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2021 | OriginalPaper | Buchkapitel

Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process

verfasst von : Yuliia Viazovychenko, Oleksiy Larin

Erschienen in: Integrated Computer Technologies in Mechanical Engineering - 2020

Verlag: Springer International Publishing

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Abstract

The search for parameters is carried out using stochastic optimization algorithms, namely, by the double annealing method. Self-heating simulation is based on finite element model of direct simulation of stationary heating of the specimen. The criterion for convergence of the optimization process is the data on the heating temperature obtained from field experiments. The verification of the reliability of the results is carried out by comparing the data on the area of the hysteresis loop obtained by numerical calculation and from a full-scale experiment. The paper presents the results of identification of the parameters of calculations of the thermal state, as well as the process of convergence of the search algorithm.

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Vorheriges Kapitel 3D-Modeling of the Dynamics of Real Processes of Different Nature

Nächstes Kapitel Comparison of Metoheuristic Search Methods for the Task of Choosing a Rational Set of Measures to Risks’ Respond

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Titel: Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process
verfasst von: Yuliia Viazovychenko
Oleksiy Larin
Verlag: Springer International Publishing
Buch: Integrated Computer Technologies in Mechanical Engineering - 2020
Print ISBN: 978-3-030-66716-0

Electronic ISBN: 978-3-030-66717-7

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-66717-7_55

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