV subclass) SNe Ia at later phases, with a stronger Ca ii NIR feature. Notably, throughout all phases of observation, SN 2017fzw displayed spectral evolution characteristics that were comparable to those of HV SNe Ia, and at peak brightness, the Si ii λ6355 velocity was determined to be 13,800 ± 415 km s−1 and a more pronounced Ca ii NIR feature was also detected. Based on these findings, we classify SN 2017fzw as a transitional object with properties of both normal and 91bg-like SNe Ia, providing support for the hypothesis of a continuous distribution of supernovae between these two groups.