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  • Institute Director
    Charles Farrar
    (505) 663-5330
  • UCSD EI Director
    Michael Todd
    (858) 534-5951
  • Institute Office Manager
    Jutta Kayser
    (505) 663-5649

Composite-to-Steel Joint Integrity Monitoring and Assessment

UCSD Faculty and Graduate Students

LANL Collaborators

  • Dr. Matt Bement (INST-OFF: INSTITUTES)

  • Dr. Francois Hemez (X-1-MV: METHODS AND VERIFICATION)

  • Dr. Gyuhae Park (INST-OFF: INSTITUTES)

The use of heterogeneous materials in aggregate structural systems is increasing for a number of applications where performance demands and/or cost considerations warrant such hybrid designs. For example, the U.S. Navy is considering such a hybridized design for the next generation destroyer DD-X surface ship, which has a composite material superstructure and a metal hull. Such a design involves many metal-to-composite bolted connections. Under normal in-service mechanical and thermal stresses, composite materials are known to creep over time, resulting in loss of pre-load at the connection. This pre-load loss leads to reduced functionality in the joint to support design loads and can ultimately lead to catastrophic failure through wear, fatigue, or stress rupture. Currently, the inspection and re-torquing of bolted connections is performed by rote schedule without regard to condition, but as the U.S. Navy officially transitions to a condition-based maintenance philosophy, an on-line, automated diagnostic condition assessment is needed to meet these new requirements.
This need is further strengthened as a large manning reduction of up to 70% is expected to accompany the DD-X program. The primary goals for UCSD students assigned to this task are (1) to identify the relevant time-frames for the creep-induced pre-load loss and subsequent failure mechanism(s) of these bolted connections; (2) to identify an appropriate diagnostic test or tests that can track these processes; (3) to identify an appropriate sensor system to obtain the proper measurements needed to complete the diagnostic test(s); (4) to develop predictive models and couple them with the diagnostic analysis and real-time loads monitoring for prognostic capability; and (5) ultimately, to develop and deliver a prototype fastener degradation diagnostic/prognostic system meeting U.S. Navy requirements in condition-based maintenance. Students that develop the signal processing, nonlinear dynamics, tune-dependent plasticity material behavior, and statistical pattern recognition capabilities necessary to complete this task will have the technical background to directly impact LANL programs such as the advanced material modeling and data interrogation associated wit our high-fidelity predictive modeling efforts that supports almost all large-scale nuclear weapons engineering programs and the pit manufacturing program.

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