On Shannon entropy computations in selected plasticity problems

This paper considers the problem of determining probabilistic entropy fluctuations, which are important for understanding uncertainty propagation in mechanical systems in the elasto‐plastic regime. Probabilistic entropy is conceptualized based on an initial definition by Shannon, which demands discr...

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Published in:International journal for numerical methods in engineering Vol. 122; no. 18; pp. 5128 - 5143
Main Author: Kamiński, Marcin M.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 30.09.2021
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Subjects:
Algorithms
Aluminum
Approximation
Computational fluid dynamics
Computer algebra
Constitutive models
Entropy
Entropy (Information theory)
Finite element method
Least squares method
Mathematical models
Mechanical systems
Monte‐Carlo simulation
Numerical analysis
Numerical methods
Optimization
plasticity problems
Polynomials
probabilistic entropy
Response functions
Shannon theory
Statistical analysis
stochastic perturbation technique
Tensile stress
Uncertainty
ISSN:0029-5981, 1097-0207
Online Access:Get full text
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Summary:This paper considers the problem of determining probabilistic entropy fluctuations, which are important for understanding uncertainty propagation in mechanical systems in the elasto‐plastic regime. Probabilistic entropy is conceptualized based on an initial definition by Shannon, which demands discrete representation of the uncertainty source. Numerical analysis is performed using the Response Function Method with polynomial bases. Coefficients are found and order optimization is completed using polynomial interpolations or the Least Squares Method. Approximations are based on the Finite Element Method. Local polynomial bases enable nonlinear increment analysis, and allow for a given degree of freedom in the FEM model to be described as a function of a random input parameter. Academic FEM software and the ABAQUS system were used for numerical experiments. Polynomial approximations, probabilistic moment computations, and statistical entropy estimations were programmed in the symbolic algebra package MAPLE. Transformation of the input probability density into the output function was performed using the Monte‐Carlo simulation algorithm for statistically optimized polynomial bases of extreme displacement functions. Two computational examples are given to demonstrate probabilistic entropy fluctuations for a small statically indeterminate aluminum truss structure and also for practical engineering case study of the steel round bar under uniform tensile stress. In these examples, some material and geometrical uncertainties distributed according to Gaussian, triangular, uniform as well as lognormal distributions were analyzed. The presented approach could be used for constitutive models of solids, computational fluid dynamics, and in other discrete numerical methods.
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ISSN:0029-5981
1097-0207
DOI:10.1002/nme.6759