  The detection of critical heat flux (CHF) is crucial in heat boiling
applications as failure to do so can cause rapid temperature ramp leading to
device failures. Many machine learning models exist to detect CHF, but their
performance reduces significantly when tested on data from different domains.
To deal with datasets from new domains a model needs to be trained from
scratch. Moreover, the dataset needs to be annotated by a domain expert. To
address this issue, we propose a new framework to support the generalizability
and adaptability of trained CHF detection models in an unsupervised manner.
This approach uses an unsupervised Image-to-Image (UI2I) translation model to
transform images in the target dataset to look like they were obtained from the
same domain the model previously trained on. Unlike other frameworks dealing
with domain shift, our framework does not require retraining or fine-tuning of
the trained classification model nor does it require synthesized datasets in
the training process of either the classification model or the UI2I model. The
framework was tested on three boiling datasets from different domains, and we
show that the CHF detection model trained on one dataset was able to generalize
to the other two previously unseen datasets with high accuracy. Overall, the
framework enables CHF detection models to adapt to data generated from
different domains without requiring additional annotation effort or retraining
of the model.
