用AI从气泡图生成温度分布，大幅降低模拟成本。
Decrypting the temperature field in flow boiling with latent diffusion models
- 用扩散模型将气泡分布图转为温度场，分两阶段训练。
- 低中频波数下与真实数据高度一致，高频有偏差。
- 适合需要快速建模的沸腾仿真与实验校准场景。
本文提出一种基于潜在扩散模型（LDM）的新方法，通过数值模拟生成的BubbleML数据集，将相场信息转换为对应的温度分布。该方法采用两阶段训练：先用向量量化变分自编码器（VQVAE）提取特征，再用去噪自编码器重建温度场。模型能有效还原界面处复杂的温度分布。谱分析显示，在低到中波数范围与真实数据高度吻合，但在高频区域存在偏差，提示未来可优化。该机器学习方法显著降低传统模拟的计算开销，并提升实验校准精度。后续工作将聚焦小尺度湍流表征能力增强及更广沸腾条件下的应用扩展。
原文摘要 · Abstract (English)
This paper presents an innovative method using Latent Diffusion Models (LDMs) to generate temperature fields from phase indicator maps. By leveraging the BubbleML dataset from numerical simulations, the LDM translates phase field data into corresponding temperature distributions through a two-stage training process involving a vector-quantized variational autoencoder (VQVAE) and a denoising autoencoder. The resulting model effectively reconstructs complex temperature fields at interfaces. Spectral analysis indicates a high degree of agreement with ground truth data in the low to mid wavenumber ranges, even though some inconsistencies are observed at higher wavenumbers, suggesting areas for further enhancement. This machine learning approach significantly reduces the computational burden of traditional simulations and improves the precision of experimental calibration methods. Future work will focus on refining the model's ability to represent small-scale turbulence and expanding its applicability to a broader range of boiling conditions.
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