Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00563675" target="_blank" >RIV/60162694:G43__/26:00563675 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0263224124017020" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0263224124017020</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2024.115817" target="_blank" >10.1016/j.measurement.2024.115817</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research

Popis výsledku v původním jazyce

In case of classic thin-walled energy absorber the energy dissipation is not sufficiently controlled during a collision. A significant part of the columns does not participate in plastic deformation sufficiently. This is due to a lack of proper crush initiators for the formation of subsequent joints. The research problem of the article is to presents a method of applying artificial intelligence methods for determining the optimal parameters of crush initiators, in order to make proper use of plastic deformation zones and improve the efficiency of energy absorption. The methodology is based on the artificial neural network models made possible to select the best and optimal design parameters of multilevel crush initiators. The results of numerical tests were verified on the test bench. The values of all crashworthiness indicators improved, the PCF has been reduced up to 30% from for certain geometric parameters of the multilevel crush initiator.

Název v anglickém jazyce

Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research

Popis výsledku anglicky

In case of classic thin-walled energy absorber the energy dissipation is not sufficiently controlled during a collision. A significant part of the columns does not participate in plastic deformation sufficiently. This is due to a lack of proper crush initiators for the formation of subsequent joints. The research problem of the article is to presents a method of applying artificial intelligence methods for determining the optimal parameters of crush initiators, in order to make proper use of plastic deformation zones and improve the efficiency of energy absorption. The methodology is based on the artificial neural network models made possible to select the best and optimal design parameters of multilevel crush initiators. The results of numerical tests were verified on the test bench. The values of all crashworthiness indicators improved, the PCF has been reduced up to 30% from for certain geometric parameters of the multilevel crush initiator.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

MEASUREMENT

ISSN

0263-2241

e-ISSN

1873-412X

Svazek periodika

242

Číslo periodika v rámci svazku

115817

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

115817

Kód UT WoS článku

001328199700001

EID výsledku v databázi Scopus

2-s2.0-85204953090