| Autor: |
Xiaojian Yi, Zhezhe Wang, Shulin Liu, Xinrong Hou, Qing Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 12, Iss 18, p 9302 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app12189302 |
| Popis: |
Durability evaluation plays an important role in product operation and maintenance during the design stage. In order to ensure a long life, high reliability, and short development cycle, an accelerated degradation durability evaluation model for the turbine impeller of a turbine based on a genetic algorithms back-propagation neural network is established. Based on the proposed model, we discuss two types of practical problems. One is the matching problem of the component strengthening test and whole machine system test. The other is the design problem of two kinds of bench tests. All in all, this work not only proposes a durability evaluation model to effectively solve the current turbine durability evaluation problems, but it also provides a feasible research idea for similar problems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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