DescriptionTransonic flow over an airfoil sits at the heart of compressible flow modeling, where local flow velocities approach and exceed the speed of sound, producing complex shock and expansion phenomena that govern aircraft aerodynamic performance. This CFD simulation uses ANSYS Fluent to model compressible airflow over a Naca 0012 airfoil in three dimensions, capturing transonic behavior at a Mach number of 0.7, an air temperature of 300 K, and a 2-degree angle of attack.MethodologyA two-dimensional airfoil geometry is constructed with multiple zone divisions in ANSYS Design Modeler and extruded to form a three-dimensional computational domain. The domain is discretized in ANSYS Meshing using a structured grid of over 1.5 million cells. The simulation employs a pressure-based solver configured for compressible flow, using the Coupled pressure-velocity coupling algorithm, ideal-gas behavior for air density, and the Sutherland model to capture temperature-dependent viscosity. The setup runs as a steady-state simulation appropriate for transonic flow conditions.Results AnalysisPost-processing examines velocity and pressure distributions around the airfoil to characterize the transonic flow field, including the effects of the 2-degree angle of attack on flow characteristics. The relationship between Mach number, local velocity, and pressure is analyzed to identify regions of compressibility effects and potential shock formation. These results provide insight into aerodynamic behavior in the transonic regime, relevant to aircraft design, aerospace research, and the broader study of compressible flow phenomena.

DescriptionThe Lockheed Martin F-35 Lightning II is an American family of single-seat, single-engine, all-weather stealth multirole combat aircraft, also capable of electronic warfare and intelligence, surveillance, and reconnaissance missions.Notably, the F-35 can reach speeds of around 500 m/s, placing the surrounding airflow firmly in the supersonic regime. Since full-scale wind tunnel experiments at these conditions are costly in both time and money, CFD solvers are frequently used for initial evaluation. This project studies the supersonic, compressible flow around an F-35 aircraft. The geometry consists of a 20 m F-35 positioned inside a 150 m wind tunnel.The mesh contains 7,182,542 elements. In terms of quality, a maximum skewness of 0.79 with an average of 0.22 is satisfactory for this problem. To resolve the boundary layer accurately, 25 prism layers were added adjacent to both the wind tunnel walls and the aircraft body. The mesh was generated in ANSYS Meshing and subsequently converted to a polyhedral mesh within ANSYS Fluent, reducing the count to 1,845,364 elements while preserving the same quality. As with any numerical study, the first step in the modeling was the creation of the CAD geometry.MethodologyAir is treated as a compressible ideal gas, and a Mach number of 2.0 is reached at the maximum speed of 544 m/s. Solving this problem requires the flow equations to be handled in their differential form.A non-isothermal, compressible ideal-gas condition was assumed inside the wind tunnel; consequently, the energy equation was solved alongside the flow and turbulence equations. The governing mass and momentum equations are written in their standard conservative form for compressible flow.ConclusionThe drag force and the shock profiles were obtained over the course of the study. After the solution converged, the results were examined through post-processing. As a check on convergence, the drag value was monitored throughout the solution iterations: the solution was deemed converged once the drag force settled to a constant value and the residuals dropped below 10⁻⁶.The results are then presented for the pressure and velocity fields. The shock profile is visible in both the pressure and Mach number contours, while the velocity field is shown through both contours and streamlines to give deeper insight into the flow. The temperature gradient and its variation across different locations are also presented, since the temperature rise is an important factor in compressible aerodynamic calculations. Finally, the drag force was calculated at 181.66 kN, a reasonable value for a 20 m aircraft with the stated specifications.

DescriptionThis project simulates an aerospike using ANSYS Fluent. The goal is to model a nose cone fitted with an aerospike and to study how it reshapes the shock wave — pushing it away from the nose cone — and thereby reduces drag. Because the vehicle travels at supersonic speed, the flow is fully compressible, and the analysis centers on resolving the shock structure that forms ahead of the body — a defining feature of compressible flow.A drag-reducing aerospike is a device used to lower the forebody pressure drag of blunt bodies at supersonic speeds. The aerospike creates a detached shock ahead of the body, and a zone of recirculating flow forms between the shock and the forebody. This recirculation zone acts like a more streamlined forebody profile, which reduces the aerodynamic drag.The geometry was created in ANSYS Design Modeler, and meshing was performed in ANSYS Meshing using an unstructured grid with a total of 153,987 cells. The figure below shows an overview of the mesh.MethodologyThe simulation uses the k-omega SST turbulence model, with the flow set to Mach 2. At this speed the air must be treated as a compressible medium, so the solver captures the density variations and shock waves that govern the aerodynamics of the aerospike.ConclusionOn completion of the solution, two-dimensional velocity contours and an animation were obtained. The results show that the aerospike produces a much thinner and weaker oblique shock, positioned well away from the cone. Because the pressure gradient of the primary shock wave now sits far from the body, the drag is reduced.A small separation region also forms on the cone itself, and the reattachment of the separated flow together with the oblique shock occurs at some distance from the main body — which reduces the aerothermodynamic efficiency. Overall, the simulation illustrates a classic compressible-flow phenomenon: how modifying the shock structure ahead of a supersonic body can achieve meaningful drag reduction on a blunt forebody.

DescriptionThis simulation examines flow through a rocket engine nozzle in ANSYS Fluent, a compressible flow problem central to propulsion design. A rocket nozzle works by converting the internal energy of hot propellant gases into directed kinetic energy, producing a high-velocity exhaust jet; the gases enter subsonic, are forced to accelerate as the passage narrows toward the throat, and reach sonic velocity exactly at that minimum cross-section. Beyond the throat, the diverging section allows the gas to keep expanding and accelerating, pushing the flow to supersonic exit speeds. The geometry is built as a 2D convergent-divergent nozzle in Design Modeler, capturing the interior flow path through the throat and diverging sections, and meshed in ANSYS Meshing with an unstructured grid of 69,342 cells.MethodologyBecause the flow is compressible and spans subsonic-to-supersonic regimes across the nozzle, the density-based solver is used, with gas density computed from the ideal gas law rather than treated as constant. The nozzle inlet is set to a gauge pressure of 2,268,000 Pa, and the outlet to 39,365 Pa, a large pressure ratio that drives the expansion and acceleration through the throat.AnalysisThe solution produces 2D contours of temperature, pressure, velocity, density, and enthalpy, along with streamlines through the nozzle. These fields confirm the expected compressible-flow behavior: the gas accelerates sharply as it passes through the throat, and this acceleration is accompanied by a corresponding pressure drop as the cross-sectional area shrinks, consistent with the physics of convergent-divergent nozzle flow.

Aircraft Propeller Using Mesh Motion — ANSYS Fluent CFD SimulationDescriptionThis project analyzes the thrust and lift generated by a rotating propeller and its effect on an aircraft fuselage using ANSYS Fluent, with the Mesh Motion (moving mesh) technique as the central theme. A propeller converts the rotational power of an engine into thrust: its twisted blades act like small rotating wings, producing an aerodynamic force that resolves into a component along the aircraft axis — the propulsive thrust — and a component in the plane of the blades — the torque. Reproducing this behavior in CFD requires the propeller region to physically rotate within the simulation, and the moving-mesh approach is what makes that possible. Within the Rotary Equipment: Beginner CFD Training Package, this project introduces the Mesh Motion method on an external rotating body, moving beyond the steady MRF approach to a fully transient, physically rotating simulation.MethodologyThe aircraft and propeller geometry was designed in SolidWorks and imported into ANSYS Meshing for grid generation and boundary naming. The mesh was first built with tetrahedral elements and then converted to a polyhedral mesh within Fluent, which yields fewer cells and higher quality — 3,812,519 elements for the tetrahedral mesh and 692,023 for the polyhedral mesh. The model is divided into two zones, rotational and stationary, which is the defining structure of a mesh-motion simulation. A cylindrical rotating domain sized at 1.12 propeller diameters surrounds the impeller and is meshed more finely, reflecting the greater importance of the blade region to the results. This rotating domain sits inside the fixed outer zone, and the two are connected through an interface that transfers flow quantities between them. The Mesh Motion method makes the rotating domain physically spin about the impeller axis, directly capturing the propeller's rotation, and a transient solver is used to resolve the resulting time-dependent flow. To scale the simulation correctly, the advance ratio is used as the governing similarity parameter: with an impeller diameter of 0.0532 m and a rotational speed of 1800 rpm (30 rad/s), an advance ratio of J = 1.225 corresponds to a flow velocity of 2 m/s, providing a consistent basis for simulating the propeller across different scales by holding the advance ratio fixed.AnalysisThe results yield the drag and lift on the fuselage together with the thrust and torque on the propeller, presented in the accompanying diagrams, along with contours, vectors, and flow lines that reveal the flow physics around the aircraft and blades. The study shows that, by respecting the advance ratio for each propeller, working points can be defined through the relationship between flow velocity and rotational speed. For a fully rigorous match, additional criteria are needed — in particular the Reynolds number based on both the impeller speed and the flow velocity — and a valid scaled simulation requires that the computed Reynolds number exceed the critical value for that propeller. On that basis the model can represent real propeller operating points. By the end of this project, you'll be able to split a domain into rotating and stationary zones joined by an interface, set up the Mesh Motion method with a transient solver to capture the genuine rotation of a propeller, apply the advance ratio as a scaling parameter, and interpret the thrust, torque, and aerodynamic loads the propeller produces.

Movement of Golf Ball, Impact (Dynamic Mesh) — ANSYS Fluent CFD SimulationDescriptionThis project simulates the motion of a golf ball driven by an impact force of 200 N applied at an angle of 30°, determining the ball's flight path with ANSYS Fluent. The central theme is dynamic mesh modeling: rather than holding the ball fixed in a steady stream, the simulation lets the ball move freely through the domain in response to the aerodynamic and impact forces acting on it, and the computational mesh deforms and regenerates to follow that motion. Within the Dynamic Mesh: Beginner CFD Training Package, this project builds on the basic prescribed-motion case by letting the body's trajectory be computed from the forces acting on it, introducing free flight through a fluid.MethodologyThe model is three-dimensional, with the golf ball placed inside a surrounding flow domain created in Design Modeler. Meshing was carried out in ICEM, producing a grid of more than 945,765 cells. Because the ball moves and its trajectory evolves in time, a transient solver is used so that the displacement of the ball can be tracked as a function of time. Dynamic mesh is what makes the free motion possible, and it is the core of the methodology: as the ball travels, the cells around it stretch and distort, so their quality degrades over time. To keep the solution stable and accurate, the smoothing and remeshing sub-models are enabled — smoothing adjusts node positions to relieve distortion, while remeshing rebuilds cells locally whenever their quality falls below acceptable limits. The six-degrees-of-freedom (6-DOF) solver governs the ball's movement, allowing all possible translational and rotational motions to be computed from the forces acting on it — here initiated by the 200 N impact. For the turbulence field, the SST k-ω model is applied, chosen for its strong performance both near the ball's surface and in the surrounding free stream.AnalysisAfter solving, the simulation yields two- and three-dimensional contours of pressure and velocity at successive flow times, capturing how the flow field evolves as the ball moves. The pressure contours show a region of elevated pressure at the front of the ball — the stagnation point where the flow is brought to rest against the surface — and a region of reduced pressure at the rear, marking the wake where the flow separates from the ball. By the end of this project, you'll be able to set up a dynamic-mesh simulation with 6-DOF motion, configure smoothing and remeshing to preserve mesh quality as a body moves, apply the SST k-ω model for external aerodynamics, and interpret the time-dependent pressure and velocity fields that shape a body's free flight through a fluid.

IntroductionThis report presents the computational fluid dynamics (CFD) simulation of the Switchblade 300 drone using ANSYS Fluent. The primary objective was to analyze the fluid dynamics around the geometry under the specified conditions of motion and environmental parameters.The geometry of the Switchblade 300 was created in SpaceClaim. Using ANSYS Meshing, a non-conformal mesh of approximately 40 million tetrahedral cells was initially generated. This dense mesh provided high resolution of the flow features around the geometry and was well suited to capturing the intricate details of the flow.However, the initial 40,000,000-element mesh presented significant computational challenges. To optimize the simulation, the mesh type was converted to polyhedral, which dramatically reduced the element count from 40,000,000 to 7,000,000 while preserving the necessary resolution. Figure 2 shows the mesh configuration, highlighting the non-conformal mesh regions essential for accurately capturing the fluid interactions.MethodologyThe simulation was performed in ANSYS Fluent, making use of its Mesh Motion capability to analyze the flow characteristics of the rotating geometry. The setup specified a velocity inlet of 28.05 m/s, an angle of attack of 5 degrees, and a rotational speed of 5000 RPM. The k-ω SST turbulence model was selected for its effectiveness in predicting boundary layer separation and handling complex flow dynamics. For the numerical methods, the SIMPLE algorithm was used for pressure-velocity coupling, and standard initialization was applied to set the initial flow conditions within the solution domain.ResultsVelocity Contour — Presented in Figure 3, the velocity contour depicts the flow around the Switchblade 300, identifying the regions of high- and low-speed flow that arise from the angle of attack and the rotation of the geometry.Pressure Contour — Shown in Figure 4, the pressure contour illustrates the varying pressure distribution across the geometry, which is critical for identifying the aerodynamic forces — such as lift and drag — acting on the drone.ConclusionThe simulation results provide a detailed understanding of the aerodynamic performance of the Switchblade 300, offering significant insight into how rotational speed, inlet velocity, and angle of attack influence the overall flow behavior. These findings support the optimization of the design and operating parameters to enhance performance. Further simulations under varied conditions are recommended to explore additional operational scenarios and improve predictive accuracy.

DescriptionThis CFD project investigates the aeroacoustic behavior of air flowing past a cylinder, using the Wave Equation acoustic model in ANSYS Fluent to characterize the resulting sound field. As air flows around the cylinder, unsteady vortex shedding in the wake generates pressure fluctuations that radiate outward as sound, a classic aeroacoustic phenomenon relevant to noise prediction around bluff bodies such as struts, cables, or landing gear components in aerospace applications. The two-dimensional geometry was constructed in ANSYS Design Modeler, and a structured mesh consisting of 23,264 elements was generated in ANSYS Meshing to accurately resolve both the near-wall flow behavior and the acoustic field around the cylinder.MethodologyThe simulation was performed in transient (unsteady) mode, since capturing the time-varying flow structures responsible for sound generation requires resolving flow behavior at each time step rather than a steady-state average. A pressure-based solver was employed, consistent with the low-speed, incompressible flow regime of the working fluid. The Wave Equation acoustic model was then applied on top of the resolved flow field to compute how the pressure fluctuations generated by vortex shedding propagate as acoustic waves through the domain. This is a widely used two-step, hybrid aeroacoustic approach, where the flow field is solved first and the acoustic field is derived from it separately, rather than solving both simultaneously.AnalysisAt the conclusion of the simulation, sound pressure levels were extracted at defined receiver points placed in the domain, allowing the acoustic response to be evaluated at specific locations of interest. The acoustic source data was exported in ASD, or Acoustic Source Data, format, enabling further post-processing or use in downstream acoustic analysis tools. The frequency spectrum captured extended up to 100,000 Hz, with the results showing that the maximum sound pressure levels occurred at frequencies below 10,000 Hz, a pattern consistent with the dominant tonal noise typically produced by periodic vortex shedding off a cylinder, often referred to as an Aeolian tone, rather than broadband high-frequency turbulent noise.

Ffowcs Williams & Hawkings (FW-H) Acoustic Model — ANSYS Fluent CFD SimulationDescriptionThis project explores the Ffowcs Williams & Hawkings (FW-H) acoustic model in ANSYS Fluent, one of the most powerful acoustic-simulation techniques available in modern CFD. The FW-H model is the standard method for predicting the far-field noise radiated by a flow — it takes the unsteady flow near a body and propagates the resulting sound out to distant receivers. This project applies it to a fundamental problem in aeroacoustics: the noise induced by airflow around a cylinder. Within the Acoustics: Beginner CFD Training Package, this project introduces the workhorse aeroacoustic method, building on the Wave Equation model toward the standard tool for far-field noise prediction used throughout the applied cases that follow.MethodologyThe optimized 2D geometry is created in ANSYS Design Modeler and meshed in ANSYS Meshing with a structured grid of 23,264 elements suited to acoustic simulation. A transient analysis is set up to capture the time-dependent acoustic behavior, using a pressure-based solver for the incompressible flow, with the Ffowcs Williams & Hawkings acoustic model implemented to predict the radiated sound. The setup supports extracting sound-pressure levels, analyzing A-weighted acoustic pressure, and performing Fourier transforms for frequency-domain analysis.AnalysisPost-processing focuses on the sound-pressure levels, interpreted in the frequency domain to understand how sound energy is distributed across frequencies. The A-weighted acoustic pressure is evaluated to tailor the data to human hearing perception and identify the critical frequency ranges for human-centric acoustic design, while the spatial distribution of acoustic pressure at varying distances from the source illustrates the principles of acoustic attenuation. This kind of analysis is essential across aerospace (aircraft noise reduction), automotive design (vehicle aeroacoustics), wind-turbine development, and environmental noise assessment. By the end of this project, you'll be able to set up a transient FW-H acoustic simulation, extract and interpret sound-pressure levels and A-weighted data, perform frequency-domain analysis, and evaluate how noise attenuates with distance from the source.

Broadband Noise Sources Acoustic Model — ANSYS Fluent CFD SimulationDescriptionThis project explores the Broadband Noise Sources acoustic model in ANSYS Fluent, a valuable technique for identifying where noise is generated in a flow. Unlike the transient FW-H and Wave Equation methods, the Broadband Noise Sources model gives a quick, steady picture of the acoustic source distribution — a low-cost way to locate the regions responsible for noise before committing to a full transient simulation. This project applies it to a fundamental problem in aeroacoustics: the noise induced by airflow around a cylinder. Within the Acoustics: Beginner CFD Training Package, this project completes the trio of core acoustic models, adding the source-identification method to the propagation-based Wave Equation and FW-H models taught before it.MethodologyThe optimized 2D geometry is created in ANSYS Design Modeler and meshed in ANSYS Meshing with a structured grid of 23,264 elements suited to acoustic simulation. The analysis uses a pressure-based solver for the incompressible flow, with the Broadband Noise Sources model implemented for a comprehensive picture of the acoustic sources. The setup supports extracting acoustic power-level contours and analyzing the LEE-Self noise and LEE Shear-noise source distributions, as well as comparing the noise generation with and without the cylinder obstruction.AnalysisPost-processing focuses on the acoustic power-level contours, interpreted to understand the relationship between acoustic pressure and decibel levels and to locate the critical areas of noise generation around the cylinder. The model distinguishes between the LEE-Self noise and LEE Shear-noise sources, clarifying the mechanisms responsible for the noise, and comparing the cases with and without the cylinder reveals the impact of a flow obstruction on noise generation. This kind of analysis is essential across aerospace, automotive design, wind-turbine development, and HVAC optimization. By the end of this project, you'll be able to set up a Broadband Noise Sources simulation, extract and interpret acoustic power-level contours, distinguish between noise-source types, and identify where noise originates in a flow — a fast, practical basis for noise-reduction design.