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Record W4241360954 · doi:10.4043/15216-ms

Evacuation Performance

2003· article· en· W4241360954 on OpenAlexaff
António Simões Ré, Brian Veitch

Bibliographic record

VenueOffshore Technology Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsMemorial University of NewfoundlandNational Research Council Canada
Fundersnot available
KeywordsContext (archaeology)HarmHazardProcess (computing)Computer scienceScale (ratio)Risk analysis (engineering)Operations researchBusinessAeronauticsTransport engineeringEngineeringPolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract Evacuation system performance deteriorates as weather conditions worsen. A research program based primarily on model scale tests of several different types of evacuation system has quantified how prevailing weather affects performance. To do this, several measures of performance were proposed and their utility confirmed. The most recent phase of the research program investigated performance in extreme weather conditions. Results are presented and discussed in the context of goal-based decision making. Introduction Evacuation of an offshore installation is a process that begins with embarkation of personnel, followed by lowering or delivery to the sea, followed by departure from the splashdown point to a place of safety removed from the immediate hazard that precipitated the evacuation and where a rescue can be attempted. This might be expressed as a goal: In circumstances that necessitate a marine evacuation, personnel must have access to an evacuation system, be able to embark and launch safely, clear the installation, and survive until rescued, and to have a reasonable expectation of successfully escaping harm in the environmental conditions that can reasonably be expected to prevail during operations. Regulations in the offshore and maritime industries in many jurisdictions are moving away from specification-based standards and towards goal-based standards, a move that has prompted debate in both industries. To add to and help inform the debate in the arena of evacuation, a research program has been investigating the performance capabilities of several types of evacuation system, including conventional twin falls davit launched lifeboats, the same system modified by the addition of a flexible boom, and free fall systems [e.g. 1,2]. These investigations have been based on model scale experiments and have focused on quantifying how weather conditions and various evacuation station design parameters affect the performance of evacuation systems. In order to quantify performance, it has been necessary to define performance measures and demonstrate their utility. Recently, the performance of the conventional twin falls davit launched totally enclosed motor propelled survival craft (TEMPSC) was tested in extreme weather conditions with the aims of determining the upper operational weather limit of such a system, the role of wave steepness, and the effects of launch orientation. The results of these experiments complement earlier work and have led to a conceptual framework for assessing performance capabilities and designing to meet safety goals. The framework is presented here along with experimental results. Evacuation Zones and Performance Measures It is useful to consider the evacuation area as consisting of several zones, illustrated in Figure 1 and named here as splash-down, clearing, rescue, and exclusion zones. One measure of the system's performance is how closely the evacuation system delivers the lifeboat to the target launch point, which for the conventional system considered here is vertically below the lifeboat in its deployed position. The closer the actual splash down is to the target, the better. The distance between the target launch point and the installation is the clearance and this can be expected to have an important influence on the likelihood of a successful evacuation, particularly in terms of avoiding collisions after launching.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.197
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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