Coupled modeling and simulation of tank self-pressurization and thermal stratification

Abstract

Numerical analysis of thermo-fluid dynamics for cryogenic propellant storage primarily consists of nodal and computational fluid dynamics (CFD) modeling approaches. While nodal approaches prioritize faster computation over fidelity, CFD approaches promise accuracy and fidelity at the expense of significant computational resources. This paper presents an efficient coupling methodology developed to facilitate the simulation of a long-term self-pressurization process of a cryogenic propellant tank as well as associated thermal stratification phenomenon under both normal and reduced gravity conditions. Key highlight of the methodology is the development of a coupling scheme based on a domain decomposition approach that separates the tank into two computational regions at the liquid-vapor interface. SINDA/FLUINT, the nodal code, is utilized to simulate the liquid region, while ANSYS Fluent, the CFD code, handles the vapor region. A data exchange algorithm was developed to compute required boundary conditions for each region using the local thermodynamic properties of assumed infinitely thin interface. The coupling between the nodal and CFD codes was demonstrated by simulating a self-pressurization process in a small size tank using hydrogen as the working fluid. A sensitivity study on the grid sizing and coupling time step was conducted to determine appropriate spatial and ensure a divergent-free explicit data exchange time step size. Using the coupling approach, two numerical case studies were performed to study tank self-pressurization by considering two initial hydrogen fill levels. The effectiveness and efficiency of the coupling methodology was assessed by comparing the temperature and pressure evolution results of the coupled simulation with those obtained solely from the CFD simulation. Results showed that the coupling scheme and data exchange algorithm were successfully implemented, and the coupled simulation agreed well with the CFD simulations. In addition, a stratification number was used to characterize the transient dynamic development of the thermal stratification. Lastly, the computational costs associated with both approaches were compared. Significant reductions in simulation time were achieved for both cases.