Wall temperature prediction at critical heat flux using a machine learning model

Park, Hae Min; Lee, Jong Hyuk; Kim, Kyung Doo

doi:10.1016/j.anucene.2020.107334

Cited by 34 publications

(5 citation statements)

References 12 publications

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“…The simultaneous appearance of over-and under-prediction demonstrates that complexity of regression feature generated by SVR model is insufficient to accommodate the multiple parameters. The result is in equal context with analysis by Park and Lee [25] about prediction of pool boiling critical heat flux. Thus, it is concluded that SVR model is not suitable for regression about the quenching curve having high resolution and multiple thermal-hydraulic parameters.…”

Section: Support Vector Regression (Svr)supporting

confidence: 60%

“…The combined ANN model showed good performance for the test pseudo-data and provided parametric effect on the CHF in narrow rectangular channels under downward flow conditions. Park et al [25] reported the application of machine learning method, which can predict the CHF temperature determining heat transfer regimes of the pre-and post-CHF, for reduced calculation time. Multi-layer perceptron (MLP) neural network was built using the CHF temperature database produced by a subprogram constructed in the SPACE code [26] .…”

Section: Introductionmentioning

confidence: 99%

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High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

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Quenching heat transfer is a representative complex phenomenon in thermal-hydraulic engineering. Despite the tremendous effort s to precisely predict the quenching behavior, conventional analysis methodology and correlations have exhibited limited prediction capacity on quenching heat transfer according to axial locations of heater rods. The deviations of existing models result from the uncertainty of axial heat conduction with low-resolution temperature measurement and limited regression performance. In this study, machine learning models were trained with optical fiber temperature measurement data having high spatial resolution about quenching to overcome the intrinsic limitation of conventional prediction methods. Support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) models were trained, and the MLP model showed best prediction performance (R 2 = 0.9999 and test RMSE = 1.41 °C) due to its strong analysis of the underlying relationship between input and output with interpolation capacity. The optimal MLP model (5-30-30-30-1 architecture) showed good agreement with experimental data for bottom quenching behaviors of Inconel-600 and Monel K500, in aspects of minimum film boiling temperature, quench front propagation velocity, and transient boiling curve by providing spatially resolutive quenching behavior that cannot be obtained from conventional correlations and having high uncertainty in existing computational analysis methods. The developed MLP model has the capability to predict the quenching behavior of FeCrAl accident tolerant fuel cladding surface, and better predictability on minimum film boiling temperature (average error of 3.7%) than conventional correlation having 7.1% average error. The suggested methodology using machine learning technique will contribute to innovatively improve the predictability on quenching phenomena with reducing uncertainty.

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Section: Support Vector Regression (Svr)supporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

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“…Furthermore, the determination of the critical heat flux (CHF) is crucial for the safe operation of a water-cooled reactor. In addition to traditional empirical relations, look-up table methods, and mechanism-/phenomenon-based models, AI-based methods have been extensively explored in recent years [ [61] , [62] , [63] , [64] ]. Besides, some public CHF datasets are available for deeper investigation and optimization of AI algorithms [ 65 , 66 ].…”

Section: Application Of Ai To Nuclear Reactor Design Optimizationmentioning

confidence: 99%

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Huang

Peng

Deng

et al. 2023

Heliyon

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“…Recently, the International Atomic Energy Agency (IAEA) has urged the nuclear community to integrate ML in the industry within the framework of emerging technologies, given its superior capability in handling big-data (IAEA, 2020). In fact, the potential of using ML technology has been explored to estimate some key figures of merit such as the power pin peaking factor (Bae et al, 2008), the wall temperature at critical heat flux (Park et al, 2020), the flow pattern identification (Lin, 2020), to detect anomalies and warn of equipment failure (Ahsan and Hassan, 2013;Chen and Jahanshahi, 2018;Devereux et al, 2019); to determine core configuration and core loading pattern optimization (Siegelmann et al, 1997;Faria and Pereira, 2003;Erdogan and Gekinli, 2003;Zamer et al, 2014;Nissan, 2019), to identify initiating events and categorize accidents (Santosh et al, 2003;Na et al, 2004;Lee and Lee, 2006;Ma and Jiang, 2011;Pinheiro et al, 2020;Farber and Cole, 2020) and to determine of key performance metrics and safety parameters (Ridlluan et al, 2009;Montes et al, 2009;Farshad Faghihi and Seyed, 2011;Patra et al, 2012;Young, 2019;Park et al, 2020;Alketbi and Diab, 2021), and in radiation protection for isotope identification and classification (Keller and Kouzes, 1994;Abdel-Aal and Al-Haddad, 1997;Chen, 2009;Kamuda and Sullivan, 2019), etc. However, it is worth noting that the application of ML in nuclear safety is still limited despite its potential to enhance performance, safety, as well as economics of plant operation (Chai et al, 2003) which warrants further research (Gomez Fernandez et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

Sallehhudin

Diab

2021

Front. Energy Res.

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In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

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Wall temperature prediction at critical heat flux using a machine learning model

Cited by 34 publications

References 12 publications

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

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