Decrypting the temperature field in flow boiling with latent diffusion models
This work addresses the computational inefficiency in simulating temperature fields for flow boiling, which is important for researchers in thermal engineering, though it is incremental as it applies an existing machine learning method to a new dataset.
The paper tackles the problem of generating temperature fields from phase indicator maps in flow boiling by using Latent Diffusion Models, achieving high agreement with ground truth data in low to mid wavenumber ranges and reducing computational burden compared to traditional simulations.
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.