3-Dimensional Modeling and Simulation for Lifetime Predictions | CRANBAA18 0006

Opportunity Information: Apply for CRANBAA18 0006

The Department of Defense, through Naval Surface Warfare Center (NSWC) Crane, offered a discretionary research opportunity focused on improving how the Navy predicts the service life of components using advanced 3-dimensional modeling and simulation. The effort is centered on building 3-D lifetime prediction models that combine several technical approaches: finite element analysis to capture stresses, strains, and other physics-based behaviors; fatigue analysis to estimate damage accumulation under repeated loading; and deep learning methods to improve prediction accuracy, recognize patterns in complex data, and potentially fuse disparate data sources into a single predictive framework. The intent is to move beyond simple life estimates and toward a more comprehensive, data-informed digital representation of how parts degrade over time.

A key feature of the opportunity is that it targets two broad classes of aging mechanisms that affect reliability. First, it includes forces and operational conditions that change material properties inside a system, such as mechanical loads, vibration, thermal cycling, shock, and other stressors that can accelerate wear, crack initiation, crack growth, or deformation. Second, it explicitly includes chemical aging and time-dependent material changes that occur even in the absence of external loads, meaning degradation that happens simply due to the passage of time and environmental exposure. Examples of this kind of behavior can include oxidation, corrosion processes, polymer embrittlement, outgassing, moisture absorption, chemical diffusion, or other reactions that slowly alter material structure and performance. By requiring the model to address both load-driven and no-load aging pathways, NSWC Crane is signaling an interest in lifetime tools that remain useful across real-world storage, transport, and operational scenarios, not just during active use.

The solicitation also emphasizes usability for maintenance and field decision-making, not just technical accuracy. Virtual reality visualization is intended to be part of the solution so that field operators can interpret simulation and prediction results in a more intuitive, spatially grounded way. In practice, that suggests an end product that can display 3-D geometry, highlight high-risk regions, show predicted damage progression, and allow users to explore "what-if" conditions or compare alternative maintenance actions. The operational goal is to translate modeling outputs into practical guidance that supports maintenance scheduling, condition-based maintenance decisions, and timely corrective actions before failure occurs.

From an administrative standpoint, this opportunity was issued as a cooperative agreement, which typically indicates an expectation of substantial collaboration or involvement by the government during the project rather than a purely hands-off grant. The funding activity category was science and technology and other research and development, and the listed CFDA number was 12.300. The award information indicated an award ceiling of $200,000 and an expectation of one award. Eligibility was open to public and state-controlled institutions of higher education and, more broadly, unrestricted to any type of entity as permitted by any additional eligibility language in the full announcement. The opportunity was posted by NSWC Crane with a creation date of April 19, 2018, and an original closing date of May 16, 2018, under funding opportunity number CRANBAA18 0006.

Overall, the opportunity sought research that blends physics-based engineering simulation with modern machine learning, then packages results in a visualization format that supports real maintenance decisions. The underlying theme is reducing uncertainty in component life estimates by combining mechanistic understanding (finite element and fatigue modeling) with data-driven inference (deep learning), while accounting for both operational loading and chemical/time-based aging that can silently erode reliability even when equipment is not being actively stressed.

• The Department of Defense, NSWC - CRANE in the science and technology and other research and development sector is offering a public funding opportunity titled "3-Dimensional Modeling and Simulation for Lifetime Predictions" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.300.
• This funding opportunity was created on Apr 19, 2018.
• Applicants must submit their applications by May 16, 2018. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $200,000.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Public and State controlled institutions of higher education, Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.

Apply for CRANBAA18 0006

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