Dynamic Strain Mapping and Real-Time Damage-State Estimation Under Random Fatigue Loading

Mohanty, Subhashish; Chattopadhyay, Aditi; Rajadas, John

doi:10.2514/1.j050820

Cited by 7 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more details it is suggested to refer to the websites [1-3] of the above-mentioned libraries and related publications such as [4,5]. Previous work related to the specific use of the AI/ML technique for time-series fatigue prediction can also be found from [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Brief Theoretical Background Of the Ai/ml/dl Techniques Usedmentioning

confidence: 99%

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Mohanty

Listwan

2020

View full text Add to dashboard Cite

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years.

show abstract

Section: Brief Theoretical Background Of the Ai/ml/dl Techniques Usedmentioning

confidence: 99%

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Mohanty

Listwan

2020

View full text Add to dashboard Cite

show abstract

“…The variability arises due to scatter in stress/strain-life curves which arise due to micro structure variability even though tests were conducted with similar materials under similar circumstances. Using Bayesian statistics based Gaussian Process (GP) probabilistic inference techniques [1,3,8,[15][16][17][18][19][20][21]; a given historical S-N data set can be mapped offline and can be used in real-time to estimate the mean N i in Eq. 3.1 and its associated confidence bound.…”

Section: Probabilistic Modeling Of Usage Factor and Remaining Useful mentioning

confidence: 99%

“…21 Strain gage sensor measurement based real time forecasted fatigue life for the in air fatigue test (F09) specimen at any given fatigue cycle.…”

mentioning

confidence: 99%

Online stress corrosion crack and fatigue usages factor monitoring and prognostics in light water reactor components: Probabilistic modeling, system identification and data fusion based big data analytics approach

Mohanty

Jagielo

Iverson

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Mohanty et al [22,23] developed a purely data-driven GP based prognosis framework, combining on-line and off-line information, for damage state estimation and RUL estimation of metallic structural hotspots under complex loading, such as random and Fighter Aircraft Loading STAndard For Fatigue (FALSTAFF) [24,25]. Mohanty et al [26] also presented a passive sensing based strain mapping approach for real-time damage state estimation under random loading. In this method, the strains are predicted at any time with new loading information and estimated reference model.…”

Section: Introductionmentioning

confidence: 99%

“…The damage states are then evaluated by comparing the predicted and actual strains via correlation analysis. Although these models [22,23,26] provide very accurate results, the accuracy of prediction is dependent on the available training data. In the initial stage, where there is less training data, the prediction is not accurate.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Prediction Using Hybrid Prognosis for Structural Health Monitoring

Neerukatti

Liu

Kovvali

et al. 2014

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

As metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper a hybrid prognosis model that accurately predicts the crack growth regime and the residual useful life (RUL) estimate of aluminum components is developed. The methodology integrates physics based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lugjoint under fatigue loading conditions. The results indicate that fusing the measured data and physics based models improves the accuracy of prediction compared to a purely data-driven or physics based approach.

show abstract

Dynamic Strain Mapping and Real-Time Damage-State Estimation Under Random Fatigue Loading

Cited by 7 publications

References 18 publications

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Online stress corrosion crack and fatigue usages factor monitoring and prognostics in light water reactor components: Probabilistic modeling, system identification and data fusion based big data analytics approach

Fatigue Life Prediction Using Hybrid Prognosis for Structural Health Monitoring

Contact Info

Product

Resources

About