Abstract
We present a machine learning methodology that can be used online and quasi-real-time (i.e., as fast as we can practically run an experiment) to accelerate the analysis of infrared, boiling heat transfer investigations. Precisely, we use feed-forward artificial neural networks with one layer of hidden neurons to measure bubble growth time, bubble period, and nucleation site density directly from the radiation recorded by the high-speed infrared camera. We test and validate the methodology against saturated pool boiling experiments with water, run on both plain and nanoengineered surfaces. Using such a technique, we have measurements of the quantities above within a few seconds from the moment the camera records the boiling surface radiation, with a regression coefficient of 0.95 or higher compared to reference measurements obtained by conventional, time-consuming, image processing techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.