Digital Twins Projects
Explore guided student projects in Digital Twins. Build hands-on skills with real aerospace tools and data.
6 projects
Combine with other filters →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 →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 →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/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 → 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 →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.
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