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Stochastic modelling of nanofluids using the fast Boundary-Domain Integral Method

机译：使用快速边界域积分方法对纳米流体进行随机建模

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摘要

In this paper, we couple a numerical method aimed at simulation of flow and heat transfer of nanofluids with stochastic modelling of input and material parameters. In order to simulate nanofluids, an in-house numerical method was developed, based on the solution of 3D velocity-vorticity formulation of Navier-Stokes equations. A fast Boundary-Domain Integral Method has been employed to solve the governing equations and set up the deterministic flow and heat transfer solver. The developed algorithm is used to simulate natural convection of a nanofluid in a closed cavity. The uncertainty present in the input parameters is propagated to the output of interest via the Stochastic Collocation Method. The stochastic mean, variance, and higher-order moments of the output values are presented. The non-intrusive nature of the Stochastic Collocation Method facilitates the previously validated deterministic code to remain unchanged. The stochastic analysis reveals that the uncertainty of input parameters influences the output results most in the areas where high flow field gradients appear.
机译：在本文中，我们耦合了一种数值方法，旨在模拟纳米流体的流动和传热以及输入和材料参数的随机建模。为了模拟纳米流体，基于Navier-Stokes方程的3D速度涡度公式的求解，开发了一种内部数值方法。快速边界域积分法已被用于求解控制方程并建立确定性的流动和热传递求解器。所开发的算法用于模拟封闭腔中纳米流体的自然对流。输入参数中存在的不确定性通过随机搭配方法传播到目标输出。给出了输出值的随机均值，方差和高阶矩。随机配置方法的非侵入式性质有助于先前验证的确定性代码保持不变。随机分析表明，在出现高流场梯度的区域中，输入参数的不确定性对输出结果的影响最大。

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