Bubble2Heat: Optical to Thermal Inference in Pool Boiling Using Physics-encoded Generative AI

TL;DR

This paper introduces a deep learning framework that uses physics-encoded generative AI to infer detailed temperature fields in pool boiling experiments from limited measurements, bridging the gap between observable phenomena and thermal transport understanding.

Contribution

It presents a novel deep learning approach combining simulation-trained GANs with preprocessing to accurately generate temperature data from experimental measurements in complex boiling systems.

Findings

Framework successfully generates high-resolution temperature fields from limited data.

Preprocessing aligns simulation and experimental data, improving inference accuracy.

Data augmentation enhances physical plausibility of generated temperature fields.

Abstract

Phase change process plays a critical role in thermal management systems, yet quantitative characterization of multiphase heat transfer remains limited by the challenges of measuring temperature fields in chaotic, rapidly evolving flow regimes. While computational methods offer temperature data at a high spatiotemporal resolution in ideal cases, replicating complex experimental conditions remains prohibitively difficult. In this paper, we present a deep learning framework that can generate temperature field data at simulation resolution from segmented high-speed recordings and pointwise thermocouple readings which are typically available in a canonical pool boiling experimental configuration without requiring advanced techniques. This framework leverages a conditional generative adversarial network trained only on simulation data. To ensure direct applicability of the model to experimental data, our framework also introduces a preprocessing pipeline that aligns high resolution simulation data with experimental measurements through both conventional image processing and image segmentation with pretrained convolutional neural network. We further show that standard data augmentation strategies are effective in enhancing the physical plausibility of the inference when precise physical constraints are not applicable. Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport, offering a powerful approach to augment and interpret experimental measurements in complex two-phase systems.