Experimental Critical Heat Flux Records for Physics-Informed ML

Processed and compiled experimental records of critical heat flux and boundary conditions prepared for the journal article 'On the prediction of critical heat flux using a physics-informed machine learning-aided framework'. The dataset originates from Mendeley Data and is associated with a 2020 publication. It is licensed under CC-BY-4.0.

Use Cases

• Training machine learning models to predict critical heat flux based on experimental boundary conditions.
• Benchmarking physics-informed ML frameworks against compiled experimental records.
• Analyzing the relationship between experimental conditions and critical heat flux failure points.

Strengths

• Data is directly linked to a peer-reviewed journal article (doi: 10.1016/j.applthermaleng.2019.114540).
• Records are processed and compiled for a specific research purpose, suggesting curation.
• Available under a permissive CC-BY-4.0 license.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Mendeley Data, V1, doi: 10.17632/5p5h37tyv7.1

Collection Method

Processed and compiled from experimental records.