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

Xingang Zhao, Koroush Shirvan, Robert K. Salko, Fengdi Guo

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

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.

Original languageEnglish
Article number114540
JournalApplied Thermal Engineering
Volume164
DOIs
StatePublished - Jan 5 2020

Funding

This research was supported by the Consortium for Advanced Simulation of Light Water Reactors (www.casl.gov), an Energy Innovation Hub (http://www.energy.gov/hubs) for modeling and simulation of nuclear reactors under U.S. Department of Energy Contract No. DE-AC05-00OR22725. This research was supported by the Consortium for Advanced Simulation of Light Water Reactors ( www.casl.gov ), an Energy Innovation Hub ( http://www.energy.gov/hubs ) for modeling and simulation of nuclear reactors under U.S. Department of Energy Contract No. DE-AC05-00OR22725 .

Keywords

  • Critical heat flux
  • Departure from nucleate boiling
  • Domain knowledge
  • Hybrid framework
  • Machine learning

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