A fully explicit and unconditionally energy-stable scheme for Peng-Robinson VT flash calculation based on dynamic modeling

Abstract
Since the Peng-Robinson (PR) equation of state (EoS) has proven itself to be one of the most reliable EoS, especially in the chemical and petroleum industries, the flash calculation based on the PR EoS is considered to be a foundation for describing complex compositional flows and for evaluating hydrocarbon reservoirs. Compared to the traditional Pressure-Temperature (PT) flash calculation, the novel Volume-Temperature (VT) flash calculation has become more appealing due to its advantages, such as less sensitivity to primary variables like pressure or volume. However, previous numerical schemes of the VT flash calculation involved many complicated nonlinear systems, which makes convergence hard to achieve. To treat this challenge, a fully explicit and unconditionally energy-stable scheme is proposed in this work. It is known that the dynamic model for VT flash calculation can preserve both the Onsager's reciprocal principle and the energy dissipation law. By combining the dynamic model and the linear semi-implicit scheme, the moles and volume can be updated, with the advantage that the energy-dissipation feature can be preserved at a discrete level unconditionally. Then, with the convex-concave splitting approach and the component-wise iteration framework, the scheme becomes fully explicit. The scheme shows promising potential not only because it inherits the original energy stability to ensure convergence, but it also reduces the implementation burden significantly in some engineering scenarios. A lot of numerical experiments are carried out. The numerical results show good agreement with benchmark data and the energy decaying trend at a very large time step demonstrates the stability and efficiency of the proposed scheme.

Citation
Feng, X., Chen, M.-H., Wu, Y., & Sun, S. (2022). A fully explicit and unconditionally energy-stable scheme for Peng-Robinson VT flash calculation based on dynamic modeling. Journal of Computational Physics, 111275. https://doi.org/10.1016/j.jcp.2022.111275

Acknowledgements
Partially supported by King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01, URF/1/4074-01, and URF/1/3769-01. The authors also acknowledge support from the National Natural Science Foundation of China (No.51874262 and No.51936001), Peacock Plan Foundation of Shenzhen (No.000255), the General Program of Natural Science Foundation of Shenzhen (No.20200801100615003) and Ministry of Science and Technology, R.O.C. (No.108-2115-M-194-004-MY2).

Publisher
Elsevier BV

Journal
Journal of Computational Physics

DOI
10.1016/j.jcp.2022.111275

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0021999122003370

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