Non-linear model reduction by genetic algorithms with using a system structure related fitness function

2001 ◽  
Author(s):  
Maik Buttelmann ◽  
Boris Lohmann
Keyword(s):  
Genetic Algorithms ◽  
Model Reduction ◽  
Linear Model ◽  
Fitness Function ◽  
System Structure ◽  
Non Linear

scholarly journals Design and Optimization of Slotted Block Horn Utilized in Ultrasonic Insertion Process Through Rsm-fea-ga Integration Technique

2021 ◽  
Author(s):  
Anand Kumarasamy ◽  
Elangovan Sooriyamoorthy
Keyword(s):  
Thermal Analysis ◽  
Linear Model ◽  
Fitness Function ◽  
Displacement Amplitude ◽  
Design Matrix ◽  
Element Analysis ◽  
Design And Optimization ◽  
Non Linear ◽  
Harmonic Analyses ◽  
Insertion Process

Abstract This paper investigates design and optimization of slotted block horn used in ultrasonic insertion process by integrating response surface methodology (RSM), finite element analysis (FEA) and genetic algorithm (GA). Performance and reliability of block horn depend on the uniformity of displacement amplitude developed at the output face of horn. Amplitude uniformity of horn can be improved by optimizing the design of block horn. Modal and harmonic analyses are carried out as per design matrix obtained from RSM, and then non-linear model for displacement amplitude is developed. Design optimization of block horn is performed by coupling the non-linear model with GA as fitness function. Thermal analysis is carried out to validate optimized dimensions of block horn theoretically by predicting the temperature at joint. Slotted block horn is fabricated with optimum geometry using Aluminium alloy (AA6351) and the design is validated experimentally by measuring the temperature at joint using thermocouple and Data Acquisition System (DAQ). Results of this study show that the temperature predicted from thermal analysis correlates well with temperature measured from experiments and the design of slotted block horn is validated.

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.

Non-linear model reduction of multi-scale reaction-convection processes

2004 ◽  
Vol 37 (13) ◽  
pp. 775-780
Author(s):  
M.-N. Contou-Carrere ◽  
P. Daoutidis
Keyword(s):  
Model Reduction ◽  
Linear Model ◽  
Multi Scale ◽  
Non Linear

scholarly journals Non-linear model reduction for the Navier–Stokes equations using residual DEIM method

2014 ◽  
Vol 263 ◽  
pp. 1-18 ◽  
Author(s):  
D. Xiao ◽  
F. Fang ◽  
A.G. Buchan ◽  
C.C. Pain ◽  
I.M. Navon ◽  
...  
Keyword(s):  
Model Reduction ◽  
Linear Model ◽  
Stokes Equations ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Non Linear

scholarly journals The Adjoint Petrov–Galerkin method for non-linear model reduction

2020 ◽  
Vol 365 ◽  
pp. 112991 ◽  
Author(s):  
Eric J. Parish ◽  
Christopher R. Wentland ◽  
Karthik Duraisamy
Keyword(s):  
Galerkin Method ◽  
Model Reduction ◽  
Linear Model ◽  
Non Linear
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