RapidRotor AI — Community Feedback

Hacking for Defense · Purdue University · Spring 2026

The RapidRotor AI team at Purdue (Hacking for Defense, Spring 2026) is validating whether AI/ML surrogate models trained on high-fidelity CFD data can meaningfully reduce the weeks-to-months cycle times that rotorcraft and UAV aerodynamic workflows demand today. We're asking practitioners across drone OEMs, aviation, defense, and research to share ground-truth perspective on workflow bottlenecks and adoption realities.

This effort is led by Greg Sapp, an MBT candidate at Purdue's Mitch Daniels School of Business and a member of GovCon Chatham House (GCH). Your responses directly inform the team's final recommendation to the U.S. Army Research Laboratory. This is not a sales pitch.

Use of your responses: information collected here is used strictly for educational and business-formation feedback purposes. It will not be sold, shared, or used outside of those contexts.

~7–10 minutes · 22 questions Please do not include classified, CUI, ITAR, or proprietary information

A · About you

So we can attribute insight and follow up only if you want us to.

Please enter your name.
Please enter your company or organization.
Please choose one.
Please choose one.

B · Your workflow reality

Helping us validate where time actually goes.

Select all that apply.

Please select at least one.
Please share your top bottleneck.

Select all that apply.

C · The AI surrogate hypothesis

Testing whether our proposed solution actually lands.

Select all that apply.

Please select at least one.

Select all that apply.

Please select at least one.

D · Adoption & integration

What would actually stand in the way.

Select all that apply.

Select all that apply.

One sentence is fine — what matters most?

E · Close

0 of 22 answered

Thank you.

Your feedback is logged. The H4D RapidRotor AI team appreciates you taking the time — this directly shapes the final recommendation.

You may close this tab.