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October 3, 2025Open Access

Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks

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Authors

QFQianxi FuYSYoungjoon SuhXZXiaojing Zhang

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Overview

This analysis demonstrates the capability of CGAN to reconstruct temperature fields in pool boiling, suggesting advances in thermal management.

Key Points

  • The model infers temperature fields with reconstruction errors under 6%, supporting the effectiveness of data-driven approaches.
  • High-speed imaging data used for training enhances the reconstruction process, showing applications in complex flow regimes.
  • The generative adversarial network framework utilizes simulation-informed training to improve accuracy across datasets.
  • Results indicate the potential of advanced models to interpret multiphase heat transfer phenomena in thermal management systems.

Cite This Study

Fu et al. (2025) studied this question.

synapsesocial.com/papers/68e03501f0e39f13e7fa3890https://doi.org/10.48550/arxiv.2505.00823
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