Decrypting the temperature field in flow boiling with latent diffusion models

TL;DR

This paper introduces a novel machine learning approach using Latent Diffusion Models to efficiently generate detailed temperature fields in flow boiling, reducing computational costs and enhancing accuracy.

Contribution

The study develops a two-stage LDM framework combining VQVAE and denoising autoencoders to accurately reconstruct temperature fields from phase data in boiling simulations.

Findings

High agreement with ground truth in low to mid wavenumber ranges

Effective reduction in computational burden

Potential for improved experimental calibration

Abstract

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.