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Shengyuan Liu

Publications and source records attributed to Shengyuan Liu.

21 records · Page 2Linked to original sources

The ALMA-QUARKS Survey: II. the ACA 1.3 mm continuum source catalog and the assembly of dense gas in massive star-forming clumps

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($λ\sim$ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7-m data. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde (H$_2$CO) transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16-84 percentile values of 6-160 $M_{\odot}$), gravity-dominated ($M\propto R^{1.1}$) fragments within massive clumps, with supersonic turbulence ($\mathcal{M}>1$) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering the fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio ($L/M$), we find that the DGF increases significantly with $L/M$, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by dynamic global collapse process.

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Build Smart Grids on Artificial Intelligence -- A Real-world Example

Power grid data are going big with the deployment of various sensors. The big data in power grids creates huge opportunities for applying artificial intelligence technologies to improve resilience and reliability. This paper introduces multiple real-world applications based on artificial intelligence to improve power grid situational awareness and resilience. These applications include event identification, inertia estimation, event location and magnitude estimation, data authentication, control, and stability assessment. These applications are operating on a real-world system called FNET-GridEye, which is a wide-area measurement network and arguably the world-largest cyber-physical system that collects power grid big data. These applications showed much better performance compared with conventional approaches and accomplished new tasks that are impossible to realized using conventional technologies. These encouraging results demonstrate that combining power grid big data and artificial intelligence can uncover and capture the non-linear correlation between power grid data and its stabilities indices and will potentially enable many advanced applications that can significantly improve power grid resilience.

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Calculate Center-of-Inertia Frequency and System RoCoF Using PMU Data

The power system frequency is important for the system overall stability. However, there does not exist a single measurement point of the system frequency due to the distributed nature of the system inertia and the small inconsistency of different generator rotor electrical speeds in one synchronized system. This paper proposed a new approach to calculate the system center-of-inertia (COI) frequency and the rate-of-change-of-frequency (RoCoF) more accurately using PMU data at multiple locations. The COI frequency and the RoCoF value were further used to assist fast estimation of the imbalance MW amount of a frequency event. Test results using actual measurements in the U.S. Eastern Interconnection system validated the effectiveness of the proposed method.

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