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| Description
| - *This report describes a methodology for the WP3 - Geometry optimization. The deterministic optimization procedures have been already described in reports of WP1 and WP2. Therefore, Robust/Reliability (Design) Optimization methodologies are investigated in this work. In other words, the inclusion of probabilistic descriptions of Noise parametersare presented. Reliability-based design optimization (RBDO) is a research area that tries to optimize structures under assumption of uncertainties. Usually, the objective function (e.g. a structure weight, a maximal displacement etc.) is to be minimized with respect to constraints in which the probabilistic approach is included. It is hard or nearly impossible to create an analytical probabilistic approach on real structures thus some alternative method should be used. Our solution utilizes a surrogate-based Monte Carlo approach which is enhanced by an adaptive Design of (Computer) Experiments (DoE). As the model is enumerated many times, it is appropriate to use some meta-model (surrogate) that is easier to solve and give the approximation for the response on the original model. As presented in reports from WP2, the Radial Basis Function approximation shows some drawbacks, therefore, Kriging meta-model is applied in this work. To improve the quality of the surrogate, an adaptive multi-objective updating procedure is proposed, see again reports in WP2. The final Reliability Based Design Optimization (RBDO) problem consists of the minimization of the weight of the structure as the first objective and minimization of the probability of failure characterized by a reliability index as the second objective. The latter is evaluated by a new method called Asymptotic Sampling that minimizes the need for the sampling of the previously updated surrogate model. And again, this problem is multi-objective and already presented NSGA-II algorithm is used.
- *This report describes a methodology for the WP3 - Geometry optimization. The deterministic optimization procedures have been already described in reports of WP1 and WP2. Therefore, Robust/Reliability (Design) Optimization methodologies are investigated in this work. In other words, the inclusion of probabilistic descriptions of Noise parametersare presented. Reliability-based design optimization (RBDO) is a research area that tries to optimize structures under assumption of uncertainties. Usually, the objective function (e.g. a structure weight, a maximal displacement etc.) is to be minimized with respect to constraints in which the probabilistic approach is included. It is hard or nearly impossible to create an analytical probabilistic approach on real structures thus some alternative method should be used. Our solution utilizes a surrogate-based Monte Carlo approach which is enhanced by an adaptive Design of (Computer) Experiments (DoE). As the model is enumerated many times, it is appropriate to use some meta-model (surrogate) that is easier to solve and give the approximation for the response on the original model. As presented in reports from WP2, the Radial Basis Function approximation shows some drawbacks, therefore, Kriging meta-model is applied in this work. To improve the quality of the surrogate, an adaptive multi-objective updating procedure is proposed, see again reports in WP2. The final Reliability Based Design Optimization (RBDO) problem consists of the minimization of the weight of the structure as the first objective and minimization of the probability of failure characterized by a reliability index as the second objective. The latter is evaluated by a new method called Asymptotic Sampling that minimizes the need for the sampling of the previously updated surrogate model. And again, this problem is multi-objective and already presented NSGA-II algorithm is used. (en)
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| Title
| - *Geometry optimization
- *Geometry optimization (en)
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| skos:prefLabel
| - *Geometry optimization
- *Geometry optimization (en)
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| skos:notation
| - RIV/68407700:21110/14:00228478!RIV15-MSM-21110___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21110/14:00228478
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - optimization; reliability index; probability of failure; surrogates; meta-modeling; adaptive samplig; design of experiments (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...telVyzkumneZpravy
| - Astrium GmbH, Business Unit Space Transportation
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Lepš, Matěj
- Pospíšilová, Adéla
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| number of pages
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| http://localhost/t...ganizacniJednotka
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