Relaxation of superflow in a network: Application to the dislocation model of supersolidity of helium crystals

AbstractThis work presents a regularized eigenstrain formulation around the slip plane of dislocations and the resultant non-singular solutions for various dislocation configurations. Moreover, we derive the generalized Eshelby stress tensor of the configurational force theory in the context of the proposed dislocation model. Based on the non-singular finite element solutions and the generalized configurational force formulation, we calculate the driving force on dislocations of various configurations, including single edge/screw dislocation, dislocation loop, interaction between a vacancy dislocation loop and an edge dislocation, as well as a dislocation cluster. The non-singular solutions and the driving force results are well benchmarked for different cases. The proposed formulation and the numerical scheme can be applied to any general dislocation configuration with complex geometry and loading conditions.

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Present-day orogenic processes in the western Kalpin nappe explored by interseismic GNSS measurements and coseismic InSAR observations of the 2020 Mw 6.1 Kalpin event

Geophysical Journal International ◽

10.1093/gji/ggab097 ◽

2021 ◽

Author(s):

Ping He ◽

Yangmao Wen ◽

Shuiping Li ◽

Kaihua Ding ◽

Zhicai Li ◽

...

Keyword(s):

Source Parameters ◽

Satellite System ◽

Tien Shan ◽

Dislocation Model ◽

Maximum Displacement ◽

Finite Fault ◽

Instantaneous Deformation ◽

Gnss Measurements ◽

Global Navigation Satellite ◽

Finite Fault Model

Summary As the largest and most active intracontinental orogenic belt on Earth, the Tien Shan (TS) is a natural laboratory for understanding the Cenozoic orogenic processes driven by the India-Asia collision. On 19 January 2020, a Mw 6.1 event stuck the Kalpin region, where the southern frontal TS interacts with the Tarim basin. To probe the local ongoing orogenic processes and potential seismic hazard in the Kalpin region, both interseismic and instantaneous deformation derived from geodetic observations are employed in this study. With the constraint of interseismic global navigation satellite system (GNSS) velocities, we estimate the décollement plane parameters of the western Kalpin nappe based on a two-dimensional dislocation model, and the results suggest that the décollement plane is nearly subhorizontal with a dip of ∼3° at a depth of 24 km. Then, we collect both Sentinel-1 and ALOS-2 satellite images to capture the coseismic displacements caused by the 2020 Kalpin event, and the interferometric synthetic aperture radar (InSAR) images show a maximum displacement of 7 cm in the line of sight near the epicentral region. With these coseismic displacement measurements, we invert the source parameters of this event using a finite-fault model. We determine the optimal source mechanism in which the fault geometry is dominated by thrust faulting with an E–W strike of 275° and a northward dip of 11.2°, and the main rupture slip is concentrated within an area 28.0 km in length and${\rm{\,\,}}$10.3 km in width, with a maximum slip of 0.3 m at a depth of 6–8 km. The total released moment of our preferred distributed slip model yields a geodetic moment of 1.59 × 1018 N$\cdot $m, equivalent to Mw 6.1. The contrast of the décollement plane depth from interseismic GNSS and the rupture depth from coseismic InSAR suggests that a compression still exists in the Kalpin nappe forefront, which is prone to frequent moderate events and may be at risk of a much more dangerous earthquake.

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Dynamic model for a water distribution network: application to leak diagnosis and quality monitoring

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.1091 ◽

2020 ◽

Vol 53 (2) ◽

pp. 16679-16684

Author(s):

J.A. Delgado-Aguiñaga ◽

F.I. Becerra-López ◽

L. Torres ◽

G. Besançon ◽

V. Puig ◽

...

Keyword(s):

Dynamic Model ◽

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Quality Monitoring ◽

Network Application

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Is designing a neural network application an art or a science?

ACM SIGCHI Bulletin ◽

10.1145/181518.181522 ◽

1994 ◽

Vol 26 (3) ◽

pp. 23-29 ◽

Cited By ~ 1

Author(s):

Roman Erenshteyn ◽

Richard Foulds ◽

Scott Galuska

Keyword(s):

Neural Network ◽

Network Application

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