Accession Number:

ADA609379

Title:

Submarine Propulsion Shaft Life: Probabilistic Prediction and Extension through Prevention of Water Ingress

Descriptive Note:

Master's thesis

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE DEPT OF MECHANICAL ENGINEERING

Personal Author(s):

Report Date:

2014-06-01

Pagination or Media Count:

53.0

Abstract:

Submarine propulsion shafts have demonstrated acceptable reliability performance when inspected and refurbished at least every 6 years. Designers wish to extend the inspection interval to 12 years without sacrificing reliability. This interval is unprecedented, as no known submarine shafting system is currently operated with this inspection cycle, nor are any known commercial vessel shafts. Experience and improved design have eliminated many threats to the life of a submarine shaft, but inspections of existing shafts show a high percentage with signs of wetting, leaving designers with less-than-acceptable confidence to approve this longer inspection interval due to the possibility of corrosion fatigue failure. This thesis uses probabilistic models from literature for pitting and cracking of wetted shafts, along with Monte Carlo simulations, to predict results of shafts inspections. Each possible water ingress distribution is analyzed by simulating shafts under 6 years of exposure to the water ingress, pitting, and cracking models in order to estimate the effects of corrosion fatigue. A water ingress distribution that predicts inspection results closest to actual inspection results is identified. Some information about water ingress is inferred from this distribution. Next, using the same literature models, a water ingress distribution that predicts similar performance at 12 years is identified. It is shown that the time a shaft is in service prior to becoming wetted must increase substantially. Predicted failure rates are low, but they are still higher than acceptable. This thesis recommends that inspection procedures are updated to provide more robust information for future analyses, which would better identify the appropriate distributions and greatly reduce uncertainty.

Subject Categories:

  • Statistics and Probability
  • Machinery and Tools
  • Submarine Engineering
  • Mechanics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE