Multi-Objective Reliability-Based Design Optimization using Subset Simulation Enhanced by Meta-Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F16%3A00301197" target="_blank" >RIV/68407700:21110/16:00301197 - isvavai.cz</a>

Výsledek na webu

<a href="http://rec2016.rub.de/papers.html" target="_blank" >http://rec2016.rub.de/papers.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Reliability-Based Design Optimization using Subset Simulation Enhanced by Meta-Models

Popis výsledku v původním jazyce

This paper deals with double-looped reliability-based design optimization, where the reduction of the computational effort is made by utilizing subset simulation using local meta-models, namely radial basis function model, that are assembled from an adaptively updated Design of Experiment (DoE). Since the optimized design variables change with every optimization iteration and a meta-model is utilized for a reliability assessment, the meta-model is trained only in the vicinity of the relevant design variable which makes the meta-model computationally faster and more precise. DoE is updated by selected points from subset simulation samples with respect to two criteria: first, beneficial samples are located in the vicinity of the limit state, second, these samples should also be placed in the sparsest position of the DoE.

Název v anglickém jazyce

Multi-Objective Reliability-Based Design Optimization using Subset Simulation Enhanced by Meta-Models

Popis výsledku anglicky

This paper deals with double-looped reliability-based design optimization, where the reduction of the computational effort is made by utilizing subset simulation using local meta-models, namely radial basis function model, that are assembled from an adaptively updated Design of Experiment (DoE). Since the optimized design variables change with every optimization iteration and a meta-model is utilized for a reliability assessment, the meta-model is trained only in the vicinity of the relevant design variable which makes the meta-model computationally faster and more precise. DoE is updated by selected points from subset simulation samples with respect to two criteria: first, beneficial samples are located in the vicinity of the limit state, second, these samples should also be placed in the sparsest position of the DoE.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-07299S" target="_blank" >GA15-07299S: Numerické nástroje pro návrh robustních a optimalizovaných experimentů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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