Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.
Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.
复制标题
用于辐照粒子负载湍流的不确定性量化和灵敏度分析的双保真度近似。
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Doostan
中科院分区：
文献类型：
--
作者：
Hillary R. Fairbanks;L. Jofre;G. Geraci;G. Iaccarino;A. Doostan
有效地进行预测研究的辐射粒子负载的湍流有可能提供显着的贡献，更好地理解和优化，例如，集中的太阳能发电系统。由于这种流动中存在许多固有的不确定性，不确定性量化是提高数值模拟预测能力的基础。对于具有高维不确定性的大规模多物理问题，表征随机解提出了一个重大的计算挑战，因为许多方法需要大量的高保真解决方案。当典型的融合高保真仿真需要密集的计算资源时，这种要求导致需要可能不可行的仿真次数。为了降低量化高维不确定性的成本，我们研究了一个非侵入性的，双保真度近似估计与辐照粒子负载湍流相关的感兴趣的数量的统计数据的应用。这种方法依赖于利用低秩结构的解决方案，以加速随机采样和近似过程的廉价运行，较低的保真度表示。该双保真度近似的应用导致对生活质量统计的准确估计，同时需要少量的高保真模型评估。
Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations.
影响因子：
2.7
作者：
Giles, Michael B.
通讯作者：
Giles, Michael B.
影响因子：
--
作者：
Cliffe, K. A.;Giles, M. B.;Teckentrup, A. L.
通讯作者：
Teckentrup, A. L.
影响因子：
2.1
作者：
Barth, Andrea;Schwab, Christoph;Zollinger, Nathaniel
通讯作者：
Zollinger, Nathaniel