Divers use decompression schedules that provide a stepped ascent to the surfacefrom their maximum depth to help prevent the occurrence of decompression illness.The risk of decompression illness resulting from these schedules varies acrossdifferent dives and the models used to generate them. The diver is unaware of thisvariance in risk.This thesis describes an investigation into the feasibility of producing optimised isoprobabilisticdecompression schedules that minimise the time it takes for the diver toreach the surface from maximum depth. In particular, 1.3 bar constant partialpressure of oxygen in helium dives are considered. The US Linear ExponentialMulti-gas (LEM) model is used to describe the risk of decompression illness for agiven dive. The Sequential Quadratic Programming (SQP) method is used tominimise the ascent time given non-linear risk constraints and a maximum dive timeconstraint.Two approaches to describing the ascent profile have been investigated. The firstscheme finds the stop times at each possible stop depth to produce optimisedschedules. The total time for decompression is a function of the sum of the stoptimes. The second scheme defines the ascent profile as a three parameter hyperbolictangent equation. The SQP method finds the three parameters that produce optimiseddecompression schedules once the curve is converted to a schedule of decompressionstops.The schedules produced by the SQP method, using a curve to describe the ascentprofile, show that it is feasible to produce optimised iso-probabilistic tables that areoperationally practical given an acceptable physiological risk model. Comparisonwith the QinetiQ 90 tables with a nominal 2% operational risk of decompressionillness show that the method could provide reductions in the ascent time subject tomanned testing.
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