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Decompression schedule optimization with an isoprobabilistic risk of decompression sickness.

Beverley J Horn1, Graeme C Wake, T Gavin Anthony

  • 1Centre for Bioengineering, University of Canterbury, NZ. beverley.horn@canterbury.ac.nz

Aviation, Space, and Environmental Medicine
|January 21, 2006
PubMed
Summary

Optimized dive decompression schedules significantly reduce surface time. This study demonstrates the feasibility of creating faster, safer dive profiles using advanced algorithms and reliable risk models for decompression sickness.

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Area of Science:

  • Diving Physiology
  • Decompression Theory
  • Computational Fluid Dynamics

Background:

  • Divers rely on decompression schedules to prevent decompression sickness (DCS) upon surfacing.
  • Current schedules have variable DCS risk, often unknown to divers.
  • This research explores optimized, iso-probabilistic schedules to minimize ascent time.

Purpose of the Study:

  • To investigate the feasibility of creating optimized iso-probabilistic decompression schedules.
  • To minimize diver surface time while maintaining a target probability of DCS.
  • To compare optimized schedules against established decompression tables.

Main Methods:

  • Utilized the sequential quadratic programming (SQP) method for optimization.
  • Employed the U.S. linear-exponential multi-gas model to estimate DCS probability.

Related Experiment Videos

  • Analyzed 1.3-bar oxygen-helium rebreather bounce dives (18-81m) against UK Navy QinetiQ 90 tables.

    • Main Results:

    • SQP method demonstrated reliable and stable convergence for schedule optimization.
    • Optimized schedules matched QinetiQ 90 table stop times for shallow dives (18m).
    • Significant decompression time savings (up to 30 min) achieved for deeper dives (39-81m).

    Conclusions:

    • Feasible to generate optimized iso-probabilistic decompression tables.
    • Requires a reliable DCS risk model and validated dive trials.
    • Offers potential for reduced diver exposure and increased efficiency.