NACA0012 Airfoil, Compressible Flow, Paper Numerical Validation, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates compressible flow around a NACA0012 airfoil using ANSYS Fluent, with results compared and validated against the reference article "Numerical Simulation of Viscous Transonic Airfoil Flows."The geometry was designed in Design Modeler and meshed in ANSYS Meshing using a structured grid totaling 73,320 cells.MethodologySeveral assumptions were applied to the simulation: given the compressible nature of the flow, a density-based solver was used, the simulation was run under steady-state conditions, and gravitational effects were excluded.ConclusionComparing the simulation results against the reference article confirms the validity of the current approach. The predicted drag coefficient (CD) and lift coefficient (CL) closely match the experimental values reported in the paper:CoefficientExperimental ValueCFD ValueCD0.00790.00804CL0.2410.246This close agreement between the simulated and experimental values confirms that the current CFD setup accurately reproduces the viscous transonic airfoil flow behavior reported in the reference study.

Compressible Flow around an Aerial Structure, LES CFD Simulation, ANSYS Fluent TrainingDescriptionThis project simulates compressible flow around an aerial structure using ANSYS Fluent. The airflow moves horizontally at 300 K, Mach 5, and atmospheric pressure within the computational domain. The 3D geometry was designed in Design Modeler, representing the aerial structure positioned within a rectangular cube-shaped computational domain. Given the model's perfectly symmetrical structure, only half the geometry was modeled, with a symmetry condition applied along the symmetry plane.The domain was meshed in ANSYS Meshing using an unstructured grid totaling 5,515,041 elements.MethodologyA density-based solver was used to capture the airflow behavior, appropriate given the fully compressible nature of the flow. Density-based solution approaches are commonly applied to models involving acoustic waves, shock wave phenomena, airfoils in supersonic conditions, and any case where density variation plays a significant role.The computational domain's side walls were all assigned a pressure far-field boundary condition, with air defined as an ideal gas, so that density varies as a function of pressure, velocity, and temperature according to the ideal gas relationship. The simulation was run as unsteady (transient), with turbulence captured using Large Eddy Simulation (LES).ConclusionResults include 2D contours of pressure, velocity, temperature, density, and Mach number. The results clearly demonstrate the compressible nature of the airflow around the aerial structure, with air density varying substantially in response to the pressure and velocity changes occurring in the flow field surrounding the structure — confirming the strongly compressible behavior expected at this Mach 5 flow condition.

Acoustic in a Turbojet Intake Fan CFD SimulationDescriptionThis project simulates airflow inside a turbojet, examining the acoustic waves and sound generated within it using ANSYS Fluent. The incoming airflow is defined at a pressure of 85,416.92 Pa and a temperature of 283.9524 K, derived from the relevant governing equations.The model includes a turbojet fitted with a fan at its inlet, rotating at 2000 rpm about the X-axis. A dedicated airflow region surrounding the fan was defined and modeled using the Moving Reference Frame (MRF) approach to capture this rotational motion. The turbojet itself moves through the air at Mach 0.5 — since this exceeds the commonly used Mach 0.3 threshold for treating flow as compressible, the simulation accounts for compressibility accordingly, with a density-based solver applied and air density defined via the ideal gas law. The surrounding airflow domain was assigned a pressure far-field boundary condition at Mach 0.5.The 3D geometry was built in Design Modeler, consisting of the turbojet body with its internal fan positioned within a cylindrical computational domain representing the surrounding airflow. The region immediately around the fan was defined as an independent computational zone, allowing the fluid rotation induced by the fan to be captured through the frame motion method, while the full surrounding cylindrical domain carried the pressure far-field boundary condition. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 3,723,166 elements.MethodologyAcoustic behavior was modeled using the Broadband Noise Sources model, with reference values set to match standard air properties: a reference density of 1.225 kg/m³, a reference sound speed of 340 m/s, and a reference acoustic power of 1×10⁻¹² W.ConclusionResults include contours and vector fields for velocity, pressure, temperature, Acoustic Power Level (dB), and Surface Acoustic Power Level (dB) throughout the domain, offering detailed insight into the turbojet's acoustic behavior. As air strikes the fan and its surrounding wall, the resulting acoustic parameters become most clearly defined in the region immediately downstream of the fan, with the Surface Acoustic Power Level concentrated along the fan surface itself — identifying it as the dominant noise source in this system.Plots of Acoustic Power Level and Surface Acoustic Power Level taken along the domain's centerline further clarify the precise magnitude and distribution of acoustic activity downstream of the fan, providing a clear quantitative picture of how the fan's rotation drives the turbojet's overall acoustic signature.

Solid Fuel Ramjet Engine, SFRJ, CFD Simulation, ANSYS FluentDescriptionA solid fuel ramjet (SFRJ) is a propulsion system that generates thrust by compressing ram air, functioning as an air-breathing engine that burns solid fuel using atmospheric oxygen rather than carrying an onboard oxidizer. Its structure typically consists of an air inlet, a combustion chamber where solid fuel burns, and a nozzle through which exhaust gases are released to produce thrust.Operating on the ram effect, an SFRJ's forward motion compresses incoming air before combustion occurs, following four key stages: air intake (ram action drives air into the inlet as the engine advances), compression (incoming air compresses as the engine moves forward), combustion (compressed air enters the combustion chamber, where its heat ignites the solid fuel), and thrust production (the resulting hot expanding gases are expelled through the rear nozzle, propelling the engine forward). Given their high efficiency and speed, SFRJs are widely used across aerospace and defense applications.Fuel flow within an SFRJ can be either subsonic or supersonic, determined by the Mach number at the engine's entry point. When the fuel-oxidizer mixture in the combustion chamber travels below the speed of sound (Mach < 1), pressure changes can propagate upstream against the flow direction — meaning downstream conditions can influence upstream behavior, directly affecting combustion efficiency and resulting thrust.The 2D geometry was built in Design Modeler and meshed in ANSYS Meshing, totaling 35,224 elements.MethodologyThis project simulates an SFRJ using hydroxyl-terminated polybutadiene (HTPB) as the solid fuel, with inlet air treated as subsonic flow at a Mach number of 0.9. The model features two inlets — one for air and one for fuel — with the diffuser's cone half-angle set to 20 degrees. Air inlet velocity was calculated from the Mach number using:V_inlet = M × √(γ × R × T)where γ (Cp/Cv) equals 1.4 for air, R is the specific gas constant (287 J/kg·K for air), and T is the absolute temperature. This yielded an air inlet velocity of 311.5 m/s, corresponding to a mass flow rate of 0.94 kg/s.Air enters through the diffuser inlet while HTPB enters through the fuel inlet into the combustion chamber, where the two react according to the defined combustion reaction. The Species Transport model governed this combustion process, with both the volumetric and dissipation rate options activated within the species model. Turbulence was resolved using the Realizable k-epsilon model, chosen as well suited to this flow configuration.ConclusionModern SFRJ engines can reach chamber temperatures of up to 2900 K. This simulation predicted a maximum combustion chamber temperature of 1485 K — a reasonable and desirable result for this configuration.Temperature and velocity contours illustrate the combustion process throughout the chamber, while oxygen and CO₂ mass fraction contours reveal how reactants are consumed and products distributed as the reaction proceeds — together confirming that the simulation captures the expected combustion behavior and thrust-generating mechanism central to SFRJ operation.

Turbocharger Performance CFD Simulation, ANSYS Fluent Training: (BladeGen+TurboGrid)DescriptionTurbochargers play a crucial role in improving engine performance and efficiency across a wide range of applications. This study explores the fluid dynamic behavior and thermal characteristics of a turbocharger using CFD analysis, focusing on a one-sixth periodic section of the device to capture the complex flow patterns and temperature distributions within it.The primary objective is to analyze how the turbocharger's geometry shapes flow patterns, pressure distribution, and temperature gradients, gaining insight into the device's overall performance and its resulting impact on engine efficiency.Geometry & MeshThe turbocharger geometry was generated using ANSYS Vista CCD in combination with BladeGen, enabling accurate definition of the turbomachinery components. Meshing was performed in ANSYS TurboGrid, producing a high-quality structured mesh suited to rotating machinery simulations, totaling 972,176 cells — sufficient to capture the key flow features while maintaining computational efficiency.MethodologyThe simulation used a pressure-based solver, with air modeled as an ideal gas to capture the compressible flow behavior typical of turbocharger operation. A steady-state approach represented continuous operation under constant inlet conditions, with the SST k-omega turbulence model applied to accurately resolve the complex flow structures within the device, and the energy equation enabled to capture temperature distribution and heat transfer.To reduce computational cost without sacrificing accuracy, periodic boundary conditions were applied, allowing simulation of only one-sixth of the full geometry — accordingly, the total mass flow rate of 0.1 kg/s was scaled to 0.01666666 kg/s for this reduced domain. The inlet temperature was set to 350 K, with the Frame Motion model used to simulate blade rotation at 92,000 rpm, accurately capturing the turbocharger's characteristic high-speed operation.ConclusionThe results reveal how the turbocharger impeller accelerates flow through the blade passages, generating localized high-velocity regions near the blade leading edges and flow channels, while lower velocities persist near the hub and blade surfaces. Pressure contours show a clear pressure rise across the impeller, with higher pressure concentrated on the pressure side and outlet region and lower pressure near the inlet and suction side — confirming effective energy transfer and compression of the fluid as it passes through the turbocharger, consistent with the device's intended performance behavior.

Multi-Stage Axial Compressor CFD Simulation, ANSYS Fluent TutorialDescriptionAn axial compressor is a gas compressor capable of continuously pressurizing gas, using a rotating, airfoil-based design in which the working fluid flows primarily parallel to the axis of rotation. Axial compressors consist of both rotating and stationary components: a central drum, driven by a shaft and supported by bearings within a stationary tubular casing, carries rows of airfoils alternating between the drum and the casing.A pair consisting of one row of rotating airfoils (blades/rotors) followed by one row of stationary airfoils (vanes/stators) forms a stage. The rotating blades accelerate the fluid in both the axial and circumferential directions, while the stationary vanes convert this increased kinetic energy into static pressure through diffusion, redirecting the flow to prepare it for the next stage's rotor. The cross-sectional area between the rotor drum and casing progressively narrows along the flow direction, maintaining an optimal Mach number as the fluid compresses.This project models a compressor with 4 stages (2 rotors, 2 stators), each featuring 120 blades, representing a high-performance, high-pressure compressor under realistic operating conditions. The 3D geometry was designed in ANSYS BladeGen, with the domain defined by a mass flow inlet and a pressure outlet. The domain was meshed in TurboGrid using an unstructured grid totaling 11,430 elements.MethodologyThe simulation used a pressure-based solver, with turbulence modeled using the k-omega SST model, run at the compressor's operational point with a mass flow rate of 110 kg/s. Compressor rotation was modeled using the MRF method, with each stage rotating at 10,000 rpm, and the Turbo Workflow module was used to streamline the overall compressor modeling process.ConclusionThe Mach number contour clearly shows how flow speed changes through each passage, dropping progressively at each stage, while the pressure contour shows pressure rising correspondingly after each stage, yielding an overall pressure ratio of 4 across the full compressor.The velocity triangles generated by the rotating rotors are clearly visible in the results, and the pressure contour on the blades reveals regions of flow stagnation and separation throughout the system. Using the software's workflow efficiency calculation, the compressor's overall efficiency was determined to be 20%, with the corresponding pressure drop also reflected in the pressure contour results.

IntroductionThis study investigates the aerodynamic performance of a turbine vane using Computational Fluid Dynamics (CFD) simulations with two different turbulence models. The simulations were conducted using ANSYS Fluent, comparing the k-Omega SST and Large Eddy Simulation (LES) turbulence models. The study aims to analyze the flow characteristics and turbulence effects within the turbine vane passage, providing insights into the relative strengths and differences between these two popular turbulence modeling approaches.The geometry of the turbine vane was created using ANSYS Design Modeler. The computational domain was then discretized using ANSYS Meshing, resulting in a high-resolution structured mesh of 12,634,415 elements, ensuring accurate capture of the complex flow features around the vane.MethodologyThe simulation of Turbine Vane setup included a pressure-based solver in steady-state conditions. The energy equation was enabled to account for compressibility effects. Air was modeled as an incompressible ideal gas, entering from the left boundary and exiting through the right vertical boundary.Two separate simulations were performed using identical geometry and mesh, but with different turbulence models:k-Omega SST (Shear Stress Transport) modelLarge Eddy Simulation (LES) modelThese models were chosen to compare a widely-used RANS (Reynolds-Averaged Navier-Stokes) approach (k-Omega SST) with a more computationally intensive but potentially more accurate scale-resolving method (LES).ResultsThe CFD simulations using the k-Omega SST and LES turbulence models provided detailed insights into the aerodynamic performance of the turbine vane. The results are summarized as follows:Density DistributionThe density contours indicate a variation in density across the vane passage. The highest density regions are observed at the leading edge, where compression effects are significant. The k-Omega SST model shows slightly smoother density gradients compared to the LES model, which captures more detailed fluctuations, particularly in the wake region.Static Pressure DistributionThe static pressure contours reveal high-pressure regions at the leading edge and low-pressure regions at the trailing edge of the vane. The LES model demonstrates a more pronounced pressure drop across the vane, capturing finer details of the pressure distribution, especially in the wake, compared to the k-Omega SST model.Velocity MagnitudeVelocity contours show acceleration of flow around the vane, with maximum velocities occurring near the trailing edge. The LES model captures higher velocity gradients and more complex flow structures, suggesting better resolution of turbulent eddies compared to the k-Omega SST model.Turbulent IntensityThe turbulent intensity contours highlight regions of high turbulence, particularly downstream of the vane. The LES model predicts higher turbulent intensity levels, indicating its capability to resolve smaller scale turbulence structures, while the k-Omega SST model provides a more averaged view.Streamlines and Vector FieldsStreamlines and velocity vectors illustrate the flow path and direction through the vane passage. The LES model shows more intricate flow patterns and vortex formations, providing a detailed visualization of the flow dynamics, whereas the k-Omega SST model presents a more streamlined flow with less complexity.Overall, the LES model offers a more detailed and accurate representation of the flow characteristics and turbulence effects within the turbine vane passage. However, this comes at the cost of higher computational resources compared to the k-Omega SST model, which provides a good balance between accuracy and computational efficiency.

Multi-Stage Axial Gas Turbine CFD Simulation, ANSYS Fluent TrainingDescriptionAn axial turbine is a turbine in which the working fluid flows parallel to the shaft, as opposed to radial turbines, where fluid moves around the shaft (as in a watermill). Structurally similar to an axial compressor, an axial turbine operates in reverse, converting the flow of a fluid into rotating mechanical energy rather than adding energy to the fluid.A set of static guide vanes, or nozzle vanes, accelerates and imparts swirl to the fluid, directing it toward the next row of turbine blades mounted on the rotor. Axial-flow turbines are the most widely used type for compressible fluid applications, with their gas turbine implementation emerging as a direct outgrowth of earlier steam turbine technology. As gas turbine inlet temperatures have trended progressively higher in recent years, various cooling schemes have become essential, and axial-flow turbines are now designed with a high work factor to reduce fuel consumption and turbine noise. Turbine blade and vane cooling technology has advanced substantially — from early air and steam cooling methods to modern blade structures engineered to eliminate both transverse and linear grain boundaries, enabling operation at very high temperatures.This project models a turbine with 4 stages (2 rotors, 2 stators), each featuring 130 blades, representing a high-performance, high-pressure turbine under realistic operating conditions. The 3D geometry was designed in ANSYS BladeGen, with the domain defined by a mass flow inlet and a pressure outlet. The domain was meshed in TurboGrid using an unstructured grid totaling 12,182 elements.MethodologyThe simulation used a pressure-based solver, with turbulence modeled using the k-omega SST model, run at the turbine's operational point with a mass flow rate of 520 kg/s. Turbine rotation was modeled using the MRF method, with each stage rotating at 6000 rpm, and the Turbo Workflow module was used to streamline the overall turbine modeling process.ConclusionThe Mach number contour clearly shows flow speed increasing progressively through each stage's passages, while the pressure contour shows a corresponding pressure drop after each stage, yielding an overall pressure ratio of 0.8 across the full turbine.The velocity triangles generated by the rotating rotors are clearly visible in the results, and the pressure contour on the blades reveals regions of flow stagnation and separation throughout the system. Using the software's workflow efficiency calculation, the turbine's overall efficiency was determined to be 18%, consistent with the expected performance behavior of a multi-stage axial turbine operating under these conditions.

IntroductionThis study investigates the flow dynamics within a steam turbine configuration using Computational Fluid Dynamics (CFD) analysis. The simulation aims to understand the complex flow behavior, pressure distributions, temperature variations, and velocity profiles within the turbine. By employing advanced models for turbulence and real gas behavior, this research provides valuable insights into the performance and efficiency of steam turbines.The CFD simulations were conducted using ANSYS Fluent software. The geometry, which is designed in Spaceclaim, represents a 3-dimensional steam turbine configuration, with the mesh generated in ANSYS Meshing. The mesh consists of about 19 million tetrahedral elements, providing a high-quality representation of the complex turbine geometry.MethodologyA density-based solver is employed for the solution and is suitable for compressible flow simulations. For turbulence modeling, the Realizable k-ε model with standard wall function is utilized, known for its robustness in predicting complex turbulent flows. The energy equation is enabled to solve for temperature distributions and heat transfer effects. The working fluid is air, with real-gas-peng-robinson model is employed for compressible flow, and a moving reference frame is activated for the rotating zone at 100 rpm to capture rotational effects. We employ an implicit formulation for numerical stability and efficient convergence.ResultsThe simulation results demonstrate the intricate flow behavior within the steam turbine. The pressure and temperature distributions provide insights into the energy conversion process, while the velocity profiles reveal the flow acceleration through the blade passages. At the Inlet, the pressure is higher than in other regions, and it gradually decreases toward the outlet. In the temperature contour, you can see that the temperature is also highest near the Inlet. Between the blades, the temperature reaches its minimum, and in this region, the pressure is also low. For the velocity contour, you can see that in the same region where the pressure and temperature are lowest, the velocity is higher than in other areas. This behavior is consistent with the flow acceleration through the blade passages.

Wet Steam for Condensation inside a Steam Ejector, ANSYS FluentDescriptionThis project simulates the steam condensation process occurring within an ejector using ANSYS Fluent. An ejector is a mechanical device that uses an actuator (driving) fluid to draw in a secondary material — the two actuator fluids and the suction substance ultimately mix and exit together as a single stream. Ejectors serve two primary functions: vacuuming/suctioning gases and mixing fluids.Structurally, an ejector takes the form of a convergent-divergent tube. As the driving fluid enters and passes through the nozzle's converging section, the reducing cross-sectional area increases flow velocity according to the continuity equation — effectively converting the fluid's potential energy into kinetic energy. Per Bernoulli's principle, this increasing velocity corresponds to a drop in fluid pressure, which in turn drives the suction effect central to the ejector's operation.The 2D geometry was designed in Design Modeler, representing an axisymmetric plane of the ejector: 0.411 m in length, with a 0.02 m wide exit, a 0.0038 m wide first entrance, and a 0.0165 m wide second entrance. The model's lower edge was defined as an axis of rotation, allowing the 2D geometry to represent the full 3D structure — a simplification made possible by the ejector's perfectly symmetrical geometry, reducing computational cost significantly.The domain was meshed in ANSYS Meshing using a structured grid totaling 25,984 cells.MethodologyThe Wet Steam multiphase model solves two coupled sets of transport equations: the mass fraction of the condensed liquid phase, and the number/concentration of droplets per unit volume. This phase-change model captures the formation of liquid droplets during a homogeneous, non-equilibrium condensation process, based on classical non-isothermal nucleation theory.As superheated dry steam rapidly expands through the ejector, it cools and forms a nucleating core — ultimately producing a two-phase mixture of saturated steam and liquid droplets known as wet steam. A density-based solver was used throughout to capture this compressible, phase-changing flow.ConclusionThe resulting pressure drop generates a compressive vacuum within the ejector, drawing the secondary material into the flow. The primary driving fluid and the entrained secondary material mix and compress together within the diffuser section downstream.Results include 2D contours of pressure, velocity, temperature, turbulent kinetic energy, and the rate of liquid mass generation (equivalent to the condensation rate) — with 3D contours obtainable by rotating these 2D results around the central axis. The liquid mass generation rate stands out as one of the most important results, offering direct insight into how effectively and where condensation occurs throughout the ejector's convergent-divergent geometry.