Open datasets and machine learning for two-phase heat transfer: a review following a spatial-temporal taxonomy

Abstract:Two-phase heat transfer underpins boiling, condensation, immersion cooling, flow boiling, energy conversion, and electronics thermal management, but its coupled interfacial physics make data reuse and model comparison difficult. This narrative review synthesizes open datasets, machine-learning methods, and reusable software for two-phase heat-transfer research, with emphasis on boiling, multimodal sensing, and thermal-management datasets. We organize the review around a spatial-plus-temporal dimensionality taxonomy, denoted S+TD, that classifies data objects by the dimensionality of the measured, simulated, or derived fields, including 0+0D point values, 0+1D time series, 1+1D profiles, 2+0D images, 2+1D videos, 3+0D/3+1D fields, and mixed multimodal records. The taxonomy is used to connect dataset types to AI tasks such as tabular regression, acoustic sequence learning, image segmentation, video analysis, inverse heat-flux reconstruction, surrogate modeling, and multimodal fusion. The review also develops a roadmap for physics-aware open data, including metadata definitions, evidence and reuse-maturity labels, benchmark splits, decoders, baseline models, and community databanks. NED3 resources are discussed as one implementation case within a broader open-data ecosystem rather than as a complete solution. The main conclusion is that progress in two-phase AI now depends as much on findable, decodable, benchmarkable, and physically interpretable data infrastructure as on model architecture.

Accepted manuscript replacing arXiv:2605.23037 (v1). Substantially expanded from the original preprint and published in Transport Phenomena 2026, 1(3), 20260081. this https URL

Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)

arXiv:2605.23037 [cs.LG]

(or arXiv:2605.23037v2 [cs.LG] for this version)

https://doi.org/10.1515/tp-2026-0081 DOI(s) linking to related resources