| Project Overview | This B.Tech Aerospace / Aeronautical Engineering project is based on the recent research direction 'Enhancing surrogate-based aerodynamic shape optimization using machine learning'. The project focuses on applying artificial intelligence, machine learning, deep learning, computer vision, reinforcement learning, surrogate modelling, or RAG-style intelligent assistance to the Aircraft Design Projects area. Students can use the linked 2023-onward research paper/source as the academic base, then convert it into an implementation-focused final-year project with a simplified dataset, simulation model, Python workflow, dashboard, or prototype demonstration. |
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| Research Paper Title | Enhancing surrogate-based aerodynamic shape optimization using machine learning |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2026 |
| Project Area | Aircraft Design Projects |
| Project Type | ML Optimization |
| Required Tools / Software | Python, Scikit-learn, PyTorch/TensorFlow, OpenVSP optional, CAD data, Streamlit |
| Main Features / Working Principle | Implement a simplified surrogate-based optimization workflow for aircraft aerodynamic shape design |
| Expected Output | A shape-optimization prototype with before/after comparison |
| Possible Add-ons | Add active learning and constraint handling |
| Get Help | Get Help on WhatsApp Message: Hi FE, I need help with "Enhancing surrogate-based aerodynamic shape optimization using machine learning" in "Aerospace / Aeronautical Engineering" |
This B.Tech aerospace project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.
Related resource: Aircraft Landing Time Prediction with Deep Learning on Trajectory Images
