• Nuclear Engineering and Technology
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• 제55권7호
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• pp.2395-2406
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• 2023

To study the influence of parameter uncertainty in small pressurized water reactor (SPWR) once-through steam generator (OTSG), the nonlinear mathematical model of the SPWR is firstly established. Including the reactor core model, the OTSG model and the pressurizer model. Secondly, a control strategy that both the reactor core coolant average temperature and the secondary-side outlet pressure of the OTSG are constant is adopted. Then, the uncertainty quantification method is established based on Latin hypercube sampling and statistical method. On this basis, the quantitative platform for parameter uncertainty of the OTSG is developed. Finally, taking the uncertainty in primary-side flowrate of the OTSG as an example, the platform application work is carried out under the variable load in SPWR and step disturbance of secondary-side flowrate of the OTSG. The results show that the maximum uncertainty in the critical output parameters is acceptable for SPWR.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3449-3459
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• 2021

With the increasing availability of high-performance computing (HPC) platforms, uncertainty quantification (UQ) and sensitivity analyses (SA) can be efficiently leveraged to optimize design parameters of complex engineering problems using modeling and simulation tools. The workflow involved in such studies heavily relies on HPC resources and hence requires pre-processing and post-processing capabilities of large amounts of data along with remote submission capabilities. The NEAMS Workbench addresses all aspects of the workflows involved in these studies by relying on a user-friendly graphical user interface and a python application program interface. This paper highlights the NEAMS Workbench capabilities by presenting a semiautomated coupling scheme between Dakota and any given package integrated with the NEAMS Workbench, yielding a simplified workflow for users. This new capability is demonstrated by running a SA of a turbulent flow in a pipe using the open-source Nek5000 CFD code. A total of 54 jobs were run on a HPC platform using the remote capabilities of the NEAMS Workbench. The results demonstrate that the semiautomated coupling scheme involving Dakota can be efficiently used for UQ and SA while keeping scripting tasks to a minimum for users. All input and output files used in this work are available in https://code.ornl.gov/neams-workbench/dakota-nek5000-study.