Bubbleformer: Forecasting Boiling with Transformers

TL;DR

Bubbleformer is a transformer-based model that accurately forecasts complex boiling dynamics, including nucleation and heat transfer, without needing future input data, and generalizes across various fluids and conditions.

Contribution

The paper introduces Bubbleformer, a novel transformer model that predicts boiling behavior autonomously and generalizes across different fluids, geometries, and operating conditions.

Findings

Sets new benchmarks in boiling flow prediction.

Accurately models nucleation and interface evolution.

Generalizes across diverse fluids and conditions.

Abstract

Modeling boiling (an inherently chaotic, multiphase process central to energy and thermal systems) remains a significant challenge for neural PDE surrogates. Existing models require future input (e.g., bubble positions) during inference because they fail to learn nucleation from past states, limiting their ability to autonomously forecast boiling dynamics. They also fail to model flow boiling velocity fields, where sharp interface-momentum coupling demands long-range and directional inductive biases. We introduce Bubbleformer, a transformer-based spatiotemporal model that forecasts stable and long-range boiling dynamics including nucleation, interface evolution, and heat transfer without dependence on simulation data during inference. Bubbleformer integrates factorized axial attention, frequency-aware scaling, and conditions on thermophysical parameters to generalize across fluids, geometries, and operating conditions. To evaluate physical fidelity in chaotic systems, we propose interpretable physics-based metrics that evaluate heat-flux consistency, interface geometry, and mass conservation. We also release BubbleML 2.0, a high-fidelity dataset that spans diverse working fluids (cryogens, refrigerants, dielectrics), boiling configurations (pool and flow boiling), flow regimes (bubbly, slug, annular), and boundary conditions. Bubbleformer sets new benchmark results in both prediction and forecasting of two-phase boiling flows.