| Autor: |
GE Aviation, Neumann Way, Don Beeson, Gene Wiggs, Liping Wang |
| Rok vydání: |
2006 |
| Předmět: |
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| Zdroj: |
11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. |
| Popis: |
The objective of this paper is to apply state -of -the -art meta -modeling techniques to achieve more efficient and robust probabilistic analy sis for challenging industrial applications with high dimensional and non -monotonic design spaces. The proposed approach enables Cumulative Distribution Function (CDF) and Probability Density Function (PDF) calculations in design spaces that are monotonic or non -monotonic and have a large number of variables (100+). The proposed method includes 1) constructing an accurate and fast running meta -model from a small number of training points; 2) applying a large number of Monte Carlo runs to the meta -model; 3) post -processing the Monte Carlo output in a special way so that accurate CDF and PDF curves and other probabilistic information are obtained. Since accurate meta -models can be constructed for design spaces that are non -monotonic or have a very large numbe r of variables (100+), this approach provides a practical general -purpose solution process that is applicable to most probabilistic design problems encountered in industry. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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