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Systems Availability Modelling: Queen Elizabeth Class (QEC) Carrier Variant (CV)

BMT Reliability Consultants updated the QEC Whole Ship Systems Availability Models (SAMs) to assess the impact of the CV Joint Strike Fighter (JSF) on the operational availability of the platform.

Summary

BMT updated the QEC Whole Ship Systems Availability Models (SAMs) to assess the impact of the CV Joint Strike Fighter (JSF) on the operational availability of the platform. 

BMT lead a number of stakeholder workshops and discussions to obtain the required modelling data for the CV systems e.g. the catapult and arrestor gear. The modelling data and assumptions were documented in an availability modelling Master Data & Assumptions List (MDAL), which was reviewed and agreed by the MoD. 

The results of the modelling task were presented in a Modelling Results Report which:

  • Compared the predicted operational availability for the previous SAM modelling with the updated SAM modelling which represented the CV design
  • Presented the top unavailability drivers to obtain confidence in the modelling results and also to increase the understanding of the drivers
  • Reported on the results of the sensitivity analysis, which was performed where there was either uncertainty in the system configuration or use, or where an equipment had a significant bearing on the modelling result
  • Identified where to concentrate resources to obtain further data or conduct further analysis that would improve the confidence in the modelling results

Key Benefits

  • Provision of traceable information to understand the impact of CV systems on the QEC
  • Creation of a baseline model that can be taken forward as the QEC CV project progresses and data matures

Key Features

  • Cost effective approach to predicting the impact of the CV systems on the operational availability performance of the QEC operating against a wartime mission profile
  • Ability to determine the key drivers to unavailability, in order to identify where to concentrate effort to increase confidence in the availability prediction