Investigation on Critical Heat Flux of Flow Boiling in Rectangular Microchannels: A Parametric Study and Assessment of New Prediction Method

Cong Deng, Xiaoping Luo (), Zhiwei Sun, Jinxin Zhang, Yijie Fan and Donglin Liu
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Cong Deng: School of Mechanical and Automobile Engineering, South China University of Technology, Guangzhou 510640, China
Xiaoping Luo: School of Mechanical and Automobile Engineering, South China University of Technology, Guangzhou 510640, China
Zhiwei Sun: School of Energy and Power Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Jinxin Zhang: School of Mechanical and Automobile Engineering, South China University of Technology, Guangzhou 510640, China
Yijie Fan: School of Mechanical and Automobile Engineering, South China University of Technology, Guangzhou 510640, China
Donglin Liu: School of Mechanical and Automobile Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2025, vol. 18, issue 18, 1-20

Abstract: The critical heat flux (CHF) of minichannel heat sinks is crucial, as it helps prevent thermal safety incidents and equipment failure. However, the underlying mechanisms of CHF in minichannels remain poorly understood, and existing CHF prediction models require further refinement. This study systematically investigates the characteristics and influencing factors of critical heat flux (CHF) in rectangular minichannels through combined experimental and theoretical approaches. Experiments were conducted using microchannels with hydraulic diameters ranging from 0.5 to 2.0 mm, with ethanol employed as the working fluid. Key parameters-including mass flux, channel geometry, system pressure, and inlet subcooling-were analyzed to assess their influence on CHF. Results indicate that CHF increases with mass flux; however, the increase rate diminishes under higher mass flux. Larger channel dimensions significantly enhance CHF by delaying liquid film dryout. System pressure further improves CHF by reducing bubble detachment frequency and promoting flow stability. Increased inlet subcooling enhances CHF by delaying the onset of nucleate boiling and improving convective heat transfer. Four classical CHF prediction models were evaluated, revealing significant overprediction-up to 148.69% mean absolute error (MAE)-particularly for channels with hydraulic diameters below 1.0 mm. An ANN deep learning model was developed, achieving a reduced MAE of 8.93%, with 93% of predictions falling within ±15% error. This study offers valuable insights and a robust predictive model for optimizing microchannel heat sink performance in high heat flux applications.

Keywords: microchannels; flow boiling; critical heat flux; inlet subcooling; prediction model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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