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首页> 外文期刊>International Journal of Heat and Mass Transfer >Uncertainty quantification of heat transfer in a microchannel heat sink with random surface roughness
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Uncertainty quantification of heat transfer in a microchannel heat sink with random surface roughness

机译：随机表面粗糙度的微通道散热器中传热的不确定性定量

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摘要

To numerically investigate the stochastic heat transfer performance of a microchannel heat sink, a randomly generated, rough surface profile with a prespecified autocorrelation function is applied to its bottom surface. The magnitude of the maximum relative roughness of the surface is treated as a Gaussian random input with specified uncertainty, and uncertainty in the output is modeled via polynomial chaos expansions. According to deterministic analysis of the simulation results, an increase in surface roughness enhances the heat transfer. As found through stochastic analysis of the joint probability density functions of the local surface height and local Nusselt number, the local surface height alone is insufficient to explain changes in the local Nusselt number, and the sensitivity of the local Nusselt number to other factors increases with the absolute value of the local surface height. Polynomial chaos expansions are used to identify the peaks and ridges of the surface as regions where the Nusselt number and velocity magnitude are more sensitive to the local surface roughness than in valleys. Probability density functions are estimated from the polynomial chaos expansions of the local Nusselt number, spanwise-averaged Nusselt number, and three global outputs: the average Nusselt number over the rough bottom surface, the pressure drop over the channel, and the performance factor of the bottom surface. Because of nonlinear propagation of the Gaussian random input, the distributions of the local and spanwise-averaged Nusselt numbers can not generally be assumed to have a Gaussian shape. In contrast, the global outputs can be treated as Gaussian as a good approximation.
机译：为了数值研究微通道散热器的随机传热性能，将随机产生的粗糙表面轮廓施加到其底表面上。表面的最大相对粗糙度的大小被视为具有指定不确定性的高斯随机输入，并且通过多项式混沌扩展建模输出中的不确定性。根据模拟结果的确定性分析，表面粗糙度的增加增强了传热。如通过随机分析的局部表面高度和局部露珠数的随机分析，单独的局部高度不足以解释当地营养数的变化，以及当地营养号码与其他因素的敏感度增加局部表面高度的绝对值。多项式混沌扩展用于将表面的峰值和脊识别为何时何地区的营养数和速度幅度对局部表面粗糙度比谷的速度更敏感。概率密度函数估计本地露天数量，跨越纽带数和三个全局输出的多项式混沌扩展：粗糙底面的平均冲击数，频道上的压力下降，以及性能因子底表面。由于高斯随机输入的非线性传播，通常不能假设局部和跨越平均界面数的分布以具有高斯形状。相比之下，全局输出可以被视为高斯作为一个很好的近似。

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