Calculation of the power peaking factor in a nuclear reactor using support vector regression models

Bae, In Ho; Na, Man Gyun; Lee, Yoon Joon; Park, Goon Cherl

doi:10.1016/j.anucene.2008.09.004

Cited by 35 publications

(14 citation statements)

References 9 publications

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“…The LPD divided by the average power density in the reactor core is the power peaking factor (PPF). 20 It is calculated by multiplying the axial power peaking factor (APPF) and the radial power peaking factor (RPPF). The radial, axial and total PPFs are given in Table 4.…”

Section: Neutronic Analysismentioning

confidence: 99%

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

Shahmirzaei

Ansarifar

Koraniany

2022

Intl J of Energy Research

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Summary The reactor design includes optimizing parameters such as fuel composition. In this research, the issue of fuel composition optimization in a Small Modular Nuclear Reactor (SMR) is investigated. Indeed, considering the importance of fuel management optimization at the core of a nuclear reactor as a fundamental issue, this study analyzes gadolinium concentration effects on the neutronic and thermal hydraulic parameters in the NuScale reactor. In addition, optimizing the gadolinium concentration in fuel composition has been done via a genetic algorithm (GA) as a Machine Learning method. At first, the core of the NuScale reactor was modeled using neutronic codes (WIMS & CITATION). Then, the core of this reactor was simulated in different concentrations of natural gadolinium with 141 different concentration combinations in fuel assemblies through the mentioned neutronic codes. Furthermore, the neutronic parameters, including excess reactivity, radial and axial power peaking factors (PPFradial, PPFaxial) related to each design, were calculated. Thermal‐hydraulic modeling of the hot channel for each design created was performed using ANSYS FLUENT code, and thermal‐hydraulic parameters including heat transfer coefficient, MDNBR, pressure drop, Vout‐Vin, and Vmax/Vavg, have been obtained. An artificial neural network (ANN) was trained using the obtained data. Finally, optimal gadolinium concentrations in fuels were determined using the ANN by implementing the GA. In the core of the conventional NuScale reactor, there are assemblies with 2%, 6%, and 8% gadolinium concentrations. Via optimization algorithm, in this paper, two sets of optimal gadolinium concentrations have been presented using different appropriate cost functions. First cost function proposed the optimal concentrations as 1.4256%, 4.2606%, and 5.4968%, while, based on the second one, 1.4502%, 4.2552%, and 5.5296% are optimal concentrations. Finally, the reactor core containing the optimal fuel composition has been redesigned and compared with the conventional NuScale reactor core. Most of the neutron and thermal‐hydraulic parameters significantly improved over the NuScale reactor's optimally designed core. This optimization leads to better fuel management and safety in the optimized core than in the core of the NuScale reactor and can also economically increase power plant efficiency.

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Section: Neutronic Analysismentioning

confidence: 99%

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

Shahmirzaei

Ansarifar

Koraniany

2022

Intl J of Energy Research

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“…The power peaking factor (PPF) is defined as the highest LPD divided by the average power density in the reactor core and is one of the major concerns from safety perspective (Bae et al, 2008).…”

Section: Local Power Peaking Factorsmentioning

confidence: 99%

Design and neutronic investigation of the Nano fluids application to VVER-1000 nuclear reactor with dual cooled annular fuel

Ansarifar

Ebrahimian

2016

Annals of Nuclear Energy

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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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Calculation of the power peaking factor in a nuclear reactor using support vector regression models

Cited by 35 publications

References 9 publications

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

Design and neutronic investigation of the Nano fluids application to VVER-1000 nuclear reactor with dual cooled annular fuel

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

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