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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

COMPUTATIONAL & APPLIED MATHEMATICS

ISSN

2238-3603

e-ISSN

1807-0302

Svazek periodika

42

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

31

Strana od-do

—

Kód UT WoS článku

000910045700001

EID výsledku v databázi Scopus

2-s2.0-85145777715