Presentation Information
[2C19]VoidGAN: Void Fraction Signal Generative Adversarial Networks for Nuclear Reactor Thermal Hydraulics
*HANYU WANG1, Shuichiro Miwa1, Koji Okamoto1 (1. The University of Tokyo)
Keywords:
Two-phase flow,Time-series signal generation,Generative adversarial network,Thermo-hydraulics modeling
High-fidelity void-fraction signals constitute essential data for modeling two-phase flows. However, the scarcity of such data constrains the development and validation of high-accuracy models, impeding the design and optimization of nuclear energy systems. This study proposes a novel database enhancement framework, termed VoidGAN, based on conditional generative adversarial networks (GANs). The proposed model integrates Transformer modules with multi-scale convolutional Inception blocks, enabling it to capture both long-term temporal dependencies and local, irregular fluctuations. A comprehensive multi-step validation framework is further established to rigorously assess the reliability of the generated data, encompassing direct comparisons with testing datasets and benchmarking against established mechanistic models, including the two-group drift-flux model and the two-phase flow-induced vibration (TP-FIV) excited force model. This work provides a new perspective for mitigating data scarcity issues in two-phase flow modeling and paves the way for more efficient design and optimization of nuclear reactors.