Impact of Heater Thermal Properties on Nucleate Pool Boiling: Insights from a Multiscale Automata Simulation

TL;DR

Problem addressed: how heater material properties influence nucleate pool boiling. Approach: a two-domain automata model coupling heater-side conduction with surface boiling, applied to copper and silicon oxide, with cavity distributions fixed and a contact angle set. Key findings: high-thermal-diffusivity copper accelerates thermal response, increases $\dot{q}_{me}$ and mean bubble departure frequency, and exhibits two-frequency regimes and collective effects; the model aligns with semi-empirical correlations such as $\nu D_d = 1.18 \frac{t_g}{t_g+t_d} \left[ \frac{\sigma g (\rho_l - \rho_v)}{\rho_l^2} \right]^{1/4}$ and captures spatial heterogeneity at modest cost. Significance: informs heater material choice and surface design to boost pool-boiling heat transfer in electronics and nuclear systems.

Abstract

This study investigates the influence of heater material properties on nucleate pool boiling using a comprehensive simulation model. Copper and silicon oxide are selected as reference materials due to their properties as excellent and poor heat conductors, respectively. The model integrates well-known heat transfer mechanisms, allowing for the assessment of the effects of these distinct heater materials. The results show that materials with superior thermal diffusivity, such as copper, significantly enhance cooling efficiency during nucleate boiling. Moreover, the study provides insights into the relationship between bubble growth, microlayer recovery beneath a bubble, temperature fluctuations, and heater properties. Comparisons between copper and silicon oxide underscore variations in bubble frequency, attributed to differences in bubble growth time, microlayer recovery time, and material-dependent behavior. The influence of neighboring boiling sites is especially pronounced in silicon oxide due to its low thermal conductivity and diffusivity values. Temperature variations in this material become highly visible due to its very slow response to temperature changes. Simulation results align well with semi-empirical correlations, confirming the model's success in capturing the intricate phenomena of nucleate pool boiling. In summary, the model reveals that changes in the thermal properties of the heater affect not only boiling performance but also key characteristics of the process, including bubble frequency, boiling patterns, regularity, and cavity reactivation speed.

Figures (11)

• Figure 1: Model setup diagram. Black dots represent cavities, while hemispheres and circles depict bubbles.
• Figure 2: Schematic representation of the boiling heat transfer model, including combined microlayer and superheated liquid evaporation. Radiation heat is not depicted due to its minor contribution in nucleate boiling.
• Figure 3: Temporal evolution of the average surface temperature found in the $SiO_2$ and the $Cu$ heaters. For reference, error bars have been plotted, indicating the standard deviation of the average surface temperature across all surface cells in the heaters.
• Figure 4: Mean boiling frequency evolution over time up to reach the steady state for two different powers, for both $Cu$ and $SiO_2$ heaters. The error bars in the plot indicate the standard deviation of the average boiling frequency across all active cells in the heaters.
• Figure 5: Evolution the number of active cavities vs. time up to reach the steady state for two different powers, for both $Cu$ and $SiO_2$ heaters.