Active learning Kriging approach for creep-fatigue reliability assessment of turbine disk
- 18 April 2025
- journal article
- research article
- Published by Springer Nature in Structural and Multidisciplinary Optimization
- Vol. 68 (4) , 1-22
Abstract
No abstract availableKeywords
Funding Information
- National Key R&D Program of China (2021YFB1715000)
- National Natural Science Foundation of China (52105136, 52205164, 52375139)
- Hong Kong Scholars Program (XJ2022013)
This publication has 69 references indexed in Scilit:
- AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysisQuality and Reliability Engineering International, 2024
- New learning functions for active learning Kriging reliability analysis using a probabilistic approach: KO and WKO functionsStructural and Multidisciplinary Optimization, 2023
- A unified reliability evaluation framework for aircraft turbine rotor considering multi-site failure correlationStructural and Multidisciplinary Optimization, 2023
- Small failure probability: principles, progress and perspectivesStructural and Multidisciplinary Optimization, 2022
- A novel inverse strain range-based adaptive Kriging method for analyzing the combined fatigue life reliabilityStructural and Multidisciplinary Optimization, 2021
- Support vector machine-based importance sampling for rare event estimationStructural and Multidisciplinary Optimization, 2021
- HALK: A hybrid active-learning Kriging approach and its applications for structural reliability analysisEngineering with Computers, 2021
- An active weight learning method for efficient reliability assessment with small failure probabilityStructural and Multidisciplinary Optimization, 2019
- Multi-source uncertainty considered assembly process quality control based on surrogate model and information entropyStructural and Multidisciplinary Optimization, 2018
- Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statisticsStructural and Multidisciplinary Optimization, 2018