Multiple time-space scale atmosphere-ocean interactions and improvement of Zebiak-Cane model

1997 ◽  
Vol 40 (6) ◽  
pp. 577-583
Author(s):  
Weihong Qian ◽  
Shaowu Wang
Keyword(s):  
Multiple Time ◽  
Space Scale ◽  
Time Space ◽  
Cane Model

A multi-time-space scale optimal operation strategy for a distributed integrated energy system

2021 ◽  
Vol 289 ◽  
pp. 116698
Author(s):  
Peng Li ◽  
Zixuan Wang ◽  
Jiahao Wang ◽  
Tianyu Guo ◽  
Yunxing Yin
Keyword(s):  
Energy System ◽  
Optimal Operation ◽  
Operation Strategy ◽  
Space Scale ◽  
Time Space ◽  
Integrated Energy System

scholarly journals Variable space scale factor spherical coordinates and time-space metric

2012 ◽  
Vol 61 (8) ◽  
pp. 080401
Author(s):  
Bian Bao-Min ◽  
Lai Xiao-Ming ◽  
Yang Ling ◽  
Li Zhen-Hua ◽  
He An-Zhi
Keyword(s):  
Scale Factor ◽  
Spherical Coordinates ◽  
Space Scale ◽  
Time Space ◽  
Variable Space

scholarly journals On Burgers equation on a time-space scale

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Gro Hovhannisyan ◽  
Logan Bonecutter ◽  
Anita Mizer
Keyword(s):  
Burgers Equation ◽  
Space Scale ◽  
Time Space

scholarly journals The Darboux Transformation and N-Soliton Solutions of Coupled Cubic-Quintic Nonlinear Schrödinger Equation on a Time-Space Scale

2021 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Huanhe Dong ◽  
Chunming Wei ◽  
Yong Zhang ◽  
Mingshuo Liu ◽  
Yong Fang
Keyword(s):  
Dynamic System ◽  
Darboux Transformation ◽  
Soliton Solutions ◽  
Einstein Condensate ◽  
Nonlinear Schrödinger ◽  
Bose Einstein Condensate ◽  
Space Scale ◽  
Time Space ◽  
Bose Einstein ◽  
Quintic Nonlinear Schrödinger Equation

The coupled cubic-quintic nonlinear Schrödinger (CQNLS) equation is a universal mathematical model describing many physical situations, such as nonlinear optics and Bose–Einstein condensate. In this paper, in order to simplify the process of similar analysis with different forms of the coupled CQNLS equation, this dynamic system is extended to a time-space scale based on the Lax pair and zero curvature equation. Furthermore, Darboux transformation of the coupled CQNLS dynamic system on a time-space scale is constructed, and the N-soliton solution is obtained. These results effectively combine the theory of differential equations with difference equations and become a bridge connecting continuous and discrete analysis.

Impact diagnosis in stiffened structural panels using a deep learning approach

2020 ◽  
pp. 147592172092504
Author(s):  
Sakib Ashraf Zargar ◽  
Fuh-Gwo Yuan
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Network Architecture ◽  
Time History ◽  
Low Velocity Impact ◽  
Multiple Time ◽  
Time Space ◽  
Spatio Temporal ◽  
Time Frames ◽  
The Impact

Low-velocity impact on a structure emanates an elastic wave that propagates through the structure carrying a wealth of information about the impact event. This propagating wave can be visualized through a series of images (time-frames in the context of computer-vision) in the time–space domain collectively referred to as the wavefield. An approach for the autonomous analysis of these wavefields is presented in this article for the purpose of impact diagnosis, that is, identifying the impact location and reconstructing the impact force time-history. The high spatio-temporal dimensionality of the wavefield mandates the use of deep neural networks for analysis; however, unlike the traditional object detection problem in computer-vision, the nature of the impact diagnosis problem requires the capturing of context from the wavefield evolution. This necessitates learning across multiple time-frames of the wavefield simultaneously rather than focusing independently on each frame. While scanning simultaneously across multiple time-frames provides indispensable information about the wave propagation phenomenon in terms of its interactions with geometric features, boundaries, and so on, it mandates the use of deep learning models that can analyze this complex phenomenon in both spatial and temporal domains. A unified CNN-RNN network architecture is employed in this article to address this issue of spatio-temporal information extraction. The proposed approach is verified using simulated wavefields obtained from the finite element analysis of a five-bay stiffened aluminum panel. In order to demonstrate the generalization capabilities of the model, simulated wavefields corresponding to highly idealized impact scenarios are used for training, whereas for testing, the ones corresponding to more realistic impacts are used. It is shown that by incorporating the physics-based concept of time-reversal in the recurrent part of the network, better network performance can be achieved. The potential extension of the proposed methodology to an end-to-end vision-based impact monitoring system is also discussed at the end.

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