Search NASA⌕ Search

Engineering topics

Cao, Chen

Publications and source records attributed to Cao, Chen.

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Close Major-merger Pairs at z = 0: Bulge-to-total Ratio and Star Formation Enhancement

We present a study of the bulge-to-total ratio (B/T) of a Ks-band-selected sample of 88 close major-merger pairs of galaxies (H-KPAIR), based on two-dimensional decompositions of SDSS r-band images with galfit. We investigate the dependence of the interaction-induced specific star formation rate enhancement (sSFR enh ) on the B/T ratio, and the effects of this dependence on the differences between star-forming galaxies (SFGs) in spiral+spiral (S+S) and spiral+elliptical (S+E) pairs. Of all the 132 spiral galaxies in H-KPAIR, the 44 in S+E pairs show higher B/T than those in the 44 S+S pairs, with means of B/T = 0.35 ± 0.05 and B/T = 0.26 ± 0.03, respectively. Furthermore, there is a strong negative dependence of sSFR enh on the B/T ratio, and only paired SFGs with B/T < 0.3 show significant (>5σ) enhancement. Paired SFGs in S+S pairs show a similar trend, and many disky SFGs (B/T < 0.1) in S+S pairs have strong sSFR enhancements (sSFR enh > 0.7 dex). For SFGs in S+E pairs, the sSFR has no clear B/T dependence, nor any significant enhancement in any B/T bin. Disky SFGs in S+S pairs show significant (>4σ) enhancement in molecular gas content (${M}_{{{\rm{H}}}_{2}}/{M}_{\mathrm{star}}$), while SFGs in S+E pairs have no such enhancement in any B/T bin. No significant enhancement of the total gas content (Mgas/Mstar) is found in any B/T bin for paired galaxies. The star formation efficiency of either the total gas (SFE gas = SFR/M gas ) or molecular gas (${\mathrm{SFE}}_{{{\rm{H}}}_{2}}=\mathrm{SFR}/{M}_{{{\rm{H}}}_{2}}$) does not depend on the B/T ratio. The only significant (> 4σ) SFE enhancement found for paired SFGs is the SFE gas for disky SFGs in S+S pairs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.

58 GEOSCIENCES↗