On the prediction of critical heat flux using a physics-informed machine learning-aided framework

TL;DR

This paper introduces a hybrid physics-informed machine learning framework to predict critical heat flux in boiling systems, outperforming traditional models and enhancing generalization across diverse conditions.

Contribution

It develops a novel hybrid approach combining domain knowledge and machine learning, improving prediction accuracy and robustness for critical heat flux in boiling systems.

Findings

Hybrid model outperforms traditional domain knowledge models.

Hybrid approach shows superior generalization across flow conditions.

Framework is flexible and can extend to more complex scenarios.

Abstract

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of predictive tools available to the thermal engineering community, the path for an accurate, robust CHF model remains elusive due to lack of consensus on the DNB triggering mechanism. This work aims to apply a physics-informed, machine learning (ML)-aided hybrid framework to achieve superior predictive capabilities. Such a hybrid approach takes advantage of existing understanding in the field of interest (i.e., domain knowledge) and uses ML to capture undiscovered information from the mismatch between the actual and domain knowledge-predicted target. A detailed case study is carried out with an extensive DNB-specific CHF database to demonstrate (1) the improved performance of the hybrid approach as compared to traditional domain knowledge-based models, and (2) the hybrid model's superior generalization capabilities over standalone ML methods across a wide range of flow conditions. The hybrid framework could also readily extend its applicability domain and complexity on the fly, showing an elevated level of flexibility and robustness. Based on the case study conclusions, the window-type extrapolation mapping methodology is further proposed to better inform high-cost experimental work.