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Reduced-order modelling of equations of state using tensor decomposition for robust, accurate and efficient property calculation in high-pressure fluid flow simulations
The Journal of Supercritical Fluids ( IF 3.4 ) Pub Date : 2020-06-16 , DOI: 10.1016/j.supflu.2020.104938
Carmen Alfaro-Isac , Salvador Izquierdo-Estallo , José Sierra-Pallares

Computationally efficient, accurate and robust Computational Fluid Dynamics (CFD) simulations involving thermodynamic properties from Equations of State (EOS) are hindered by limitations dictated by coupling strategies between EOS and CFD codes. This is a key aspect for a wide range of Chemical Engineering designs with special emphasis on those involving transcritical flows. We introduce a ROM approach based on a non-structured and sparse implementation of the Canonical Polyadic Decomposition of tensors that target abovementioned requirements. It reaches a similar speed with regards to direct use of the full equation of state and provides mean errors about 1 %–5 % without limiting accuracy. Its implementation is done in a standard and portable way, avoiding the need of additional implementation and an easy coupling with open and commercial CFD codes. The method is tested here for CFD but it can be directly applied in any process simulation tool.



中文翻译:

使用张量分解对状态方程进行降阶建模,以便在高压流体流动模拟中进行可靠,准确和高效的属性计算

EOS与CFD代码之间的耦合策略规定了一些限制,从而影响了涉及状态方程(EOS)的热力学性质的计算效率,准确性和鲁棒性的计算流体动力学(CFD)模拟。这是广泛的化学工程设计的关键方面,特别强调涉及跨临界流的设计。我们基于针对上述要求的张量的规范多态分解的非结构化和稀疏实现,引入了一种ROM方法。在直接使用完整状态方程时,它达到了相似的速度,并且在不限制精度的情况下提供了大约1%–5%的平均误差。它的实现是通过标准且可移植的方式完成的,避免了额外的实现方式,并且无需与公开的CFD代码轻松结合。该方法在此处已针对CFD进行了测试,但可以直接应用于任何过程仿真工具中。

更新日期:2020-06-16
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