Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems
arXiv:2605.23037v1 Announce Type: new 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, deno
The increasing complexity and computational demands of thermal-fluid systems necessitate more robust and data-driven modeling approaches, which is now feasible with advancements in AI and data infrastructure.
Open multimodal datasets and software for thermal-fluid systems will accelerate AI adoption in critical physical domains, improving efficiency, reducing waste, and enabling more sophisticated engineering solutions.
The availability of standardized, open-source resources will democratize access to advanced modeling capabilities, shifting research and development from fragmented efforts to collaborative, benchmarked progress.
- · AI/ML researchers
- · Thermal-fluid engineers
- · Manufacturing sector
- · Energy efficiency companies
- · Companies relying on proprietary, closed datasets
- · Traditional simulation software vendors
Widespread adoption of data-driven models for optimizing complex physical systems like electronics cooling and multiphase transport.
Improved energy efficiency and material usage across industrial applications due to superior system design and predictive maintenance.
The development of 'digital twins' for even more complex and dynamic industrial processes, leading to new forms of automation and resource management.
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