Analysis of a Simulation Experiment on Optimized Crewing for Damage Control

Abstract

In 2008, a simulation model was developed in the Integrated Performance Modelling Environment (IPME) to evaluate different crew-automation options for naval damage control. This previous work demonstrated the feasibility and value of applying modelling and simulation to explore a large number of factors related to optimized crewing for damage control, but stopped short of performing detailed statistical analysis on the simulation outputs. The current report re-examines the data collected from the 2008 simulation experiment and subjects them to formal hypotheses testing. In particular, it investigates the effects of automation level, automation reliability, and scenario complexity on damage control effectiveness, where damage control effectiveness was measured by time to complete fire response, number of compartments affected by fire, time to complete flood response, and maximal height reached by floodwater. The analyses compared three automation levels (full, medium, and the baseline) that were coupled with three crew sizes (small, medium and large, respectively), two levels of automation reliability (100% and 75%), and two levels of scenario complexity (high, medium). Of the studied factors, automation level was found to have the most significant impact on damage control. Full automation was found to perform best in terms of fire response. Both full automation and the baseline were found to outperform medium automation in terms of flood response. Based on these analyses, this report identified a number of strategies for streamlining future development of related simulation models, as well as future data collection and analysis for related simulation experiments. Finally, this report identified a number of directions for future research on the use of modelling and simulation to inform optimized crewing, including the evaluation of different crew-automation options for whole-ship operation.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA574208

Entities

People

  • Renee Chow

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Automation
  • Classification
  • Cognition
  • Cognitive Workload
  • Control Systems
  • Data Science
  • Engineering
  • High Reliability
  • Information Science
  • Multivariate Analysis
  • Optimization
  • Simulations
  • Statistical Analysis
  • Statistics
  • Systems Engineering
  • Test And Evaluation

Fields of Study

  • Engineering

Readers

  • Computational Fluid Dynamics (CFD)
  • Database Systems and Applications
  • Systems Analysis and Design