Campus Ideaz

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Problem

  • Student teams struggle with expensive, hard-to-use CFD software and messy, slow design comparisons.

  • Most solutions are not made for small teams: they're overcomplicated or disconnected.

  • Formula Student teams (and small automotive engineering groups) face critical bottlenecks in aerodynamics optimization:

  • Tool Fragmentation , Accessibility, Efficiency Gaps, Limited Collaboration.

 

Solution

A Unified, Cloud-Based Aero Optimization Platform

What it does:

  • Integrates 3D geometry import (CAD), parametric editing, fast cloud-CFD, and wind tunnel/track test data management.

  • Uses AI/ML to suggest high-potential shape modifications (based on historical team/partner data), predict drag/lift/donwforce for new designs, and highlight underperforming areas.

  • Provides side-by-side result comparison, easy-to-read graphs, and an automated report generator for design reviews and event entries.

  • Built-in collaboration and version-tracking: teammates can comment, annotate, and compare simulations from anywhere.

Technical Detail:

  • Web App: React/Vue.js front end

  • Simulation: Cloud CFD (like OpenFOAM) backend

  • AI: Suggests design tweaks using ML models

  • Storage: Cloud database for designs/results

  • Features: 3D model upload, version tracking, team sharing, badges/achievements

  • Security: User login, encrypted data

 

Who Benefits?

  • Formula Student race teams, engineering students, and teachers who need simple, affordable aerodynamic simulation and advice.

 

Why it Matters to Me

  • Directly relevant to my work in Formula Student.
  • Solves a real problem in student racing—faster, cheaper, smarter car design.

Votes: 28
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Comments

  • Eager to see how the platform scales with larger teams and multiple partner institutions. This could set a new standard for aero optimization in student engineering.
  • Kudos for targeting affordability and usability without sacrificing power. Cloud CFD plus collaborative features could lower barriers for new teams entering the competition.
  • Intriguing vision. If the ML models can adapt to different teams’ data, this could become an indispensable part of Formula Student workflows.
  • The emphasis on side-by-side comparisons and automated reports will make design reviews more rigorous and accessible for both students and professors.
  • Excellent! A secure, collaborative, cloud-based workflow for CFD and design reviews could streamline education and competitive preparation.
  • This has strong potential to democratize aero optimization for smaller teams. Looking forward to seeing how the platform handles real-world data integration from wind tunnels and tracks.
  • Great direction. The combination of AI-driven design suggestions and easy reporting could empower students and mentors to make better aerodynamic decisions.
  • I love how this addresses tool fragmentation and collaboration. A scalable solution that lets teams share results and iterate faster is a game changer.
  • Brilliant concept. Bringing CAD, cloud CFD, and data-driven design optimization into one accessible platform will save time and reduce costs for student teams.
  • This is exactly the kind of tooling that the Formula Student community needs. A unified, cloud-based aero platform with AI-driven insights could dramatically speed up learning and innovation.
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