Student Projects
Build Real Aerospace Projects
Guided, hands-on projects using real tools and real data. Filter by level, tool, or domain to find your next build.
Build a Model Rocket Flight Computer
Log altitude, acceleration, and temperature on a real launch
Build an Arduino-based flight computer that records sensor data during a model rocket launch. Learn embedded programming, data logging, and basic rocketry while creating something you can actually fly.
Start Project → Some AI/MLSatellite Image Classification
Teach a computer to read the Earth from space
Train a machine learning model to classify satellite images by land type — urban, forest, water, agriculture. A perfect first ML project using real remote sensing data.
Start Project →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 →Drone Obstacle Course with ArduPilot SITL
Program a virtual drone to fly itself through waypoints
Use ArduPilot's software-in-the-loop simulator to program a drone that autonomously navigates an obstacle course. No hardware needed — everything runs on your laptop.
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 →Satellite Orbit Propagator
Predict where any satellite will be, minute by minute
Build an orbit propagator from scratch that takes TLE data and predicts satellite positions. Implement Keplerian mechanics and J2 perturbation in Python or MATLAB.
Start Project → AI/MLAircraft Detection from Drone Imagery with YOLO
Train a real-time object detector to spot aircraft from above
Train a YOLO object detection model to identify aircraft in aerial and satellite imagery. Build a complete computer vision pipeline from data labeling to real-time inference.
Start Project →Parametric UAV Design in Fusion 360 + OpenVSP
Design a drone from requirements to 3D model with aero analysis
Use OpenVSP for parametric aircraft geometry and aerodynamic analysis, then bring the design into Fusion 360 for detailed CAD. Learn the professional aircraft design workflow.
Start Project → AI/MLPhysics-Informed Neural Net for Aeroelasticity
Train a neural network that respects the laws of physics
Build a physics-informed neural network (PINN) using DeepXDE to solve aeroelastic flutter equations. Combine deep learning with structural dynamics to predict wing flutter boundaries.
Start Project → AI/MLReinforcement Learning for Spacecraft Docking
Train an AI agent to autonomously dock with a space station
Build a reinforcement learning environment for spacecraft proximity operations and train an agent to perform autonomous docking. Uses OpenAI Gymnasium and Stable-Baselines3.
Start Project →Digital Twin of a Jet Engine Subsystem
Build a living simulation that mirrors real engine behavior
Create a digital twin of a jet engine compressor using MATLAB/Simulink and Python. Simulate performance, inject sensor data, and detect anomalies — the same approach used by GE and Rolls-Royce.
Start Project → AI/MLMulti-Sensor Fusion for GPS-Denied Drone Navigation
Navigate a drone without GPS using cameras, IMU, and clever math
Build a sensor fusion system that lets a drone navigate without GPS. Combine visual odometry (OpenCV), inertial measurement (IMU), and optical flow to estimate position in GPS-denied environments.
Start Project → AI/MLClassify Satellites with TensorFlow
Teach a neural network to tell a weather satellite from a GPS bird.
Build and train a convolutional neural network in TensorFlow to classify satellite images by type. You will collect a small labeled dataset, preprocess the images, and evaluate your model's accuracy—no prior ML experience required.
Start Project →Track the ISS with Ansys STK
See exactly when and where the space station will fly over your town.
Use the free Ansys STK software to model the International Space Station's orbit, generate ground tracks, and predict when it will be visible from your location. You will learn the fundamentals of orbital mechanics without writing a single line of code.
Start Project →Design a Model Rocket in SolidWorks
Go from a blank screen to a printable 3D rocket you could actually launch.
Use SolidWorks for Students to model every component of a model rocket—nose cone, body tube, fins, and motor mount—then assemble them into a complete vehicle and generate manufacturing drawings. No CAD experience required.
Start Project →Build an Orbital Simulator in Julia
Write the math that keeps satellites in orbit—from scratch.
Use the Julia programming language to numerically integrate the equations of motion for a satellite and animate its orbit around Earth. You will implement the two-body problem step by step, learning both the physics and the code as you go.
Start Project → Some AI/MLPredict Engine Failure with Kaggle Data
Use real NASA sensor data to predict when a jet engine is about to fail.
Download NASA's C-MAPSS turbofan engine dataset from Kaggle and train a machine learning model to predict Remaining Useful Life (RUL)—how many cycles an engine has left before it needs maintenance. Learn how predictive maintenance saves lives and billions of dollars in aviation.
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 → AI/MLSolve the Heat Equation with a PINN
Use a neural network to simulate how heat flows through a spacecraft wall.
Use DeepXDE, a Python library for physics-informed neural networks, to solve the 1D heat equation without writing a traditional finite-element solver. Learn how PINNs are changing the way engineers approach thermal analysis in aerospace.
Start Project → AI/MLLand on the Moon with Reinforcement Learning
Train an AI agent to fire rocket thrusters and touch down safely—in simulation.
Use OpenAI Gymnasium's LunarLander-v3 environment and the Stable-Baselines3 library to train a reinforcement learning agent that learns to land a spacecraft through trial and error—no hand-coded control laws required.
Start Project →Fly a Simulated Drone with PX4
Command a virtual drone through a waypoint mission using real autopilot software.
Set up the PX4 Software-In-The-Loop (SITL) simulator with Gazebo and use Python MAVLink commands to fly a virtual quadrotor through a series of GPS waypoints. Learn the same autopilot stack used in professional drone operations worldwide.
Start Project → Some AI/MLTrack Objects in Drone Video
Write a Python script that follows moving objects through aerial footage automatically.
Use OpenCV's built-in tracking algorithms to detect and follow moving objects—vehicles, people, or animals—in aerial drone video. Learn the fundamentals of computer vision: color spaces, background subtraction, contour detection, and bounding-box tracking.
Start Project → AI/MLCount Aircraft on a Runway with YOLO
Use a real-time object detector to automatically count planes in satellite imagery.
Run a pretrained YOLOv8 model on airport satellite images to detect and count aircraft. Learn how to use one of the most powerful object detection frameworks in computer vision without training from scratch—a key industry skill for remote sensing and airspace monitoring.
Start Project →Design a Competition Aircraft in OpenVSP
Build and analyze a complete aircraft geometry using NASA's own design tool.
Use OpenVSP—NASA's free parametric aircraft geometry tool—to design a balsa-wood competition airplane, analyze its aerodynamic performance with the built-in VSPAero solver, and iterate on wing placement and tail sizing to achieve stability targets.
Start Project →Design a Rocket Motor Mount in Fusion 360
Let AI reshape metal into the lightest bracket that still holds your rocket motor.
Use Fusion 360's Generative Design workspace to create an optimized rocket motor mount. You define the loads, material, and keep-out zones; the algorithm generates organic, lightweight structures that would be impossible to design by hand.
Start Project →Build an Aerospace Data Logger in C
Write embedded C firmware to capture atmospheric data during a weather balloon or drone flight.
Write C firmware for an Arduino microcontroller that reads temperature, pressure, and humidity sensors at 10 Hz, stores data on an SD card, and transmits live readings over serial. Then analyze the flight data in Python to reconstruct altitude profiles.
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 →Plot Satellite Orbits in MATLAB
Use MATLAB to compute and visualize Keplerian orbits and ground tracks from first principles.
Write MATLAB scripts that convert orbital elements (semi-major axis, eccentricity, inclination) to position vectors, plot 3D orbits around Earth, and generate ground tracks on a world map. Build intuition for how inclination and altitude shape a satellite's coverage.
Start Project →Build a Virtual Aircraft Hangar
Create a photorealistic 3D aircraft hangar in NVIDIA Omniverse you can walk through in real time.
Use NVIDIA Omniverse Create to build a detailed, photorealistic hangar scene populated with aircraft models, ground support equipment, and realistic lighting. Learn Universal Scene Description (USD), the emerging standard for 3D data in aerospace digital twins.
Start Project →Simulate Landing Gear Impact
Discover how much force hits the landing gear at touchdown—before building anything.
Use Siemens Simcenter student tools to set up and run a structural simulation of a landing gear drop test, visualize stress and deformation under impact loads, and identify whether the design meets safety requirements. Learn how engineers prove aircraft components are strong enough without breaking them.
Start Project → Some AI/MLRemaining Useful Life Prediction with scikit-learn
Predict when a turbofan engine will fail before it does.
Build an end-to-end machine learning pipeline on the NASA C-MAPSS dataset to estimate turbofan engine remaining useful life (RUL). You will engineer features from raw sensor streams, train regression models, and evaluate prognostic accuracy using RMSE and scoring functions from the prognostics literature.
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 →Autonomous Drone Missions with ArduPilot Lua
Script your drone's brain: autonomous survey and inspection in Lua.
Use ArduPilot's built-in Lua scripting engine to write custom autonomous mission logic that goes beyond standard waypoint navigation. You will implement a grid survey pattern, an adaptive inspection hover sequence, and a geofence-triggered return-to-launch — all running onboard without an external companion computer.
Start Project → AI/MLDeep Learning for Engine Prognostics
Teach an LSTM to hear an engine degrading cycle by cycle.
Build LSTM and 1D-CNN models in TensorFlow/Keras to predict turbofan engine remaining useful life from multi-sensor time-series. You will design sequence windowing pipelines, compare architectures, apply learning rate scheduling and early stopping, and interpret model predictions with attention weights.
Start Project → AI/MLNeural ODE Orbit Propagation with JAX
Let automatic differentiation learn orbital physics from trajectory data.
Use JAX's autodiff and ODE solvers to build a Neural ODE that learns satellite orbit dynamics purely from observed position/velocity data. You will compare the learned propagator against classical two-body and J2 models, and explore how the neural model captures unmodelled perturbations.
Start Project → AI/MLSolve Burgers Equation with PhysicsNeMo
Train a neural network to satisfy a PDE — no simulation mesh required.
Use NVIDIA PhysicsNeMo to build a Physics-Informed Neural Network (PINN) that solves the 1D viscous Burgers equation, a canonical nonlinear PDE capturing shock formation. You will set up collocation training, enforce boundary and initial conditions as soft constraints, and compare PINN solutions against a finite-difference reference.
Start Project →Design a GPS Constellation in STK
Arrange satellites in space so no point on Earth loses navigation coverage.
Use Ansys STK to design, analyse, and iterate on a navigation satellite constellation for global positioning coverage. You will define orbital parameters for a Walker or custom constellation, compute coverage metrics and PDOP maps, and trade constellation size against coverage gaps to arrive at a justified design.
Start Project →Thrust Vector Control Mount in SolidWorks
Design a gimbal that steers a rocket engine — then prove it will hold.
Design a two-axis gimbal-based thrust vector control (TVC) mount for a small liquid rocket engine in SolidWorks. You will perform parametric CAD, run FEA under maximum thrust and side-load cases, iterate on weak areas, and produce a complete design package including assembly drawings and a stress analysis report.
Start Project →CubeSat Flight Software with F Prime
Build the command and data handling brain of a CubeSat using NASA's own framework.
Use NASA's F Prime (F´) C++ flight software framework to implement the command and data handling (C&DH) software for a 3U CubeSat. You will design a component architecture, implement telemetry collection, command dispatch, and a simple fault management state machine, running the full system in the F Prime software-in-the-loop simulator.
Start Project →Rocket Trajectory Optimization in Julia
Find the fuel-minimum path from launchpad to orbit — mathematically.
Use Julia's JuMP optimization framework with the Ipopt solver to formulate and solve a fuel-optimal rocket trajectory problem using direct collocation. You will transcribe the continuous-time optimal control problem into a nonlinear program, solve it for a single-stage rocket, and analyse how constraints on thrust and dynamic pressure shape the optimal solution.
Start Project → Some AI/MLKaggle Turbofan Competition Pipeline
Go beyond homework: build a competition-grade predictive maintenance pipeline.
Build a full competition-grade machine learning pipeline for the NASA C-MAPSS turbofan degradation challenge on Kaggle. You will implement advanced feature engineering, train a diverse model ensemble (XGBoost, LightGBM, CatBoost, neural nets), and apply stacking to push toward leaderboard-competitive RMSE — all while maintaining reproducible experiment tracking.
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 → AI/MLRL Autopilot for JSBSim Flight Simulator
Train an agent to fly a real aircraft model — six degrees of freedom included.
Train a reinforcement learning agent to fly a full 6-DOF aircraft model in the JSBSim flight dynamics engine using OpenAI Gymnasium and Stable-Baselines3. You will implement a custom Gym environment wrapping JSBSim, design a shaped reward function for altitude and heading hold, and compare PPO and SAC agents on task performance.
Start Project → AI/MLVision-Based Landing with PX4 + OpenCV
Guide a drone to a precise landing using only a camera and fiducial markers.
Build a precision landing system for a PX4-based drone using ArUco marker detection in OpenCV. A companion computer runs the vision pipeline, estimates the marker pose relative to the drone, and sends position setpoints to PX4 via MAVSDK offboard control — closing a visual servo loop that guides the drone to a 10 cm landing accuracy.
Start Project → AI/MLTrain a Drone to Hover with PyTorch RL
Implement PPO from scratch and watch a quadrotor learn to balance itself.
Implement the Proximal Policy Optimisation (PPO) algorithm from scratch in PyTorch and train a quadrotor to hover and track waypoints in a lightweight physics simulation. By building PPO yourself — actor-critic networks, GAE advantage estimation, clipped surrogate loss — you gain deep understanding of why modern RL algorithms work.
Start Project →Drone Simulation in NVIDIA Isaac Sim
Build a photorealistic drone test environment where perception algorithms actually work.
Use NVIDIA Isaac Sim (built on Omniverse) to create a photorealistic drone simulation environment complete with physics, lighting, and synthetic sensor data. You will attach a simulated depth camera and IMU to a quadrotor model, generate training data for a visual odometry pipeline, and validate a perception algorithm that would be difficult to test safely in the real world.
Start Project →Wing Structural Analysis in Simcenter
Put a wing box through its paces with industrial FEA before it ever flies.
Perform a complete finite element analysis of a wing box structure under flight loads using Siemens Simcenter Nastran. You will build a shell-element wing box model, apply aerodynamic pressure loads from a VLM solution, run static, buckling, and fatigue analyses, and produce a certification-quality stress report.
Start Project →Thermal Digital Twin with Ansys Twin Builder
Distil a full FEA thermal model into a real-time digital twin that runs on a laptop.
Build a reduced-order thermal model (ROM) of an avionics bay using Ansys Twin Builder. You will run high-fidelity FEA thermal simulations in Ansys Mechanical to generate training snapshots, apply model order reduction to create a fast ROM, deploy it in Twin Builder connected to real-time heat dissipation inputs, and validate the ROM accuracy against new FEA solutions.
Start Project → Some AI/MLSatellite Telemetry Anomaly Detection
Catch satellite failures before they happen using unsupervised ML
Apply unsupervised clustering algorithms to real satellite telemetry datasets to detect anomalous behavior patterns before they escalate into failures. Combines signal processing, feature engineering, and scikit-learn to build a production-ready anomaly detection pipeline.
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/MLVision-Based Autonomous Landing with ArduPilot
Build a drone that finds and lands on a moving platform using only a camera
Develop a complete computer vision pipeline for precision autonomous landing on a moving platform. The system uses OpenCV for target detection and tracking, feeds corrections to ArduPilot via MAVLink, and achieves landing accuracy well beyond what GPS alone can provide.
Start Project → AI/MLNeural Network Flight Controller
Replace a PID controller with a neural network trained on flight data
Train a neural network in TensorFlow to replace a classical PID flight controller for a fixed-wing aircraft. The network learns the control mapping from flight dynamics data and is evaluated on stability, disturbance rejection, and robustness compared to a tuned PID baseline.
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 → Some AI/MLML-Optimized Satellite Constellation Design
Use Bayesian optimization to find constellations that maximize coverage with fewer satellites
Combine Ansys STK's high-fidelity orbital analysis with Python-based Bayesian optimization to explore the satellite constellation design space. Automatically discover configurations that achieve target coverage and revisit time requirements with the minimum number of satellites.
Start Project → Some AI/MLGenerative Design Automation with SolidWorks API
Write a Python script that designs, evaluates, and selects structural brackets autonomously
Build a Python automation system that drives the SolidWorks API to programmatically generate structural bracket design variants, run FEA evaluations, extract performance metrics, and select the optimal design — compressing days of manual CAD work into an automated pipeline.
Start Project →MISRA-Compliant ADCS Software in C++
Write flight-grade attitude control code that meets aerospace safety standards
Implement a complete CubeSat attitude determination and control system (ADCS) in MISRA-compliant C++. Covers quaternion-based attitude determination, reaction wheel control laws, and the software development practices — unit testing, static analysis, and code coverage — required for flight-grade embedded software.
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 → AI/MLMulti-Source Predictive Maintenance Pipeline
Combine multiple aerospace datasets to build a model that generalizes across equipment types
Engineer a robust predictive maintenance system by combining multiple Kaggle aerospace and industrial datasets, training models that transfer across equipment types, and deploying a unified health monitoring pipeline. Goes beyond single-dataset ML to tackle real-world generalization challenges.
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.
Start Project → Some AI/MLML-Driven Generative Design Pipeline
Combine Fusion 360 generative design with ML to automate optimal design selection
Build an end-to-end pipeline that runs Fusion 360's generative design engine, exports the resulting design variants, evaluates them with ML-based scoring models, and automatically selects the Pareto-optimal designs — turning a manual design review into a fully automated optimization workflow.
Start Project → AI/MLML Surrogate for Full Aircraft Analysis
Generate thousands of aircraft configurations in OpenVSP and train a neural network for rapid preliminary design
Script OpenVSP to generate thousands of full aircraft configurations spanning the preliminary design space, run vortex lattice aerodynamic analysis on each, and train a neural network surrogate that predicts aerodynamic performance in milliseconds — enabling real-time MDO during concept exploration.
Start Project → AI/MLEdge-Deployed Satellite Detection with YOLO
Train, quantize, and deploy a YOLO model for real-time satellite detection on Jetson Nano
Train a YOLO object detection model on satellite image datasets, apply TensorRT quantization to reduce model size and inference time, and deploy on a Jetson Nano edge device for real-time detection. Demonstrates the full ML deployment pipeline from training to embedded hardware.
Start Project → Some AI/MLSynthetic Training Data Generation in Omniverse
Generate photorealistic synthetic datasets for aerospace computer vision using NVIDIA Omniverse
Use NVIDIA Omniverse Replicator to create a photorealistic synthetic data generation pipeline for aerospace computer vision tasks. Randomize lighting, materials, backgrounds, and object poses to generate annotated training data that is cheaper, faster, and more diverse than real-world data collection.
Start Project →Turbine Blade Multiphysics Analysis
Perform coupled thermal-structural-vibration analysis of a turbine blade under real operating conditions
Conduct a complete multiphysics analysis of a gas turbine blade in Siemens Simcenter: steady-state thermal analysis under combustion gas and cooling air conditions, one-way coupled structural analysis for thermal stress, and constrained modal analysis for vibration frequencies under centrifugal load — matching the workflow used in engine certification.
Start Project → Some AI/MLHybrid Physics-ML Digital Twin
Combine a physics-based reduced-order model with ML correction for real-time structural health monitoring
Build a hybrid digital twin in Ansys Twin Builder that combines a physics-based Reduced Order Model (ROM) of a structural component with a machine learning correction layer. The hybrid twin runs in real time, processes incoming sensor data, and provides health state estimates with uncertainty bounds for structural health monitoring.
Start Project → AI/MLSimulate Gravity with JAX
Watch planets orbit each other in a simulation you built from scratch
Use JAX to build a simple gravity simulator that models how two or three bodies orbit each other. Learn the basics of numerical integration and automatic differentiation while creating animated orbital plots.
Start Project → AI/MLSimulate a Heat Shield with PhysicsNeMo
Use AI to predict how heat flows through a spacecraft's thermal protection
Use NVIDIA PhysicsNeMo to train a physics-informed neural network that predicts temperature distribution in a simple heat shield geometry. Learn how AI can solve physics problems without running expensive simulations.
Start Project →Build a Battery Digital Twin with Twin Builder
Create a virtual copy of a drone battery that predicts its behavior in real time
Use Ansys Twin Builder to create a simple digital twin of a lithium-polymer battery for a drone. Learn how engineers build virtual models that mirror real hardware and predict when a battery will run out of charge.
Start Project → Some AI/MLTest and Compare Material Strength with a Simple ML Model
Break things on purpose, then teach a computer to predict the results
Build and break popsicle-stick or balsa-wood structures, record their dimensions and failure forces, then train a scikit-learn linear regression model to predict strength from geometry. A hands-on bridge between physical testing and machine learning.
Start Project → Some AI/MLPredict Composite Laminate Failure with scikit-learn
Teach a model to predict how and where a composite will fail
Generate a dataset of composite laminate configurations using classical laminate theory, then train a multi-class classifier to predict failure mode — delamination, fiber breakage, or matrix cracking — from layup parameters. Bridges textbook composites theory with practical ML.
Start Project → AI/MLGraph Neural Network for Microstructure-Property Prediction
Turn microscope images into graphs and predict how strong the metal is
Represent alloy or composite microstructure images as graphs — with grains as nodes and boundaries as edges — and train a graph neural network to predict mechanical properties like yield strength and fatigue life. A research-grade application of geometric deep learning to materials science.
Start Project → Some AI/MLPredict Model Rocket Altitude from Motor Data with Python
Use real thrust curves and simple physics to predict how high your rocket will fly
Build a dataset from published model rocket motor thrust curves and basic physics, then train a scikit-learn regression model to predict peak altitude from motor impulse, rocket mass, and drag estimate. Combines rocketry fundamentals with your first predictive ML model.
Start Project → Some AI/MLPredict Jet Engine Thrust from Sensor Data with Random Forest
Use NASA engine data to learn what sensor readings reveal about thrust
Use the NASA C-MAPSS turbofan engine simulation dataset to build a regression model that predicts thrust output from temperature, pressure, and spool speed sensor readings. Explore feature importance and gain insight into gas turbine thermodynamics through data.
Start Project → AI/MLML Model for Combustion Instability Detection
Train a neural network to hear when a combustor is about to go unstable
Train a time-series classifier — LSTM or 1D-CNN — on pressure oscillation data to detect thermoacoustic instability in combustion chambers. Addresses a critical safety problem in rocket engines and gas turbines with deep learning on sequential sensor data.
Start Project → AI/MLMeasure and Classify Sounds Around an Airport with Python
Record real-world sounds and teach a computer to tell them apart
Record or download airport-area sounds — jet engines, propeller aircraft, ground vehicles, birdsong — and train a scikit-learn classifier to distinguish sound types using simple audio features extracted with librosa. A hands-on introduction to audio machine learning.
Start Project → AI/MLPredict Aircraft Noise Levels with Neural Networks
Use NASA wind tunnel data to predict how loud an airfoil is
Use the NASA Airfoil Self-Noise dataset from the UCI Machine Learning Repository to train a neural network that predicts sound pressure level from airfoil geometry, wind speed, and angle of attack. A clean regression problem connecting aeroacoustics with deep learning.
Start Project → AI/MLEngine Sound Anomaly Detection with Autoencoders
Teach a neural network what normal sounds like — then catch everything else
Train a convolutional autoencoder on mel-spectrograms of normal engine audio to detect anomalous sounds using reconstruction error as the anomaly score. Addresses the practical challenge of detecting unknown fault types without labeled failure data.
Start Project → AI/MLPredict Tomorrow's Wind Speed for Flight Planning
Build a weather model that helps pilots make go/no-go decisions
Download historical airport weather data (METAR observations) and build a simple machine learning model that predicts next-day wind speed. Learn how pilots use weather forecasts for flight planning and go/no-go decisions.
Start Project → AI/MLPredict Clear-Air Turbulence from Weather Data
Build a classifier that warns pilots about invisible rough air
Build a machine learning classifier that predicts turbulence severity (none/light/moderate/severe) from atmospheric variables using real NOAA pilot reports (PIREPs) and reanalysis data. Tackle one of aviation's most challenging weather hazards.
Start Project → AI/MLML-Optimized Flight Route Planning Around Weather
Route aircraft around storms and turbulence with graph search and ML
Build a system that ingests weather forecast grids and uses graph search with an ML-learned cost model to find fuel-optimal routes that avoid turbulence and convective weather. Compare against great-circle routes to quantify fuel and safety improvements.
Start Project → AI/MLSort Good vs. Bad Parts with Image Classification
Teach a computer to inspect parts like a quality engineer
Photograph real parts (3D-printed or handmade), build a labeled image dataset, and train an image classifier to sort pass vs. fail. Learn the basics of computer vision and quality control in aerospace manufacturing.
Start Project → AI/MLDetect Surface Defects in Aerospace Parts with CNN
Train a deep learning model on real industrial defect data
Train a convolutional neural network to classify images of metal surfaces as defective or non-defective using the NEU Surface Defect Database. Build a production-quality defect detection pipeline applicable to aerospace manufacturing.
Start Project → AI/MLProcess Parameter Optimization for Additive Manufacturing
Use Bayesian optimization to tune 3D printing for aerospace-grade quality
Build a Bayesian optimization pipeline that tunes additive manufacturing parameters (laser power, scan speed, layer thickness) to minimize porosity and maximize tensile strength. Apply surrogate modeling and intelligent search to a real aerospace manufacturing challenge.
Start Project →Track Nearby Aircraft with ADS-B and Visualize Patterns
Tap into live aircraft data and discover hidden flight patterns
Use the OpenSky Network API to collect real aircraft position data, plot flight paths on a map, compute basic statistics, and use k-means clustering to discover flight patterns. Your first data science project with real-time aviation data.
Start Project → Some AI/MLClassify Aircraft Types from ADS-B Trajectories
Identify what's flying overhead from how it flies
Extract flight trajectory features from OpenSky Network ADS-B data — climb rate, speed profile, turn radius, acceleration patterns — and train a classifier to identify aircraft types. Learn feature engineering on spatiotemporal data.
Start Project → AI/MLRadar Target Classification with Deep Learning
Classify drones, birds, and aircraft from synthetic radar signatures
Train a CNN on synthetic radar range-Doppler maps to classify aerial targets (drone, bird, aircraft, clutter). Generate realistic synthetic data using radar cross-section models and signal processing fundamentals.
Start Project →Calculate and Compare Carbon Footprints of Different Flights
Turn flight data into climate insight with Python
Use public flight data to calculate CO2 emissions per passenger-kilometer for different aircraft types and routes. Build a simple regression model that predicts emissions from distance and aircraft size.
Start Project → Some AI/MLPredict Flight Fuel Burn from Route and Aircraft Data
Build an ML model that estimates fuel consumption before takeoff
Use publicly available flight data from BTS or Eurocontrol to build a regression model predicting fuel consumption from distance, aircraft type, payload, and weather. A real-world ML problem with direct sustainability applications.
Start Project → AI/MLContrail Prediction and Avoidance with ML
Predict where contrails form and reroute flights to avoid them
Build an ML model predicting contrail formation probability from atmospheric conditions and satellite imagery. Explore how small route adjustments can significantly reduce aviation's non-CO2 climate impact.
Start Project → Some AI/MLBuild a Word Cloud and Classifier from Aviation Safety Reports
Mine real incident reports to discover what goes wrong in the cockpit
Download NASA ASRS incident summaries, create word clouds for different incident categories, then train a simple text classifier to categorize new reports. A hands-on introduction to natural language processing with real safety data.
Start Project → AI/MLAnalyze Aviation Incident Patterns with NLP
Apply modern NLP to uncover hidden patterns in safety data
Use the NASA ASRS database to classify incident narratives by category using TF-IDF and transformer models. Discover patterns in human-factors incidents that traditional analysis methods miss.
Start Project → AI/MLPredict Pilot Fatigue Risk from Flight Schedule Data
Model the invisible threat to flight safety with ML
Model cumulative fatigue using the SAFTE/FAST framework, then train an ML model on schedule features to predict fatigue risk scores. Tackle one of aviation safety's most challenging human factors problems.
Start Project → Some AI/MLBuild a Flutter Demo and Predict Vibration Frequency
Feel aeroelasticity with a homemade wing in front of a fan
Build a simple cantilever wing from cardboard or balsa, measure vibration frequency at different wind speeds using a phone accelerometer, then fit a regression model to predict frequency from speed.
Start Project → AI/MLPredict Wing Flutter Speed with Gradient Boosting
Train XGBoost to replace hours of aeroelastic simulation
Build a dataset of wing configurations with flutter speeds from analytical or simulation results. Train a gradient-boosted model to predict flutter onset — creating an ML surrogate for computationally expensive aeroelastic analysis.
Start Project → AI/MLML-Based Gust Load Prediction for Wing Structures
Replace expensive time-domain simulations with a trained sequence model
Train a sequence model (LSTM or Transformer) on time-series gust encounter data to predict peak wing root bending moment. Compare against classical 1-minus-cosine gust analysis for a real-world loads problem.
Start Project → Some AI/MLMeasure Heat Dissipation and Predict Cooling with Python
Newton's law of cooling meets machine learning in your kitchen
Heat small metal and plastic samples, log temperature over time, fit exponential cooling curves, and predict how long different materials take to cool. A hands-on experiment connecting thermal physics to data science.
Start Project → Some AI/MLPredict Avionics Bay Temperature with Regression Models
Build an ML model for the thermal challenge every aircraft faces
Build a regression model predicting peak avionics bay temperature from flight phase, ambient conditions, and power dissipation. Use synthetic data from thermal network models to train and validate your approach.
Start Project → AI/MLNeural Network Surrogate for Thermal Protection System Design
Accelerate reentry vehicle thermal design with deep learning
Train a neural network on parametric FEA results to predict temperature distribution through a multi-layer thermal protection system for reentry vehicles. Use the surrogate for rapid design-space exploration.
Start Project →Optimise Wing Rib Spacing with FEA
Place ribs where the loads are—not where tradition says.
Use SolidWorks and ANSYS to investigate whether non-uniform rib spacing in a wing structure can reduce weight or stress compared to evenly spaced ribs. You will design a wing box, run static structural simulations under bending loads, and compare uniform versus load-adapted rib layouts to find a more efficient internal structure.
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