Pressure effect on CHF enhancement in pool boiling of nanofluids

Sakashita, Hiroto

doi:10.1080/00223131.2015.1072482

Cited by 19 publications

(3 citation statements)

References 21 publications

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“…When the boiling process happens, at first, the nucleate boiling happens, where vapor bubbles form at nucleation sites on the heated surface. At this condition, the bubbles detach from the hot surface and move within the fluid, which results in an effective heat transfer [8]. By raising the input heat flux to the fluid, the creation of the bubble increases till a situation where a continuous creation of bubbles makes a layer of vapor that covers the heating surface [9].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids' superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modelling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN's superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithms models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable to consider many effective parameters into account with very higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF, but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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“…Dahariya and Betz [6,7] conducted pool boiling experiments for water on horizontal smoothed and sintered-particle wick copper surfaces at pressures ranging from 0 to 413.7 kPa, and the results showed that higher pressure would result in the enhancement of CHF due to the formation of a thermal boundary layer, merging of vapor trains into neighboring trains and modulation of wavelength. Sakashita [8] measured CHF in saturated pool boiling for water and TiO 2 nano uid on a vertical copper surface at pressures of 0.1∼0.8 MPa, and they indicated that CHF of water and a nano uid increased with the increase of pressure, while CHF enhancement of a nano uid would decrease with the increasing pressure. Similar to the abovementioned experimental results, Mudawar and Anderson [9], Alvarino et al [10], and Guan et al [11] also reported that pool boiling heat transfer performance and CHF would be improved with the increasing system pressure.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Analytical Study on the Influence of Saturation Pressure and Surface Roughness on Pool Boiling CHF of HFE-7100

Fan

Lei

et al. 2022

International Journal of Chemical Engineering

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Critical heat flux (CHF) determines the safety and application of pool boiling heat transfer in many industrial fields. The influence of saturation pressure and surface roughness on saturated pool boiling CHF in hydrofluoroether HFE-7100 was experimentally studied in this investigation. Visualization and heat transfer measurements were conducted at the critical and transition boiling state, and further, the accuracy of CHF prediction models and enhancement mechanism had been analyzed. The polished boiling surfaces had various surface roughness values ranging from 0.019 to 0.587 μm and their contact angles varied from 7° to 10°, while the experimental saturation pressure changed from 0.7 to 2.0 bar. The visual images showed that the pool boiling phenomenon at a critical state was composed of different-sized bubbles, vapor column, and large mushroom vapor, whereas the unsteady blanket of vapor continually injected bubbles at a transition state. The saturation pressure and surface roughness had an obvious improvement on pool boiling CHF, which might be ascribed to the effects of bubble momentum owing to evaporation, distribution and recovery period of a heat transfer boundary layer, capillary action of the working liquid, as well as ratio of vapor jets’ area. Compared with the well-known correlations reported in the literature, CHF correlation of Bailey et al. (2006) predicted the current results more accurately. To further improve the prediction accuracy, a new empirical correlation for CHF dimensionless K considering the effects of saturation pressure and surface roughness was developed, and the predicted values were in better agreement with the experimental data.

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“…Although nanofluids exhibit considerably higher critical heat flux (thermal limit) than conventional fluids (such as water) in phase change processes [10], the underlying physics for critical heat flux enhancement in nanofluids is still unclear to the research community. However, the enhanced surface wettability, roughness and wicking effect due to the nanoparticle deposition on a heated surface during the phase change process are considered as possible reasons for high critical heat flux in nanofluids [11,12]. Despite thermal benefits of nanofluids over conventional heat transfer fluids, the former do not exhibit overall hydrothermal characteristics (high dispersion stability and enhanced thermal conductivity).…”

Section: Introductionmentioning

confidence: 99%

Droplet Evaporation of Cu–Al2O3 Hybrid Nanofluid Over Its Residue and Copper Surfaces: Toward Developing a New Analytical Model

Siddiqui

Tso

et al. 2020

Journal of Heat Transfer

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Droplet evaporation based cooling techniques, such as the spray cooling, give high heat transfer rates by utilizing latent energy and are usually preferred in thermal applications. However, with the significant rise in heat dissipation levels for high heat flux devices, these devices cannot be thermally managed due to the limited cooling capacity of existing thermal fluids. In this paper, we report the evaporation of the Cu-Al2O3 hybrid nanofluid (HNF) droplet on a copper surface as well as its own residue surface, developed from the evaporation of the first Cu-Al2O3 HNF droplet. As the main novelty, we identify the critical residue size and investigate the residue size effect, above and below the critical residue size, on evaporation rate of the succeeding Cu-Al2O3 HNF droplet resting over a residue surface. We also develop a new analytical model to estimate the Cu-Al2O3 HNF droplet evaporation rate and compare our results with other existing models. The results show that the Cu-Al2O3 HNF droplet gives 17% higher evaporation rate than a water droplet on a copper surface. Also, the evaporation rate of the Cu-Al2O3 HNF droplet on a residue surface sharply increases by 106% with increasing residue size up to the critical residue size. However, further increasing the residue size above its critical value has a negligible effect on the droplet evaporation rate. Moreover, the evaporation rate of the Cu-Al2O3 HNF droplet on its residue surface is enhanced up to 104% when compared to a copper surface.

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Pressure effect on CHF enhancement in pool boiling of nanofluids

Cited by 19 publications

References 21 publications

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Experimental and Analytical Study on the Influence of Saturation Pressure and Surface Roughness on Pool Boiling CHF of HFE-7100

Droplet Evaporation of Cu–Al2O3 Hybrid Nanofluid Over Its Residue and Copper Surfaces: Toward Developing a New Analytical Model

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