DOAJ Open Access 2024

Multivariate-coupling LOCA prediction using zLSTM

Xichen Li Xiang Chen Jingke She Yifan Zhang Taizhe Wang

Abstrak

A novel deep learning model zLSTM, which evolves from Long-Short Term Memory (LSTM) with enhanced long-term processing capability, is applied to the prediction of Loss of Coolant Accident (LOCA). During the prediction process, six-dimensional multivariate coupling is established among six major system parameters after connecting each timestep with the time dimension. The demonstration experiments show that the proposed method can increase the prediction accuracy by 35.84% comparing to the traditional LSTM baseline. Furthermore, zLSTM model follows the parameter progress well at the starting stage of LOCA, which reduces the prediction error at both the beginning and the far end.

Topik & Kata Kunci

Plasma physics. Ionized gases Nuclear and particle physics. Atomic energy. Radioactivity

Penulis (5)

Xichen Li

Xiang Chen

Jingke She

Yifan Zhang

Taizhe Wang

Format Sitasi

APA MLA BibTeX

Li, X., Chen, X., She, J., Zhang, Y., Wang, T. (2024). Multivariate-coupling LOCA prediction using zLSTM. https://doi.org/10.3389/fnuen.2024.1386540

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.3389/fnuen.2024.1386540
Akses: Open Access ✓