Parameter estimation with the Markov Chain Monte Carlo method aided by evolutionary neural networks in a water hammer model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00374019" target="_blank" >RIV/68407700:21220/23:00374019 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s40314-022-02162-0" target="_blank" >https://doi.org/10.1007/s40314-022-02162-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40314-022-02162-0" target="_blank" >10.1007/s40314-022-02162-0</a>

Result language

angličtina

Original language name

Parameter estimation with the Markov Chain Monte Carlo method aided by evolutionary neural networks in a water hammer model

Original language description

Fast transients in pumps and valves may induce significant variations in pressures and flow rates throughout pipelines that can even cause structural damages. This computational work deals with parameter estimation of a water hammer model, with focus on a transient friction coefficient and an empirical parameter related to the pipeline elasticity. The hyperbolic water hammer model was solved with a total variation diminishing version of the weighted average flux finite volume scheme. Simulated pressure and flow rate measurements taken near the inlet and the outlet of the pipeline were used for the solution of the parameter estimation problem with Markov Chain Monte Carlo methods. This work aimed at the reduction of the computational time of the inverse problem solution with two different strategies: (1) the application of a recent parallel computation version of the Metropolis-Hastings algorithm; and (2) the use of a machine learning metamodel obtained with the evolutionary neural network algorithm and the approximation error model approach. These two approaches are compared in terms of the parameter estimation accuracies and associated computational times.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20302 - Applied mechanics

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTATIONAL & APPLIED MATHEMATICS

ISSN

2238-3603

e-ISSN

1807-0302

Volume of the periodical

42

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

31

Pages from-to

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UT code for WoS article

000910045700001

EID of the result in the Scopus database

2-s2.0-85145777715