| Autor: |
Linying Li, Lanqi Zhang, Bin Zhang, Hong Liu, Zhonghua Zheng |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Thermofluids, Vol 18, Iss , Pp 100351- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-2027 |
| DOI: |
10.1016/j.ijft.2023.100351 |
| Popis: |
Engine design heavily relies on numerical simulation of engine environments, which greatly accelerates the design iteration and increases efficiency. Nonetheless, for a long time, the uncertainty associated with numerical modeling has been overlooked due to resource constraints. The advent of increased computing power in recent years has enabled designers to consider the critical importance of uncertainty quantification(UQ) for developing more robust and reliable designs. UQ methods can be broadly classified into uncertainty propagation and inverse problem. Given the central role of uncertainty propagation in the inverse problem, this review focuses on the development of propagation methods and categorizes them into three groups based on the representation of uncertainty. Bayesian methods are also mentioned to provide a complete picture of UQ. The review highlights the successful applications of UQ in simulating chemical kinetics, turbulence, and scramjet, and outlines potential UQ techniques for practically simulating high-dimensional, strong-nonlinear engine combustion processes in the future. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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