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Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
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基于多场合和数据驱动的优化,对行星滚筒的冷却过程参数进行优化.

Fengli Yue1, Yang Shao1, Hongyun Sun1

  • 1College of Automotive and Transportation, Shenyang Ligong University, Shenyang 110159, China.

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概括
此摘要是机器生成的。

在行星中优化滚筒冷却可以防止铜粘附,并提高产品质量. 机器学习模型,特别是随机森林模型,有效地预测冷却性能,从而可以显著提高传热效率.

关键词:
传热传热传热传热传热传热传热传热传热传热传热传热传热机器学习是机器学习.数字模拟的数字模拟.喷雾冷却 喷雾冷却三卷滚动的行星滚动

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 机械工程 机械工程
  • 计算流体动力学的流体动力学.

背景情况:

  • 在三卷行星中,高卷表面温度会导致铜粘附,降低卷质量和产品产量.
  • 有效的滚筒冷却对于保持表面完整性和提高铜管制造中的生产效率至关重要.

研究的目的:

  • 为了研究喷雾冷却参数对滚面冷却性能的影响.
  • 开发和验证用于滚筒冷却的流体-固体-热合模型.
  • 确定最有效的机器学习模型来预测冷却性能并优化喷雾环几何.

主要方法:

  • 开发了一种流体-固体-热合模型,并通过实验验证了它.
  • 评估随机森林 (RF),梯度提升决策树 (GBDT) 和支持矢量机器 (SVM) 用于预测建模.
  • 采用粒子集群优化 (PSO) 来优化基于射频模型预测的喷雾环参数.

主要成果:

  • 与实验数据相比,相结合的模型实现了高精度 (最大偏差为4.36%).
  • 射频模型的表现优于GBDT和SVM,显示出强大的预测能力 (RMSE=1.73,MAE=1.32,R2=0.91).
  • 优化的RF-PSO方法将传热系数提高了44.72%.

结论:

  • 开发的合模型准确地预测了滚动表面的冷却.
  • 机器学习,特别是射频,为预测冷却性能提供了复杂模拟的高效替代方案.
  • 优化的喷雾冷却参数显著提高了传热,为改进的精密管制造提供了基础.