journal article·2025-03Open access

Structural reliability assessment under creep-fatigue considering multiple uncertainty sources based on surrogate modeling approach

Yuan-Ze Tang, Xian-Cheng Zhang, Hang-Hang Gu, Kai-Shang Li, Chang-Qi Hong, Shan-Tung Tu, Yutaka S. Sato, Run-Zi Wang

Abstract

• Hybrid-driven paradigm with integration of mechanics, physics and informatics. • Achieving engineering applications from specimen-level to component-level. • Development of Abaqus plug-in for structural creep-fatigue reliability assessment. • Consideration of multiple uncertainty sources: material, model, geometry, and load. Creep-fatigue reliability assessment for high-temperature equipment is crucial but challenging due to the extensive data requirements and cumbersome methods. To enhance the implementation of creep-fatigue reliability assessment within engineering practice, this study employs multidimensional computational techniques grounded in the hybrid-driven paradigm. In detail, it presents a hybrid-driven creep-fatigue reliability assessment method integrating principles from mechanics, physics, and informatics and develops an integrated plug-in embedded in Abaqus software. The plug-in automates the implementation of parametric finite element analysis rooted in engineering damage mechanics, accommodating multiple uncertainty sources such as material properties, model parameters, geometry features, and applied loads. In particular, creep-fatigue reliability assessment utilizes a time-efficient alternative, facilitated by the adoption of surrogate modeling and Monte Carlo simulation. Furthermore, two typical examples from specimen-level (hole structure simulation specimen) to component-level (low-pressure turbine disk) are employed to demonstrate the availability and efficiency of the method and the plug-in. The plug-in with a hybrid-driven paradigm is poised to emerge as a powerful simulation-based engineering tool, facilitating the process of reliability assessment with enhanced convenience.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Fatigue and fracture mechanics · Probabilistic and Robust Engineering Design · High Temperature Alloys and Creep

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (52)

Show source-provided references
1. A generalized strain energy density exhaustion model allowing for compressive hold effect10.1016/j.ijfatigue.2017.07.008 ↗
2. Nucleation and Growth of Cracks and Cavities under Creep-Fatigue Interaction10.1016/0079-6425(93)90006-7 ↗
3. High temperature fatigue and creep-fatigue behaviors in a Ni-based superalloy: Damage mechanisms and life assessment10.1016/j.ijfatigue.2018.05.008 ↗
4. Creep-fatigue interaction of soft adhesive under shear loading: Damage diagram and life prediction model10.1016/j.ijfatigue.2024.108490 ↗
5. Phase-field modeling of crack growth under coupled creep-fatigue10.1016/j.ijfatigue.2024.108577 ↗
6. Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks10.1016/j.ijfatigue.2020.105912 ↗
7. Multi-Surrogate Collaboration Approach for Creep-Fatigue Reliability Assessment of Turbine Rotor10.1109/ACCESS.2020.2975316 ↗
8. Dynamic surrogate modeling approach for probabilistic creep-fatigue life evaluation of turbine disks10.1016/j.ast.2019.105439 ↗
9. Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties10.1016/j.ress.2023.109616 ↗
10. Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines10.1016/j.ress.2023.109199 ↗
11. Creep-fatigue reliability assessment for high-temperature components fusing on-line monitoring data and physics-of-failure by engineering damage mechanics approach10.1016/j.ijfatigue.2022.107481 ↗
12. Engineering damage theory: connotation, challenge and prospect, Journal of10.3901/JME.2023.16.002 ↗
13. R5 procedures for assessing structural integrity of components under creep and creep–fatigue conditions10.1179/174328006X79463 ↗
14. Comparison of elevated temperature design codes of ASME Subsection NH and RCC-MRx10.1016/j.nucengdes.2016.08.024 ↗
15. A life prediction method and damage assessment for creep-fatigue combined with high-low cyclic loading10.1016/j.ijfatigue.2022.106923 ↗
16. Evaluation of fatigue and creep-fatigue damage levels on the basis of engineering damage mechanics approach10.1016/j.ijfatigue.2022.107277 ↗
17. The energy density exhaustion method for assessing the creep-fatigue lives of specimens and components10.3184/096034013X13757890932442 ↗
18. An efficient online time-temperature-dependent creep-fatigue rainflow counting algorithm10.1016/j.ijfatigue.2018.06.037 ↗
19. Auto-CREAM: Software application for evaluation of HEP with basic and extended CREAM for PSA studies10.1016/j.ress.2023.109318 ↗
20. An Abaqus™ plug-in for the geometry generation of Representative Volume Elements with randomly distributed fibers and interphases10.1016/j.compstruct.2018.10.096 ↗
21. ABAQUS plug-in finite element tool for designing and analyzing lattice cell structures10.1016/j.advengsoft.2022.103139 ↗
22. Abaqus2Matlab: A suitable tool for finite element post-processing10.1016/j.advengsoft.2017.01.006 ↗
23. Integrated structural analysis tool using the linear matching method part 1 – Software development10.1016/j.ijpvp.2014.05.004 ↗
24. An Abaqus plugin for efficient damage initiation hotspot identification in large-scale composite structures with repeated features10.1016/j.advengsoft.2020.102964 ↗
25. An ABAQUS® plug-in for generating virtual data required for inverse analysis of unidirectional composites using artificial neural networks
26. Benchmark of a probabilistic fatigue software based on machined and as-built components manufactured in AlSi10Mg by L-PBF10.1016/j.ijfatigue.2022.107171 ↗
27. Pressure vessel nozzle local stress prediction software based on ABAQUS-machine learning10.1016/j.softx.2023.101550 ↗
28. Recent advances in reliability analysis of aeroengine rotor system: a review10.1108/IJSI-10-2021-0111 ↗
29. Establishment of unified creep–fatigue life prediction under various temperatures and investigation of failure physical mechanism for Type 304 stainless steel10.1111/ffe.13794 ↗
30. Creep-fatigue life prediction and interaction diagram in nickel-based GH4169 superalloy at 650 °C based on cycle-by-cycle concept10.1016/j.ijfatigue.2016.11.021 ↗
31. Machine learning assisted probabilistic creep-fatigue damage assessment10.1016/j.ijfatigue.2021.106677 ↗
32. New method for evaluation of the Manson–Coffin–Basquin and Ramberg–Osgood equations with respect to compatibility10.1016/j.ijfatigue.2008.01.012 ↗
33. Enhanced fatigue damage under cyclic thermo-mechanical loading at high temperature by structural creep recovery mechanism10.1016/j.ijfatigue.2018.04.014 ↗
34. Multi-axial creep-fatigue life prediction considering history-dependent damage evolution: A new numerical procedure and experimental validation10.1016/j.jmps.2019.04.016 ↗
35. Study on creep-fatigue evaluation procedures for high-chromium steels—Part I: Test results and life prediction based on measured stress relaxation10.1016/j.ijpvp.2007.11.008 ↗
36. Probabilistic framework for multiaxial LCF assessment under material variability10.1016/j.ijfatigue.2017.06.019 ↗
37. Machine learning-based methods in structural reliability analysis: A review10.1016/j.ress.2021.108223 ↗
38. Estimation of remaining fatigue life under two-step loading based on kernel-extreme learning machine10.1016/j.ijfatigue.2021.106190 ↗
39. An uncertainty-aware dynamic shape optimization framework: Gravity dam design10.1016/j.ress.2022.108402 ↗
40. Structural fatigue reliability analysis based on active learning Kriging model10.1016/j.ijfatigue.2023.107639 ↗
41. An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis10.1016/j.ress.2023.109377 ↗
42. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach10.1016/j.ijfatigue.2022.107483 ↗
43. Research on fatigue reliability assessment of engine cylinder head based on neural network10.1016/j.ijfatigue.2023.107800 ↗
44. Curved fatigue crack growth prediction under variable amplitude loading by artificial neural network10.1016/j.ijfatigue.2020.105886 ↗
45. Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine10.1016/j.ress.2023.109178 ↗
46. A parameter identification scheme of the visco-hyperelastic constitutive model of rubber-like materials based on general regression neural network
47. Machine learning assisted probabilistic creep-fatigue damage assessment10.1016/j.ijfatigue.2021.106677 ↗
48. Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction10.1016/j.ress.2022.108805 ↗
49. A coupled peridynamic and finite strip method for analysis of in-plane behaviors of plates with discontinuities10.1007/s00366-022-01665-y ↗
50. Geometric discontinuity effect on creep-fatigue behaviors in a nickel-based superalloy hole structure considering ratcheting deformation10.1016/j.ijfatigue.2023.107798 ↗
51. A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures10.1016/j.ress.2022.108523 ↗
52. Finite element simulations of creep-fatique behavior and life assessment of GH4169 alloy