Aerodynamics Projects
Explore guided student projects in Aerodynamics. Build hands-on skills with real aerospace tools and data.
11 projects
Combine with other filters →Design and Test a Wing in XFLR5
See how airfoil shape controls lift, drag, and stall
Use XFLR5, the open-source airfoil analysis tool, to design a wing and analyze its aerodynamic performance. Understand lift, drag, and stall behavior through real engineering software.
Start Project →CFD Analysis of an Airfoil with OpenFOAM
Simulate fluid flow over a wing and validate against published data
Set up and run a complete CFD simulation of airflow over an airfoil using OpenFOAM. Generate pressure distributions, velocity fields, and validate your results against wind tunnel data.
Start Project → AI/MLPredict Airfoil Lift with PyTorch
Replace a wind tunnel with a neural network that predicts lift in milliseconds.
Train a fully-connected neural network in PyTorch to predict the lift coefficient of an airfoil from its geometric shape parameters. You will use NASA's open airfoil database as training data and learn how ML can accelerate aerodynamic design.
Start Project →Visualize Airflow Over a Wing with OpenFOAM
Run a real computational fluid dynamics simulation and watch the airflow come alive.
Set up and run a 2D airfoil CFD simulation in OpenFOAM using the simpleFoam steady-state solver, then visualize pressure coefficient, velocity streamlines, and the boundary layer in ParaView. No prior CFD experience required.
Start Project →Wing Planform Optimization in XFLR5
Squeeze every count of drag out of a wing using panel methods.
Systematically explore the design space of wing planform parameters — span, taper ratio, sweep, and twist — to maximise lift-to-drag ratio at a cruise condition using XFLR5's Vortex Lattice Method and 3D panel solver. You will script batch analyses in Python, post-process polars, and produce a documented optimum design.
Start Project → AI/MLAirfoil Flow Field with DeepXDE PINNs
Replace a CFD mesh with a neural network trained on physics equations.
Use DeepXDE to build a physics-informed neural network that solves the 2D steady incompressible Euler equations around a NACA 0012 airfoil, predicting pressure and velocity fields without a traditional computational mesh. You will enforce no-penetration boundary conditions on the airfoil surface and validate against panel method results.
Start Project → Some AI/MLML Surrogate Model for XFLR5
Replace a CFD solver with a neural network that runs 1000× faster
Generate thousands of XFLR5 airfoil simulations across a parametric design space, then train a neural network surrogate model that predicts aerodynamic coefficients in milliseconds. Enables real-time design optimization that would take days with the full solver.
Start Project → AI/MLDifferentiable Airfoil Optimization with JAX
Use automatic differentiation to find the optimal airfoil shape via gradient descent
Implement a differentiable panel-method aerodynamics solver in JAX and use automatic differentiation to compute exact gradients of lift and drag with respect to airfoil shape parameters. Perform gradient-based optimization to find airfoils that maximize L/D for given flight conditions.
Start Project → AI/MLFourier Neural Operator for Airfoil Flows
Train a neural operator that predicts full CFD flow fields in milliseconds
Train a Fourier Neural Operator (FNO) in NVIDIA PhysicsNeMo to learn the solution operator for 2D incompressible flow around arbitrary airfoil shapes. The trained FNO predicts complete pressure and velocity fields 1000× faster than OpenFOAM while maintaining engineering accuracy.
Start Project → AI/MLPhysics-Informed Neural Nets for Aerodynamics in Julia
Solve the Navier-Stokes equations with a neural network using Julia's NeuralPDE.jl
Use Julia's NeuralPDE.jl to implement physics-informed neural networks (PINNs) that solve the incompressible Navier-Stokes equations for 2D flow over a circular cylinder. Explore the benchmark Von Kármán vortex street problem and compare PINN solutions against reference DNS data.
Start Project → Some AI/MLML-Enhanced Turbulence Modeling in OpenFOAM
Train a neural network to correct RANS turbulence model errors using LES reference data
Generate high-fidelity LES (Large Eddy Simulation) data in OpenFOAM and train a neural network to correct the Reynolds-Averaged Navier-Stokes (RANS) turbulence model predictions. Implements data-driven turbulence modeling, one of the most active research areas in computational fluid dynamics.
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