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, Subhasish; Jagielo, Bryan; Iverson, William; Bhan, Chi Bum; Soppet, William S.; Majumdar, Saurin M.; Natesan, K.

doi:10.2172/1168230

Cited by 4 publications

(6 citation statements)

References 10 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a DT based predictive maintenance system can have a multi-level-framework, starting with the lowest level focused on small regions of a single component to a national level center for monitoring and predicting the structural health of all the components in all the plants (in US) from a centralized location. Figure 3.1 depicts the schematic of a hierarchical system [26]. Each plant can be divided into subsystems containing components with individual sensor nodes.…”

Section: Big Picturementioning

confidence: 99%

“…Figure 3. 1 A fault tree diagram of a national level DT based online health monitoring and prediction system [26].…”

Section: Big Picturementioning

confidence: 99%

“…The predictive model needs also to predict the probabilistic states associated with material variability and other uncertain conditions. Based on the predicted states and with knowledge about the critical failure criteria the remaining life of a component can be predicted at a given time [26,27]. Figure 3.2 depicts a schematic showing the forecasted timeseries structural states based on the information from the structural state at any given time [26].…”

Section: Big Picturementioning

confidence: 99%

“…The signals are associated with the measurements made at a given instance in time. In this case, a U-bend SG tube was interrogated in real time (online) in order to monitor SCC initiation and growth [26]. Figure 3.6 shows the corresponding estimated flaw sizes (damage indices) as a function of time.…”

Section: Heterogenous Source and Timescale Of Datamentioning

confidence: 99%

See 3 more Smart Citations

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

Mohanty¹,

Vilim²

2021

View full text Add to dashboard Cite

Section: Big Picturementioning

confidence: 99%

“…Figure 3. 1 A fault tree diagram of a national level DT based online health monitoring and prediction system [26].…”

Section: Big Picturementioning

confidence: 99%

Section: Big Picturementioning

confidence: 99%

Section: Heterogenous Source and Timescale Of Datamentioning

confidence: 99%

See 2 more Smart Citations

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

Mohanty¹,

Vilim²

2021

View full text Add to dashboard Cite

“…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

Corrosion-fatigue: a review of damage tolerance models

Larrosa

Akid

Ainsworth

2017

International Materials Reviews

104

View full text Add to dashboard Cite

The synergistic combination of mechanical fatigue stresses and environmental agents acting together can be more detrimental than that of the summation of the contributions of each mechanism acting separately. Major attempts to understand the contribution of the different agents (microstructure, chemical composition of environment, temperature, loading conditions, etc.) have been reported in the literature. Nevertheless, current knowledge is insufficient to address life estimation with a sound physical basis from the initiation of localized corrosion (such as pitting) to the estimation of crack propagation. Major simplifications and assumptions have been required in the development of life prediction methodologies. This paper reviews recent effort made by the different interested parties, both in academia and industry, in the development of corrosion fatigue lifetime prediction procedures. The paper mainly focuses on methodologies proposed in the literature for O&G, nuclear, energy generation and aerospace applications, dealing with pitting corrosion-fatigue (CF) damage in aluminium alloys, carbon and stainless steels. The transition of a pit into a small crack and its growth is influenced by interaction of the pit stress/strain concentration and the local environmental conditions, making the modelling of this stages of the utmost complexity. A major trend in the models reviewed in this paper is to simplify the analysis by assuming the pit (a volumetric defect) as a sharp crack, decouple the CF problem and account for the mechanical and environmental contributions separately. These procedures heavily rely on fitting experimental data and exhibit low generality in terms of application to varying system conditions. There is a clear opportunity in this field to develop mechanistically based methodologies, considering the inherent dependence of the damage mechanism on the interaction of environmental, metallurgical and mechanical features, allowing more realistic lifetime estimates and defect tolerance arguments, where pit-to-crack transition and small crack initiation stages pose a significant challenge.

show abstract

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

Abstract: 60439. For information about Argonne and its pioneering science and technology programs, see www.anl.gov.

Cited by 4 publications

References 10 publications

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

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

Corrosion-fatigue: a review of damage tolerance models

Contact Info

Product

Resources

About