Thermal Power Prediction of Nuclear Reactor Core based on LSTM

Zhang, Aoxin; Teng, Jing; Ju, Yun; Zhou, Rong

doi:10.1109/cac48633.2019.8997323

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Additionally, neural networks are instrumental in establishing predictive models to forecast the reaction process and outcomes of fusion energy. For example, long short-term memory networks can be utilized to develop predictive models for forecasting the energy output and reaction speed of fusion energy [19][20][21][22][23][24][25]. Moreover, neural networks play a pivotal role in anomaly detection and fault diagnosis within fusion energy devices.…”

Section: Neural Network and Nuclear Fusionmentioning

confidence: 99%

Static performance prediction of long-pulse negative ion based neutral beam injection experiment

Li,

Hu,

Zhao

et al. 2024

Plasma Phys. Control. Fusion

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The mission of Negative Ion-Based Neutral Beam Injection (NNBI) is to conduct experiments with pulses lasting thousands of seconds. It is crucial to develop a simplified physical calculation model for the long-pulse negative ion source in the current NNBI device. This model will be used to evaluate the advantages and disadvantages of the selected parameters prior to the experiment, and to assist in adjusting and establishing the experimental parameters for the long-pulse ion source experiment. This paper presents the development of a static performance prediction model using a Back Propagation (BP) neural network. The model assesses the yield of negative hydrogen ions and the quantity of electrons in the ion source under specific parameter conditions, utilizing various experimental parameters as input. The experimental data used for this model are derived from historical data generated during the operation of the 2022 NNBI experiment. The test results indicate that under the current optimal hyperparameter condition, the prediction accuracy of I_H- is 80.84%, and the prediction accuracy of I_EG is 77.57%. This can effectively prevent invalid shots, accurately assess the advantages and disadvantages of the input parameters, and enhance the performance of the long-pulse NNBI device.

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