Geometry and mesh are designed and generated by Design Modeler and ANSYS Meshing, respectively.

Compressible Flow Analysis in Curved Pipe Geometry - ANSYS Fluent CFD Training Project Overview This advanced CFD simulation examines compressible fluid dynamics within a curved pipe configuration, with particular emphasis on shock wave phenomenon analysis. The study involves air flow at 5°C entering the pipe inlet at Mach 0.9, creating significant pressure gradients that generate shock wave formations. To accurately resolve these high-gradient flow regions, adaptive mesh refinement techniques are employed using ANSYS Fluent’s gradient adaptation capabilities. Geometry and Mesh Generation Geometric Modeling The three-dimensional pipe geometry was developed using Design Modeler software, featuring a curved pipe configuration with a 30mm internal diameter. The bent geometry design creates the necessary conditions for compressible flow effects and shock wave development. Computational Grid Development Initial mesh generation was performed using ANSYS Meshing software, producing an unstructured tetrahedral grid containing 191,479 computational elements. Following the implementation of gradient-based mesh adaptation techniques, the refined grid expanded to 1,450,983 elements, providing enhanced resolution in critical flow regions with steep gradients. Simulation Methodology Solver Configuration The analysis employs a density-based computational approach specifically designed for compressible flow applications. The simulation is executed in transient mode to capture the temporal evolution of shock wave phenomena and pressure wave propagation through the curved pipe geometry. Turbulence Modeling The K-Omega SST viscous model is implemented to accurately predict fluid behavior, particularly in near-wall regions where viscous effects become significant in compressible flow conditions. Results and Analysis Flow Conditions and Shock Formation Air enters the pipe system at Mach 0.9 velocity and 5°C temperature, encountering severe pressure reduction at the pipe curvature. This phenomenon manifests as shock wave formation, requiring high-resolution computational grids for accurate simulation. Mesh Quality Considerations While optimal y-plus values below unity are recommended for accurate near-wall flow resolution, the initial coarse tetrahedral mesh configuration could not satisfy this criterion. Consequently, gradient adaptation techniques were implemented to refine mesh density in regions exhibiting elevated y-plus values. Pressure Distribution Results The simulation results demonstrate approximately 120 kPa pressure reduction along the flow path, creating substantial variable distribution throughout the pipe geometry. The pressure contours clearly illustrate the shock wave structure and its impact on the overall flow field characteristics.

Gas Sweetening System Hydrodynamic Analysis - ANSYS Fluent CFD Simulation Project Overview This computational fluid dynamics study focuses on the hydrodynamic behavior within a gas sweetening facility using ANSYS Fluent software. Gas sweetening represents a critical industrial process for eliminating hydrogen sulfide, carbon dioxide, mercaptans, and additional contaminants from natural gas and synthetic gas streams, ensuring safe transportation and end-use applications. The treatment of sour gas is essential due to the severe corrosive properties of hydrogen sulfide and carbon dioxide on pipeline infrastructure, along with their toxic effects on human health. Simulation Scope and Methodology Process Modeling Approach The computational domain incorporates two distinct materials: a specific sour gas composition and an amine solution stream. This investigation concentrates exclusively on hydrodynamic modeling aspects, excluding actual gas removal mechanisms that typically involve complex physical or chemical interactions. Water serves as the amine material substitute for this hydrodynamic analysis. Multiphase Flow Configuration A Volume of Fluid (VOF) multiphase model defines the two-phase computational environment. The system features dual inlet configurations for amine and gas streams, with the amine flow entering at 0.3 m/s velocity before encountering the gas stream within the processing equipment. Geometric Design and Computational Grid Three-Dimensional Geometry Development The gas sweetening equipment model was constructed using Design Modeler software, incorporating realistic inlet configurations for both gas and amine stream introduction into the processing vessel. Mesh Generation Specifications Computational grid development utilized ANSYS Meshing software, generating an unstructured mesh containing 2,168,649 elements. This mesh density provides adequate resolution for capturing the complex multiphase flow interactions within the sweetening equipment. CFD Simulation Configuration Fundamental Assumptions Pressure-based solver implementation for incompressible flow analysis Steady-state simulation approach Gravitational acceleration of -9.81 m/s² applied along the vertical direction Turbulence and Multiphase Modeling Model Category Configuration Parameters Viscous Model k-epsilon RNG Standard wall function treatment Multiphase Model VOF Method 2 Eulerian phases (gas & water), Dispersed interface modeling Boundary Condition Specifications Boundary Type Configuration Parameters Gas Inlet Velocity Inlet 0 m/s velocity, 0 water volume fraction Amine Inlet Velocity Inlet 0.3 m/s velocity, 1.0 water volume fraction Gas Outlet Pressure Outlet 0 Pa gauge pressure Amine Outlet Pressure Outlet 0 Pa gauge pressure Equipment Walls Stationary Wall No-slip condition Numerical Methods and Solution Algorithms Parameter Method Pressure-Velocity Coupling SIMPLE algorithm Pressure Discretization PRESTO scheme Momentum Second-order upwind Turbulence Parameters First-order upwind Volume Fraction First-order upwind Initial Conditions Standard initialization with zero gauge pressure, zero velocity components, and zero water volume fraction throughout the computational domain. Results and Flow Analysis Flow Interaction Characteristics The simulation results present comprehensive two-dimensional and three-dimensional contour visualizations for pressure distribution, velocity fields, and phase volume fractions for both gas and water phases. The analysis reveals that gas and amine streams undergo collision after navigating through internal flow barriers within the processing equipment. Hydrodynamic Performance The collision interaction between the two streams demonstrates the amine current’s capability to redirect portions of the gas flow toward the equipment outlet. This hydrodynamic behavior forms the foundation for understanding the mixing and contact efficiency in actual gas sweetening operations, where chemical absorption would occur between the amine solution and acid gas components. Engineering Insights The velocity and pressure contours provide valuable insights into flow distribution patterns, mixing zones, and potential areas for equipment optimization. These hydrodynamic characteristics are essential for designing efficient gas-liquid contact systems in industrial sweetening applications.

Pipeline Pigging Oil Flow Analysis - ANSYS Fluent CFD Training Project Overview This computational fluid dynamics investigation examines pigging operations within oil pipeline systems using ANSYS Fluent software. The simulation incorporates a Pipeline Inspection Gauge (pig) positioned within the pipeline structure to analyze flow dynamics and operational impacts. Pipeline Pigging Technology Pipeline Inspection Gauges serve multiple critical functions including geometric and fluid parameter monitoring, pipeline cleaning operations, and creating physical separation barriers between different fluid types. Pigging operations encompass the deployment, control, and navigation of these devices through pipeline networks for maintenance and inspection purposes. Engineering Challenge The presence of pigs within fluid flow pathways creates flow obstruction, resulting in significant pressure differentials across the device. This pressure drop phenomenon represents a key operational concern requiring detailed analysis to optimize pigging efficiency while minimizing flow disruption. Simulation Objectives and Scope Primary Investigation Goals The simulation focuses on analyzing fluid behavior surrounding the pig body within the pipeline environment, with particular emphasis on pressure differential characterization across the upstream and downstream pig surfaces. Operational Parameter Analysis The study examines two distinct flow scenarios featuring oil inlet velocities of 0.9 m/s and 1.9 m/s, enabling comparative analysis of velocity-dependent pigging effects on system performance. Multiphase Flow Modeling A Volume of Fluid (VOF) multiphase approach defines the gas-oil and petroleum material interactions within the pipeline system, providing comprehensive representation of real-world pigging conditions. Geometric Configuration and Computational Grid Two-Dimensional Model Development The pipeline geometry was constructed using Design Modeler software, incorporating a simplified stationary pig configuration within a representative pipeline cross-section. Computational Mesh Characteristics The computational grid was developed using ANSYS Meshing software with an unstructured mesh configuration containing 5,789 elements. The mesh quality was optimized specifically for capturing pig-fluid interaction phenomena and ensuring accurate flow field resolution around the pig geometry. CFD Simulation Configuration Fundamental Modeling Assumptions The simulation employs a pressure-based solver implementation for incompressible flow analysis with a transient approach for temporal pressure drop analysis. Gravitational effects are neglected to provide simplified flow analysis focusing on the primary pigging phenomena. Turbulence and Multiphase Modeling Framework The viscous modeling utilizes the k-epsilon standard turbulence model with standard near-wall treatment for boundary layer resolution. The multiphase flow is handled through the VOF method incorporating two Eulerian phases with implicit formulation and sharp interface modeling to accurately capture the gas-oil and petroleum phase interactions. Boundary Condition Specifications The pipeline inlet is configured as a velocity inlet with variable velocity settings of 0.9 m/s and 1.9 m/s depending on the simulation case, with petroleum volume fraction set to unity and gas-oil volume fraction at zero. The pipeline outlet employs a pressure outlet boundary condition with zero gauge pressure. Both the pipeline walls and pig surfaces are treated as stationary walls with no-slip boundary conditions to accurately represent the physical constraints. Numerical Solution Methods The pressure-velocity coupling utilizes the SIMPLE algorithm for solution convergence. Pressure discretization employs the PRESTO scheme for enhanced accuracy in complex geometries. Momentum equations are solved using second-order upwind discretization, while volume fraction transport uses the compressive scheme to maintain sharp interfaces. Turbulent kinetic energy and dissipation rate equations employ first-order upwind discretization for numerical stability. Temporal Configuration The simulation runs for a total duration of 90 seconds with a time step of 0.03 seconds, resulting in 3,000 total time steps. This temporal resolution provides adequate capture of transient pressure drop development and flow field evolution around the pig geometry. Initial Conditions Standard initialization is applied throughout the computational domain with zero gauge pressure, zero x-velocity component, and y-velocity corresponding to the respective inlet conditions for each simulation case. The petroleum volume fraction is initialized to zero throughout the domain, allowing the inlet boundary condition to drive the phase distribution. Results and Engineering Analysis Flow Field Characterization The simulation generates comprehensive two-dimensional contour visualizations depicting pressure distribution, velocity fields, and phase volume fractions for both defined fluid phases. All contour results represent final time-step conditions at 90 seconds, providing complete characterization of the developed flow field around the pig geometry. Pressure Drop Analysis The investigation reveals pressure differential patterns across the pig geometry, providing quantitative data on flow obstruction effects at different operational velocities. These results enable optimization of pigging operations while maintaining acceptable pressure losses and demonstrate the relationship between flow velocity and pressure drop magnitude. Velocity-Dependent Performance Comparative analysis between 0.9 m/s and 1.9 m/s inlet conditions demonstrates the relationship between flow velocity and pigging efficiency, offering insights for operational parameter selection in real-world applications. The higher velocity case shows increased pressure differentials and more pronounced flow disturbances around the pig body. Engineering Applications The simulation results provide valuable data for pipeline operators regarding pigging operation planning, pressure drop predictions, and system performance optimization during inspection and maintenance activities. The temporal analysis capability allows for understanding of transient effects during pig deployment and retrieval operations.

Tank Discharge System CFD Analysis - ANSYS Fluent Simulation Project Overview This computational fluid dynamics study investigates gravitational water discharge from a multi-tank system using ANSYS Fluent software. The simulation employs the Volume of Fluid (VOF) model to accurately capture the two-phase flow dynamics involving water and air phases throughout the interconnected tank network. System Configuration Multi-Tank Arrangement The computational domain encompasses three interconnected storage tanks connected through a piping network. The primary tank features rectangular geometry with dimensions of 229.4 mm by 157.7 mm, designed to serve as the initial water reservoir. The secondary tank utilizes an octagonal configuration with uniform side lengths of 51.3 mm, providing intermediate storage capacity. The tertiary tank employs rectangular geometry measuring 229.4 mm by 100 mm, functioning as the final collection vessel. Geometric Design and Computational Grid Two-Dimensional Model Development The geometric configuration was developed using Design Modeler software, incorporating realistic tank geometries and interconnecting pipe networks to simulate industrial discharge systems. The design includes air circulation pathways to maintain atmospheric pressure balance during discharge operations. Mesh Generation Specifications The computational grid was generated using ANSYS Meshing software with an unstructured mesh topology containing 15,310 elements. This mesh density provides adequate resolution for capturing the complex free surface dynamics and flow transitions between the interconnected tank systems. CFD Simulation Configuration Fundamental Modeling Assumptions The simulation utilizes a pressure-based solver approach suitable for incompressible flow conditions. The analysis is conducted in transient mode to capture the temporal evolution of the discharge process and free surface movement. Gravitational acceleration of -9.81 m/s² is applied along the negative y-axis to drive the discharge phenomenon. Multiphase Flow Modeling The Volume of Fluid homogeneous model governs the two-phase flow field equations with air and water as the defined Eulerian phases. Sharp interface modeling with interfacial anti-diffusion capabilities ensures accurate free surface tracking throughout the discharge process. The implicit formulation with implicit body force treatment provides robust solution stability for gravitational flow applications. Viscous Flow Treatment Laminar viscous modeling is employed to solve the flow field equations, appropriate for the low Reynolds number conditions typical in gravitational discharge applications. This approach provides accurate representation of viscous effects without the computational overhead of turbulence modeling. Material Properties Air properties are defined with density of 1.225 kg/m³ and dynamic viscosity of 1.7894×10⁻⁵ Pa·s, representing standard atmospheric conditions. Water-liquid properties utilize density of 998.2 kg/m³ and dynamic viscosity of 0.001003 Pa·s, corresponding to water at standard temperature conditions. Numerical Solution Methods The pressure-velocity coupling employs the SIMPLE algorithm for iterative solution convergence. Pressure discretization utilizes the PRESTO! scheme, optimized for complex geometries with significant density variations. Momentum equations are solved using second-order upwind discretization for enhanced accuracy, while volume fraction transport employs the compressive scheme to maintain sharp interface definition. Domain Initialization and Patching Standard initialization is applied throughout the computational domain with subsequent patching operations to establish initial water distribution. The primary tank region is initialized with unity volume fraction for the water phase, corresponding to coordinates spanning from x = 0.08 m to x = 0.379 m and y = 0.2264246 m to y = 0.33 m. Temporal Solution Configuration The simulation employs adaptive time advancement with initial time step size of 1×10⁻⁵ seconds. The adaptive scheme maintains minimum time step of 1×10⁻⁵ seconds and maximum time step of 0.001 seconds, with total execution of 10,000 time steps to capture complete discharge dynamics. Results and Flow Analysis Discharge Process Characterization The simulation results present comprehensive visualization of volume fraction distribution, pressure fields, velocity magnitude, and streamline patterns throughout the discharge evolution. The analysis demonstrates progressive water transfer from the primary tank to the secondary tank, with subsequent overflow to the tertiary tank as storage capacity limitations are exceeded. Free Surface Dynamics Volume fraction contours clearly illustrate the free surface evolution and interface tracking accuracy throughout the discharge process. The VOF model successfully captures the complex interface deformation as water flows through the connecting pipes and fills the downstream tanks. Flow Field Analysis Velocity and streamline visualizations reveal the flow patterns within each tank and connecting pipe network. The results demonstrate the influence of air circulation pathways in maintaining pressure equilibrium and preventing vacuum formation during discharge operations. Engineering Insights The simulation provides valuable insights into multi-tank discharge system design, including optimal pipe sizing, tank geometry effects, and air circulation requirements. The pressure distribution analysis enables assessment of system efficiency and identification of potential flow restrictions or optimization opportunities.

Tank Filling System CFD Analysis - ANSYS Fluent Simulation Project Overview This computational fluid dynamics investigation examines tank filling operations between two interconnected reservoirs using ANSYS Fluent software. The simulation focuses on two-phase flow dynamics involving air and water phases within tanks of equal height, representing common industrial applications in chemical processing operations. Industrial Applications Tank filling systems play crucial roles in chemical industry operations where component separation, mixture processing, and pure substance extraction are essential. These systems require careful analysis of two-phase interactions to optimize performance and ensure operational safety during fluid transfer processes. Multiphase Flow Analysis The simulation employs the Volume of Fluid (VOF) methodology to investigate the complex interactions between water and air phases during the filling process, providing insights into interface dynamics and pressure equilibration mechanisms. System Configuration and Geometric Design Reservoir Specifications The computational domain consists of two identical rectangular reservoirs, each measuring 1.25 meters in width by 2.5 meters in height. This configuration enables analysis of fluid transfer between tanks of equal capacity under gravitational influence. Two-Dimensional Model Development The geometric configuration was developed using ANSYS Design Modeler software, incorporating realistic tank dimensions and interconnecting pathways to simulate industrial filling operations. The design facilitates investigation of hydrostatic pressure effects and air-water interface behavior during the filling process. Computational Grid Configuration Structured Mesh Implementation The computational grid was generated using ANSYS Meshing software with a structured mesh topology containing 32,510 computational cells. The structured mesh approach provides enhanced accuracy for capturing the regular geometric features and ensures efficient computational performance for the transient simulation. Grid Quality Optimization The mesh density and distribution are optimized to accurately resolve the air-water interface movement and capture the pressure-driven flow phenomena throughout the filling process. CFD Simulation Configuration Fundamental Modeling Assumptions The simulation utilizes a pressure-based solver approach appropriate for incompressible flow conditions. The analysis is conducted in transient mode to capture the temporal evolution of the filling process and interface dynamics. Gravitational acceleration of -9.81 m/s² is applied along the negative y-axis to drive the filling phenomenon and establish hydrostatic pressure gradients. Multiphase Flow Modeling The Volume of Fluid model governs the two-phase flow field with air designated as the primary phase and water as the secondary phase. Sharp interface modeling ensures accurate tracking of the air-water boundary throughout the filling operation. The implicit formulation provides robust solution stability for gravitational flow applications with significant density differences between phases. Turbulence Modeling The realizable k-epsilon turbulence model with standard wall functions is employed to capture turbulent flow effects that may develop during the filling process. This approach provides accurate representation of turbulent mixing and energy dissipation while maintaining computational efficiency. Material Properties Air properties are defined with density of 1.225 kg/m³ and dynamic viscosity of 1.7894×10⁻⁵ Pa·s, representing standard atmospheric conditions. Water properties utilize density of 998.2 kg/m³ and dynamic viscosity of 0.001003 Pa·s, corresponding to water at standard temperature conditions. Boundary Condition Specifications The tank walls are configured as stationary wall boundaries with no-slip conditions to accurately represent the physical constraints. Inlet and outlet vents are specified as pressure boundaries with zero gauge pressure, allowing atmospheric pressure communication and air circulation during the filling process. The pressure profile multiplier is set to unity for standard atmospheric conditions. Numerical Solution Methods The pressure-velocity coupling employs the COUPLED algorithm for enhanced convergence in multiphase applications. Pressure discretization utilizes the PRESTO scheme, optimized for complex geometries with density variations. Momentum equations are solved using second-order spatial discretization for improved accuracy, while volume fraction transport employs the compressive scheme to maintain sharp interface definition. Turbulent kinetic energy and dissipation rate equations utilize first-order upwind discretization for numerical stability. Domain Initialization and Patching Standard initialization is applied throughout the computational domain with subsequent patching operations to establish initial water distribution. The water phase is patched with unity volume fraction in designated surface-body zones to represent the initial filling configuration. Temporal Solution Configuration The simulation employs a fixed time step size of 0.001 seconds with maximum of 20 iterations per time step to ensure convergence. The total simulation spans 10,000 time steps, providing comprehensive coverage of the filling process dynamics and pressure equilibration. Results and Flow Analysis Filling Process Characterization The simulation generates comprehensive two-dimensional contour visualizations depicting volume fraction distribution, pressure fields, velocity magnitude, and turbulent kinetic energy throughout the filling evolution. These results provide detailed insight into the complex multiphase flow phenomena occurring during tank filling operations. Interface Dynamics and Air Movement The volume fraction contours demonstrate the upward movement of the air phase as water fluid advances toward the tank containing air. This behavior illustrates the fundamental principle of fluid displacement and interface tracking accuracy of the VOF model throughout the filling process. Hydrostatic Pressure Equilibration The pressure field analysis reveals the establishment of hydrostatic pressure equilibrium at equal heights within both tanks after several seconds of simulation time. This phenomenon demonstrates adherence to fundamental fluid statics principles and validates the simulation’s physical accuracy. Engineering Insights The velocity and turbulent kinetic energy distributions provide valuable information about flow patterns, mixing characteristics, and energy dissipation during the filling process. These results enable optimization of tank filling operations and assessment of system efficiency in industrial applications. Temporal Evolution Analysis The animated results demonstrate the progressive nature of the filling process, showing how the air-water interface evolves over time and how pressure gradients develop to drive the fluid transfer between reservoirs.

Multi-Phase Tank Filling System CFD Analysis - ANSYS Fluent Simulation Project Overview This computational fluid dynamics investigation examines multi-phase tank filling operations using ANSYS Fluent software, focusing on the complex interactions between three distinct phases: water, alcohol, and air. The simulation addresses critical industrial applications in chemical processing where component separation and pure substance extraction from multi-component mixtures are essential operational objectives. Industrial Significance Tank filling systems with multiple fluid phases represent fundamental operations in chemical industry applications, particularly in separation processes where different fluid components must be isolated based on their physical properties. Understanding the interface dynamics and phase interactions enables optimization of separation efficiency and process design. Multi-Phase Flow Analysis The simulation employs the Volume of Fluid (VOF) methodology to investigate the complex three-phase interactions during the filling process. Surface tension effects between phase boundaries are incorporated through applied surface stresses, providing realistic representation of interfacial phenomena that govern phase separation behavior. System Configuration and Operational Parameters Tank Geometry and Inlet Configuration The computational domain consists of a cubic container with 1-meter side dimensions, providing adequate volume for comprehensive phase interaction analysis. Water injection occurs through a square valve measuring 10 cm on each side, positioned within the tank wall to facilitate controlled fluid entry at 1 m/s velocity. Initial Fluid Distribution The initial configuration establishes 40 cm of alcohol in the bottom portion of the container, creating a stratified system that allows investigation of density-driven phase separation and interface stability during the water injection process. Geometric Design and Computational Grid Three-Dimensional Model Development The computational domain was designed using Design Modeler software, incorporating realistic tank dimensions and inlet valve geometry to simulate industrial filling operations. The cubic configuration facilitates analysis of three-dimensional flow patterns and phase distribution throughout the filling process. Structured Mesh Implementation Grid generation utilized ANSYS Meshing software with structured mesh topology containing approximately 421,000 computational elements. The structured approach provides enhanced accuracy for capturing regular geometric features and ensures efficient computational performance for the complex three-phase transient simulation. CFD Simulation Configuration Fundamental Modeling Assumptions The simulation employs a pressure-based solver approach suitable for incompressible flow conditions with constant fluid properties. The analysis is conducted in transient mode to capture temporal evolution of phase interactions and interface dynamics. Gravitational acceleration of -9.81 m/s² along the negative y-axis drives density-stratified flow behavior and phase separation mechanisms. Multi-Phase Flow Modeling The Volume of Fluid homogeneous model governs the three-phase flow field equations for water, alcohol, and air phases. Sharp interface modeling ensures accurate tracking of phase boundaries throughout the filling operation. The explicit formulation provides computational efficiency while maintaining solution accuracy for the complex multi-phase system. Surface Tension Implementation Phase interaction modeling incorporates surface tension coefficients to accurately represent interfacial forces between different fluid pairs. The water-air interface utilizes a surface tension coefficient of 0.072 N/m, representing the strong interfacial tension typical of water-gas systems. The alcohol-water interface employs 0.043 N/m, reflecting the moderate interfacial tension between these miscible liquids. The air-alcohol interface uses 0.022 N/m, corresponding to the relatively weak interfacial forces between alcohol vapor and air. Material Properties Water properties are defined with density of 998.2 kg/m³, representing standard liquid water conditions. Alcohol properties utilize density of 790 kg/m³, typical of ethanol at standard conditions. Air properties employ density of 1.225 kg/m³, corresponding to standard atmospheric conditions. All thermodynamic properties are maintained constant throughout the simulation for simplified analysis. Turbulence Modeling The realizable k-epsilon turbulence model with standard wall functions captures turbulent flow effects during the injection and mixing processes. This approach provides accurate representation of turbulent energy dissipation and momentum transfer while maintaining computational efficiency for the multi-phase application. Boundary Condition Specifications The water inlet is configured as a velocity inlet with 1 m/s magnitude to provide controlled fluid injection. The tank outlet employs pressure outlet boundary conditions with zero gauge pressure to maintain atmospheric pressure communication. The cell zone condition specifies a mixture fluid type to accommodate the three-phase system throughout the computational domain. Numerical Solution Methods Pressure-velocity coupling utilizes the SIMPLE algorithm for iterative solution convergence in multi-phase applications. Pressure discretization employs the PRESTO scheme, optimized for complex geometries with significant density variations. Momentum equations use second-order upwind discretization for enhanced accuracy, while turbulent kinetic energy and dissipation rate equations employ first-order upwind discretization for numerical stability. Volume fraction transport utilizes the Geo-Reconstruct scheme to maintain sharp interface definition and accurate phase boundary tracking. Results and Engineering Analysis Phase Interaction Dynamics The simulation results demonstrate the complex three-phase interactions as water injection displaces the existing alcohol and air phases. Velocity contours at 1 and 15 seconds reveal the temporal evolution of flow patterns and phase redistribution throughout the filling process. Density-Driven Stratification The analysis shows how density differences between phases drive natural stratification, with the denser water phase settling toward the bottom and displacing the lighter alcohol phase upward. This behavior demonstrates the fundamental principles governing gravity-driven phase separation in industrial applications. Interface Tracking and Separation Potential The VOF model successfully captures the non-mixing behavior between water and alcohol phases, maintaining distinct phase boundaries throughout the filling process. This characteristic enables potential separation operations through strategically placed drainage valves at the tank bottom, as demonstrated by the clear phase stratification results. Engineering Applications The simulation provides valuable insights for designing industrial separation systems, including optimal injection velocities, tank geometries, and drainage configurations. The three-phase analysis enables assessment of separation efficiency and process optimization for chemical industry applications requiring component isolation from multi-phase mixtures. Temporal Evolution Analysis The velocity field evolution demonstrates how injection momentum affects phase distribution and mixing patterns, providing guidance for controlling separation effectiveness through operational parameter adjustment in industrial tank filling systems.