Aircraft Design Projects
Explore guided student projects in Aircraft Design. Build hands-on skills with real aerospace tools and data.
5 projects
Combine with other filters →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 →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 → 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 → 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.
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