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<p>The coupling effects of rotational hypergravity (1–3.16&#xa0;g) and nanoparticle concentration (0–0.015 wt.%) on the nucleate pool boiling heat transfer of Al₂O₃–water nanofluids were experimentally investigated using a custom-built centrifugal platform. Three machine-learning algorithms (RF, SVM, XGBoost) were further employed to develop high-fidelity predictive models. Contrary to the monotonic deterioration reported in previous literature for pure fluids, this study identifies a distinct non-monotonic enhancement–deterioration trend in the heat transfer coefficient (HTC). The HTC peaks at approximately 1.41&#xa0;g (buoyancy-assisted bubble detachment dominating) but deteriorates by up to 25.5% at 3.16&#xa0;g due to hypergravity-induced sedimentation that overrides Brownian diffusion and forms a compact thermal resistance layer. Nanoparticle concentration exhibits a complex non-linear impact: intermediate concentrations (0.0025–0.0075 wt.%) cause surface clogging and performance decline, whereas the optimal concentration of 0.015 wt.% yields maximum HTC enhancement (&gt; 60&#xa0;kW/m<sup>2</sup> K) which is hypothesized to reconstruct the heating surface with strong capillary wicking. Among the ML models, XGBoost demonstrated exceptional predictive fidelity with R<sup>2</sup> = 0.998, RMSE = 1.2%, and MAE = 0.8%, far outperforming classical semi-empirical correlations. These findings provide robust mechanistic insights and a reliable predictive tool for aerospace thermal management systems under variable gravity conditions.</p>

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Experimental investigation and data-driven modeling of nanofluid pool boiling under rotational hypergravity

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Gen Li,
  • Ao Tian,
  • Yafeng Chen,
  • Xiaojie Zhang,
  • Jianxun Xie,
  • Xiande Fang

摘要

The coupling effects of rotational hypergravity (1–3.16 g) and nanoparticle concentration (0–0.015 wt.%) on the nucleate pool boiling heat transfer of Al₂O₃–water nanofluids were experimentally investigated using a custom-built centrifugal platform. Three machine-learning algorithms (RF, SVM, XGBoost) were further employed to develop high-fidelity predictive models. Contrary to the monotonic deterioration reported in previous literature for pure fluids, this study identifies a distinct non-monotonic enhancement–deterioration trend in the heat transfer coefficient (HTC). The HTC peaks at approximately 1.41 g (buoyancy-assisted bubble detachment dominating) but deteriorates by up to 25.5% at 3.16 g due to hypergravity-induced sedimentation that overrides Brownian diffusion and forms a compact thermal resistance layer. Nanoparticle concentration exhibits a complex non-linear impact: intermediate concentrations (0.0025–0.0075 wt.%) cause surface clogging and performance decline, whereas the optimal concentration of 0.015 wt.% yields maximum HTC enhancement (> 60 kW/m2 K) which is hypothesized to reconstruct the heating surface with strong capillary wicking. Among the ML models, XGBoost demonstrated exceptional predictive fidelity with R2 = 0.998, RMSE = 1.2%, and MAE = 0.8%, far outperforming classical semi-empirical correlations. These findings provide robust mechanistic insights and a reliable predictive tool for aerospace thermal management systems under variable gravity conditions.