High-Precision Surrogate Modeling for Uncertainty Quantification in Complex Slurry Flows
arxiv(2024)
摘要
Slurry transportation via pipelines is essential for global industries,
offering efficiency and environmental benefits. Specifically, the precise
calibration of physical parameters for transporting raw phosphate material to
fertilizer plants is crucial to minimize energy losses and ensure secure
operations. Computational fluid dynamics (CFD) is commonly employed to
understand solid concentration, velocity distributions, and flow pressure along
the pipeline. However, numerical solutions for slurry flows often entail
uncertainties from initial and boundary conditions, emphasizing the need for
quantification. This study addresses the challenge by proposing a framework
that combines proper orthogonal decomposition and polynomial chaos expansions
to quantify uncertainties in two-dimensional phosphate slurry flow simulations.
The use of surrogate modeling methods, like polynomial chaos expansion, proves
effective in reducing computational costs associated with direct stochastic
simulations, especially for complex flows with high spatial variability, as
observed in phosphate slurries. Numerical results demonstrate the accuracy of
the non-intrusive reduction method in reproducing mean and variance
distributions. Moreover, the uncertainty quantification analysis shows that the
reduced-order model significantly reduces computational costs compared to the
full-order model.
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