| Autor: |
Kumar, Dinesh, Ahmed, Farid, Usman, Shoaib, Alajo, Ayodeji, Alam, Syed |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI, 2022 |
| Druh dokumentu: |
Working Paper |
| Popis: |
In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the robustness of engineering designs. This chapter aims to detail recent advances in popular uncertainty quantification methods used in engineering applications. This chapter describes the two most popular meta-modeling methods for uncertainty quantification suitable for engineering applications (Polynomial Chaos Method and Gaussian Process). Further, the UQ methods are applied to an engineering test problem under multiple uncertainties. The test problem considered here is a supersonic nozzle under operational uncertainties. For the deterministic solution, an open-source computational fluid dynamics (CFD) solver SU2 is used. The UQ methods are developed in Matlab and are further combined with SU2 for the uncertainty and sensitivity estimates. The results are presented in terms of the mean and standard deviation of the output quantities. |
| Databáze: |
arXiv |
| Externí odkaz: |
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