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RAD-NNET, a neural network based correlation developed for a realistic simulation of the non-gray radiative heat transfer effect in three-dimensional gas-particle mixtures

机译:RAD-NNET,基于神经网络的相关性,用于逼真的模拟三维气体-颗粒混合物中的非灰色辐射传热效果

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摘要

A neural network correlation, RAD-NNET, is developed to simulate the realistic effect of non-gray radiative absorption by a homogeneous mixture of combustion gases (CO_2 and H_2O) and soot using numerical data generated by RADCAL RAD-NNET is then applied to assess the accuracy of some commonly accepted approximate approaches to evaluate radiative heat transfer in three-dimensional non-gray media. Results show that there are significant errors associated with the current approximate approaches. RAD-NNET can be readily implemented in commercial CFD codes to greatly enhance the accuracy of simulation of radiative heat transfer in practical engineering systems.
机译:利用RADCAL生成的数值数据,开发了神经网络相关性RAD-NNET来模拟燃烧气体(CO_2和H_2O)和烟灰的均匀混合物对非灰色辐射吸收的实际影响。一些普遍接受的近似方法在三维非灰色介质中辐射热传递的准确性。结果表明,与当前的近似方法相关的重大错误。 RAD-NNET可以很容易地在商业CFD代码中实施,以大大提高实际工程系统中辐射传热模拟的准确性。

著录项

  • 来源
    《International Journal of Heat and Mass Transfer》 |2009年第14期|3159-3168|共10页
  • 作者

    Walter W. Yuen;

  • 作者单位

    Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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