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Research Papers: Polar and Arctic Engineering

Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment

[+] Author and Article Information
Erik Veitch

Memorial University of Newfoundland,
Faculty of Engineering and Applied Science,
Ocean & Naval Architectural Engineering,
St. John's, NL A1C 5S7, Canada
e-mail: erik.veitch@mun.ca

David Molyneux

Memorial University of Newfoundland,
Faculty of Engineering and Applied Science,
Ocean & Naval Architectural Engineering,
St. John's, NL A1C 5S7, Canada
e-mail: david.molyneux@mun.ca

Jennifer Smith

Memorial University of Newfoundland,
Faculty of Engineering and Applied Science,
Ocean & Naval Architectural Engineering,
St. John's, NL A1C 5S7, Canada
e-mail: jennifersmith@mun.ca

Brian Veitch

Memorial University of Newfoundland,
Faculty of Engineering and Applied Science,
Ocean & Naval Architectural Engineering,
St. John's, NL A1C 5S7, Canada
e-mail: bveitch@mun.ca

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received June 26, 2018; final manuscript received October 12, 2018; published online January 17, 2019. Assoc. Editor: R. M. Chandima Ratnayake.

J. Offshore Mech. Arct. Eng 141(4), 041501 (Jan 17, 2019) (12 pages) Paper No: OMAE-18-1080; doi: 10.1115/1.4041761 History: Received June 26, 2018; Revised October 12, 2018

The research investigates the influence of human expertise on the effectiveness of ice management operations. The key contribution is an experimental method for investigating human factor issues in an operational setting. Ice management is defined as a systematic operation that enables a marine operation to proceed safely in the presence of sea ice. In this study, the effectiveness of ice management operations was assessed in terms of ability to modify the presence of pack ice around an offshore structure. This was accomplished in a full-mission marine simulator as the venue for a systematic investigation. In the simulator, volunteer participants from a range of seafaring experience levels were tasked with individually completing ice management tasks. Recorded from 36 individuals' simulations, we compared ice management effectiveness metrics against two independent variables: (i) experience level of the participant, categorized as either cadet or seafarer and (ii) ice severity, measured in ice concentration. The results showed a significant difference in ice management effectiveness between experience categories. We examined what the seafarers did that made them more effective and characterized their operational tactics. The research provides insight into the relative importance of vessel operator skills in contributing to effective ice management, as well as how this relative importance changes as ice conditions vary from mild to severe. This may have implications for training in the nautical sciences and could help to inform good practices in ice management.

Copyright © 2019 by ASME
Topics: Ice , Lifeboats , Emergencies
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References

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Figures

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Fig. 1

Schematic of the simulator setup

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Fig. 2

Schematic of the simulator bridge console

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Fig. 3

Screenshot from the Instructor Station monitor during simulation. Graphics shown here are identical to those that appear in Replay files, which were used for data analysis.

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Fig. 4

“Precautionary” ice management scenario (7-tenths concentration case)

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Fig. 5

“Emergency” ice management scenario (7-tenths concentration case)

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Fig. 6

Example measurements from a single 30-minute simulation trial. “unmanaged ice” refers to recorded ice concentration values within the ice management zone when no ice management is performed; “managed ice” refers to that when ice management is performed. Zonal ice concentration values are recorded at 30 s intervals.

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Fig. 7

The example case showing the average clearing performance metric. This metric is derived by subtracting the managed ice zonal concentration values from the baseline unmanaged ice values recorded during the 30-minute simulation.

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Fig. 8

Boxplots of average ice clearing for emergency scenario

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Fig. 9

Half-normal plot of subplot effect. Squares indicate positive effects; triangles indicate error estimates. This plot shows that concentration has a large positive effect relative to the “error line” representing the smallest 50% of effects.

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Fig. 10

Half-normal plot of whole-plot effect. Squares indicate positive effects; triangles indicate error estimates. This plot shows that experience has a large positive effect relative to the error line representing the smallest 50% of effects.

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Fig. 11

Interaction plot of concentration and experience on average ice clearing. As the two lines are approximately parallel, it shows that the effect of experience (the “gap” between the lines) does not increase at a higher concentration level.

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Fig. 12

Normal plot of residuals. Since the residuals of the measured responses follow approximately a straight line, the underlying assumption required by ANOVA that residuals be normally distributed is verified.

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Fig. 13

Residuals versus ascending predicted response values. The plot shows random scatter, thereby verifying an important underlying assumption in ANOVA that variance be constant.

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Fig. 14

Residuals versus run order. There appears to be no relationship between run order and residuals of measured responses, a critical check that verifies that no time-related lurking variables have affected results.

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Fig. 15

Port lifeboat launch zone (not to scale). The circle represents an 8 m radius splash zone based on the target drop point of a 10 m TEMPSC lifeboat launch.

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Fig. 16

Boxplots of cumulative ice-free lifeboat launch times during emergency scenario

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Fig. 17

Main effect plot of cumulative ice-free lifeboat launch time versus experience

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Fig. 18

Heatmap for cadets' tracks during emergency scenario (7-tenths concentration case). Note: The lifeboat launch zone is not drawn to scale.

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Fig. 19

Heatmap for seafarers' tracks during emergency scenario (7-tenths concentration case). Note: The lifeboat launch zone is not drawn to scale.

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Fig. 20

Plot of best and worst tracks for emergency scenario (7-tenths concentration case). Criterion is cumulative ice-free lifeboat launch time.

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Fig. 21

Midway mark (15 min) during emergency scenario for best trial, measured by cumulative ice-free lifeboat launch time (7-tenths concentration case)

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Fig. 22

Midway mark (15 min) during emergency scenario for worst trial, measured by cumulative ice-free lifeboat launch time (7-tenths concentration case)

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Fig. 23

Plot of actual versus self-reported performance scores with LOESS line

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