SBIR/STTR Research Award

Predictive Model Based Control System for High-Speed Dynamic Airframe Testing

Award identifier edd514172831ff9e056d76ff1eaedd09e05c96f036b5525b25469fdd9d8a6a42Awarding agency Department of Defense · Navy

Award amount

$139,903

Program and phase

SBIR · Phase I

Award year

2021

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Defense
Branch
Navy
Recipient company
Kaney Aerospace, Inc.
Proposal award date
2021-08-04
Contract end date
2022-02-07
UEI
K5KBZEM7QKN5
Solicitation number
21.1
Topic code
N211-014

Research abstract

The US Navy seeks a new and innovative way to conduct full-scale fatigue testing on airframes, particularly for rotary wing airframes.  Typical equivalent fatigue damage approaches, often successfully applied to airframe subsystems, have be found to be insufficient.  Full-scale fatigue testing on airframes is difficult due to the multiple inputs and, more importantly, the nature of those inputs.  The multiple loading inputs vary consistently in magnitude and phasing.  Current test approaches are limited in frequency (low cycle counts), displacements and numerous other considerations necessary to properly test the airframe for equivalent, full life fatigue damage. A model-based (model-in the-loop) control system is sought to significantly improve upon typical reactive based control systems.  The model-based multiple input multiple output (MIMO) control system will be predictive and adaptive based on feedback from typical sensors that are used in structural testing.  For this Phase 1 project, Kaney’s initial modeling will be of a simplified dynamic system using MATLAB-based simulations.  The initial simplified system will include a simple model and a low number of inputs from pseudo-sensors to focus on achieving the minimum goals of the control system: a minimum loading frequency of 10 Hz with excitation sources that can achieve speeds of 100 in/s or more (~16 gpk/1.6 inpk at 10 Hz).  This will allow for ease of adjusting and experimenting with different approaches for the predictive and adaptive control system that meet the minimum goals of loading frequency.  A small-scale proof of concept (POC) will be created during this Phase of the program. In parallel with control system modeling, research will be conducted on existing excitation sources as well as existing technologies that can be altered to use as excitation sources that will meet or exceed the already discussed minimum goals of the test system. Kaney will leverage 20+ years of experience in Controls and Airframe subsystem fatigue testing to achieve the discussed goals.  Successful achievement will allow for a scalable test system that, ultimately, could be applied to numerous airframe types as well as other industries in general.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

19 SBIR/STTR records share this UEI (2020–2025). Reported award amounts total $10,842,330. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration

Agency intelligence

Department of Defense

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.