A Study of Nanoparticle Surface Modification Effects on Pool Boiling Critical Heat Flux

Stange, Gary M.; Yeom, Hwasung; Semerau, Brandon; Sridharan, Kumar; Corradini, Michael L.

doi:10.13182/nt13-a16980

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…The same procedure was used for the NP size [3,19,25], as described in the Table 1 footnotes. Two further important assumptions were used for the database construction: the NF concentration unit was converted to vol.% [5,15,17,19,22,25,31,41,42,44], and 316 L stainless steel properties were used for the non-specified stainless steel heating elements [36,40].…”

Section: Database Assumptions and Limitationsmentioning

confidence: 99%

“…According to Figure 18a, the increasing TiO 2 NP size decreases CHF. Stange et al [5] investigated how CHF changed for TiO 2 NP pool boiling using different NP sizes. They believe that this can be explained by the NP attachment to the surface.…”

Section: Chf × Np Sizementioning

confidence: 99%

“…According to all three feature importance models previously discussed, the effect of substrate thermal effusivity was considered negligible to NF pool boiling CHF. However, Stange et al [5] reported that titanium (Ti) NPs have better adhesion to zirconium than other oxide NPs. This brings up another discussion about NF pool boiling: the selection of the pairing of the heating element and the nanoparticle type can also be very important in achieving further CHF improvements.…”

Section: Chf × Substrate Thermal Effusivitymentioning

confidence: 99%

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Analysis of the Effects of Different Nanofluids on Critical Heat Flux Using Artificial Intelligence

Serrao,

Kim,

Duarte

2023

Energies

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Nanofluid (NF) pool boiling experiments have been conducted widely in the past two decades to study and understand how nanoparticles (NP) affect boiling heat transfer and critical heat flux (CHF). However, the physical mechanisms related to the improvements in CHF in NF pool boiling are still not conclusive due to the coupling effects of the surface characteristics and the complexity of the experimental data. In addition, the current models for pool boiling CHF prediction, which consider surface microstructure characteristics, show limited agreement with the experimental data and do not represent NF pool boiling CHF. In this scenario, artificial intelligence tools, such as machine learning (ML) regressor models, are a very promising means of solving this nonlinear problem. This study focuses on creating a new model to provide more accurate NF pool boiling CHF predictions based on pressure, substrate thermal effusivity, and NP size, concentration, and effusivity. Three ML models (supporting vector regressor—SVR, multi-layer perceptron—MLP, and random forest—RF) were constructed and showed good agreement with an experimental database built from the literature, with MLP presenting the highest mean R2 score and the lowest variability. A systematic methodology for optimizing the ML models is proposed in this work.

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