SBIR/STTR Research Award

Advanced Fretting Fatigue Life Prediction Method for Naval Aerospace Application

Award identifier 3598210eb7c2873de527b1166a1e5502eca0a85209126fc2fce183894ca0bda6Awarding agency Department of Defense · Navy

Award amount

$239,635

Program and phase

STTR · Phase I

Award year

2026

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
GLOBAL ENGINEERING RESEARCH AND TECHNOLOGIES, LLC
Proposal award date
2025-10-15
Contract end date
2026-04-20
UEI
HK6RLNG7CD47
Solicitation number
25.B
Topic code
N25B-T032

Research abstract

Fretting fatigue is a complex damage mechanism caused by cyclic loading combined with microscopic relative motion between contacting surfaces. This stick-slip interaction results in localized plastic deformation, micro-crack initiation, and ultimately, fatigue failure. Debris formation, oxidation, and surface wear further complicate the damage process. Despite extensive experimental efforts, it remains challenging to capture the full spectrum of interacting phenomena—such as contact pressure variation, debris evolution, and microstructural effects - purely through experimental investigation and test data.Given this complexity, computational simulations have become essential for predicting fretting fatigue behavior and supplementing empirical observations. Traditional modeling approaches include continuum mechanics and crystal plasticity methods, which provide insights into stress distributions and microstructural responses. However, these models often fall short in handling evolving contact conditions, surface degradation, and unpredictable crack paths.To address these limitations, we propose a multiscale computational framework integrating Peridynamics (PD), Finite Element Method (FEM), Crystal Plasticity (CP), and kinetic Monte Carlo (kMC) simulations. At the macro-scale, PD is coupled with FEM to combine the global deformation efficiency of FEM with the fracture modeling strength of PD, especially useful for handling arbitrary crack initiation and growth at contact interfaces. At the micro-scale, PD is coupled with CP to model grain-level deformation, capturing heterogeneity in slip, lattice rotation, and grain boundary interactions. Fatigue Indicator Parameters (FIPs) such as accumulated slip and energy dissipation are used to correlate with experimental fatigue life data. At the nano-scale, debris evolution, adhesion, and oxidation are modeled via kMC, accounting for the stochastic nature of third-body layer formation and its effect on local contact conditions and crack initiation.By integrating these three modeling layers—PD-FEM, PD-CP, and kMC - our method offers a comprehensive simulation capability that spans from global structural response to localized microstructural and environmental damage processes. This enables a transformative step forward in the predictive modeling of fretting fatigue life under complex real-world loading and environmental conditions

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

17 SBIR/STTR records share this UEI (2008–2026). Reported award amounts total $4,198,378. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense

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.