Damage modelling in metal forming problems using an implicit non-local gradient model

2006 ◽  
Vol 195 (48-49) ◽  
pp. 6646-6660 ◽  
Author(s):  
J.M.A. César de Sá ◽  
P.M.A. Areias ◽  
Cai Zheng
2012 ◽  
Vol 140 (11) ◽  
pp. 3682-3698 ◽  
Author(s):  
Chin-Hoh Moeng ◽  
Akio Arakawa

Abstract One of the important roles of the PBL is to transport moisture from the surface to the cloud layer. However, how this transport process can be accounted for in cloud-resolving models (CRMs) is not sufficiently clear and has rarely been examined. A typical CRM can resolve the bulk feature of large convection systems but not the small-scale convection and turbulence motions that carry a large portion of the moisture fluxes. This study uses a large-eddy simulation of a tropical deep-convection system as a benchmark to examine the subgrid-scale (SGS) moisture transport into a cloud system. It is shown that most of the PBL moisture transport to the cloud layer occurs in the areas under low-level updrafts, with rain, or under cloudy skies, although these PBL regimes may cover only a small fraction of the entire cloud-system domain. To develop SGS parameterizations to represent the spatial distribution of this moisture transport in CRMs, three models are proposed and tested. An updraft–downdraft model performs exceptionally well, while a statistical-closure model and a local-gradient model are less satisfactory but still perform adequately. Each of these models, however, has its own closure issues to be addressed. The updraft–downdraft model requires a scheme to estimate the mean SGS updraft–downdraft properties, the statistical-closure model needs a scheme to predict both SGS vertical-velocity and moisture variances, while the local-gradient model requires estimation of the SGS vertical-velocity variance.


2003 ◽  
Vol 56 (14) ◽  
pp. 2039-2068 ◽  
Author(s):  
M. G. D. Geers ◽  
R. L. J. M. Ubachs ◽  
R. A. B. Engelen

Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.


PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Fabian Guhr ◽  
Franz‐Joseph Barthold ◽  
Andreas Menzel ◽  
Leon Sprave ◽  
Jan Liedmann

2011 ◽  
Vol 135-136 ◽  
pp. 637-642
Author(s):  
Jia Li ◽  
Juan Chang ◽  
Han Lin Qin

Structured clouds and ground building background suppression are difficult problems for dim and small target detection technique. In this paper, the dim and small target background suppression method based on combined curvelet transform with modified non-local means filter was presented to solve the problem. And local gradient statistics is introduced to boost ability of method which suppresses false by background structure. The innovation was that the curvelet transform was adopted to decompose the input infrared image, which extracts multi-scale and directional detail features of the image. Moreover non-local means filter improved by local gradient characteristic was introduced to suppress background details and enhance target information for suppression background. Compared with two-dimensional least mean square (TDLMS) and modified partial differential equation (MPDE) methods, through visual quality and value index, several groups of experimental results demonstrate that the presented method can suppress complicated background in dim and small target image effectively.


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