A NEW REDUCTION TECHNIQUE FOR NON LINEAR THERMAL MODELS WITH CONDUCTIVE AND RADIATIVE COUPLINGS

1994 ◽  
Author(s):  
Denis Lemonnier ◽  
Hamou Sadat ◽  
Jean-Bernard Saulnier
Keyword(s):  
Reduction Technique ◽  
Thermal Models ◽  
Non Linear

scholarly journals Towards a framework for non‐linear thermal models in shell domains

2013 ◽  
Vol 23 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Francisco Chinesta ◽  
Adrien Leygue ◽  
Marianne Beringhier ◽  
Linh Tuan Nguyen ◽  
Jean‐Claude Grandidier ◽  
...  
Keyword(s):  
Thermal Models ◽  
Non Linear

A New Reduction Technique for Thermal Models with Fluid Networks

2011 ◽  
Vol 34 (7) ◽  
pp. 667-686 ◽  
Author(s):  
Stefano Zucca ◽  
Daniele Botto ◽  
Muzio Gola
Keyword(s):  
Reduction Technique ◽  
Thermal Models ◽  
Fluid Networks

An adaptive non-linear PDE-based speckle reduction technique for ultrasound images

2011 ◽  
Vol 6 (3) ◽  
pp. 304 ◽  
Author(s):  
Rajeev Srivastava ◽  
J.R.P. Gupta ◽  
Harish Parthasarathy ◽  
Subodh Srivastava
Keyword(s):  
Reduction Technique ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Linear Pde ◽  
Non Linear

Strength Reduction Technique for Non-Linear Failure Criterion

2012 ◽  
Vol 446-449 ◽  
pp. 1568-1572
Author(s):  
Li Ren ◽  
Ting Ai ◽  
Zhe Ming Zhu ◽  
Ling Zhi Xie ◽  
Ru Zhang
Keyword(s):  
Failure Criterion ◽  
Strength Reduction ◽  
Reduction Technique ◽  
Strength Reduction Technique ◽  
Non Linear

Model reduction technique for mechanical behaviour modelling: Efficiency criteria and validity domain assessment

2008 ◽  
Vol 222 (3) ◽  
pp. 493-505 ◽  
Author(s):  
K Ordaz-Hernandez ◽  
X Fischer ◽  
F Bennis
Keyword(s):  
Neural Network ◽  
Model Reduction ◽  
Linear Model ◽  
Cantilever Beam ◽  
Reduction Technique ◽  
Reduced Model ◽  
Large Displacements ◽  
Large Deflections ◽  
Non Linear ◽  
Model Reduction Technique

The current paper presents the study of a neural network-based technique used to create fast, reduced, non-linear behavioural models. The studied approach is the use of artificial neural networks (ANNs) as a model reduction technique to create more efficient models, mostly in terms of computational speed. The test case is the deformation of a cantilever beam under large deflections (geometrical non-linearity). A reduced model is created by means of a multi-layer feed-forward neural network, a type of ANN reported as ‘universal approximator’ in the literature. Then it is compared with two finite-element models: linear (inaccurate for large deflections but fast) and non-linear (accurate but slow). Under large displacements, the reduced model approximates well the non-linear model while having similar speed to the linear model. Unfortunately, the resulting model presents a shortening of its validity domain, as being incapable of approximating the deformed configuration of the cantilever beam under small displacements. In other words, the ANN-based model provides a very good compromise between accuracy and speed within its validity domain, despite the low fidelity presented: accurate for large displacements but inaccurate for small displacements.

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