🤖 AI Summary
This work addresses the long-standing challenge in multiphase transport and thermal systems research—namely, fragmented data and non-reusable raw instrument files that hinder reproducibility and benchmarking. To overcome this, the authors propose the S+TD spatiotemporal dimensional classification framework and establish an open-source ecosystem that integrates multimodal data, including boiling imagery, high-speed video, infrared thermography, and CFD field outputs, into the publicly available NED3 dataset. Complementing this data infrastructure, they develop a suite of tools—SeqReg for sequence regression, BubbleID for bubble identification, CFDTwin for digital twin modeling, and IRISApp for thermal analysis—to support computer vision, acoustic decoding, and surrogate modeling. The platform enables applications such as non-intrusive heat flux estimation and establishes a reproducible, interoperable paradigm for AI-driven thermal-fluid research.
📝 Abstract
Data-driven modeling is becoming central to multiphase transport, electronics cooling, acoustic diagnostics, and thermal-fluid digital twins, but progress is limited by fragmented datasets and raw instrument files that are difficult to decode, reuse, or benchmark. This paper presents an open ecosystem of multimodal datasets and open-source software packages developed by the Nano Energy and Data-Driven Discovery (NED3) Laboratory for reproducible AI-enabled thermal-fluid research. We introduce a spatial-plus-temporal dimensionality framework, denoted S+TD, to classify datasets by the dimensionality of measured or simulated fields, including 0+0D point values, 0+1D time series, 1+0D profiles, 2+0D images, 2+1D videos, 3+0D volumetric fields, and multimodal combinations. We organize public NED3 datasets spanning boiling images, acoustic and thermal measurements, high-speed videos, infrared thermography, thermal-resistance measurements, CFD-generated fields, design files, and acoustic-emission data. We also describe complementary software packages, including BubbleID, SeqReg, CFDTwin, IRISApp, decode-wfs, AELab, and FlowLab, which support computer vision, sequence regression, surrogate modeling, infrared analysis, waveform decoding, acoustic-emission analysis, and multimodal diagnostics. Particular emphasis is placed on SeqReg, a general sequence-regression library for 0+1D, 1+1D, and 2+1D data, with applications such as nonintrusive heat-flux estimation. Finally, we discuss future community efforts to build interoperable thermal-fluid databanks and curated AI/ML tool libraries that connect datasets, metadata, decoders, baselines, benchmarks, and physically interpretable models.