## 1 Introduction
Some scholars have established a mapping relationship between pressure and slug flow. [PERSON] et al. [29] performed a non  
Figure 1: Schematic diagrams of UWCGES. invasive analysis of slug flow vibration in a curved flexible riser. Notably, the researchers identified that the primary source of vibration induced by the slug flow was localized to the curved surface of the flexible riser. The dynamic response of the riser was influenced by fluctuations in slug flow pressure within the pipeline. [PERSON] and [PERSON] [30] examined pressure drop in slug flow using air and low-viscosity mineral oil in a 4 cm inner diameter pipe. They merged visual observations and pressure drop oscillation time analysis into a theoretical slug flow model for total pressure gradient prediction. [PERSON] et al. [31] studied slug flow pressure with air and water. Pressure sensors distributed along the pipeline enabled statistical and regression analysis, revealing slug flow behavior traits. On this basis, the investigation of slug flow commonly involves a combination of numerical simulation and visualization experiments [32; 33; 34]. Specifically, some studies quantify the movement speed and acceleration characteristic values of micro-scale gas-liquid interfaces under pulsating conditions. Comparison of experimental results with simulation models enables validation of model accuracy. [PERSON] et al. [35] analyzed the gas-liquid slug flow in a honeycomb micro-channel reactor using computational fluid dynamics (CFD) and micro-particle image velocimetry (micro-PIV). Liquid slug breakups caused non-uniform pressure and gas velocity, increased turbulence and altered residence time distribution. [PERSON] et al. [36] investigated the effect of an elbow on slug flow at different velocities using experiments and CFD simulations. The results showed that higher liquid and gas velocities increased the number of bubbles in the elbow and film velocity. [PERSON] et al. [37] proposed a pipeline solution for transitioning the two-phase flow regime using a streamlined mesh partition, achieving lower pressure drop and increased safety with minimal energy costs. CFD simulation using Reynolds-averaged Navier-Stokes equations and k-\(\epsilon\) models supported the feasibility of the proposed solution. However, the CFD model relied heavily on limited experimental data on fluid properties and operating conditions or ineffective assumptions, diminishing accuracy. Based on this, [PERSON] et al. [38] used a 3D-CFD model with Star-CCM+ software to assess non-complex drift velocity correlations, proving the accuracy with an average absolute relative error of only 6%, including highly viscous fluids. For the establishment of drift flux models, [PERSON] et al. [39] implemented OpenFOAM CFD software to derive model parameters for drift flux, enabling the creation of constitutive equations for various scenarios. The CFD modeling accurately simulated slug flow in pipelines when validated against experimental data. Several scholars mentioned above have developed CFD models to enhance the simulation of slug flow in pipelines. It is worth noting that CFD has advantages in simulating slug flow in small pipelines, including multiphase flow simulation, complex geometry modeling, flow and pressure field computation and analysis, prediction of vortices and turbulence, and visualization of flow parameters. However, CFD simulation has limitations when applied to long-distance gas and energy storage pipelines. For instance, the high computational power issues caused by complex structures, model accuracy issues due to possible inaccurate data, problems of assumptions made in CFD models and reliability assessment issues. OLGA demonstrates distinct advantages in simulating long-distance pipelines and energy storage pipelines. Specifically, it offers a methodology to optimize the design and evaluate system performance by considering crucial factors such as storage medium type, pipeline length and height, and pressure.