Computer validation and statistical correlations of a modern decompression diving algorithm.
A diving algorithm is a safe combination of model and data to efficiently stage diver ascents following arbitrary underwater exposures. To that end, we detail a modern one, the LANL reduced gradient bubble model (RGBM), dynamical principles, and correlations with the LANL Data Bank data. Table, profile, and meter fit and risk parameters are obtained in statistical likelihood analysis from decompression exposure data. The RGBM algorithm enjoys extensive and utilitarian application in mixed gas diving, both in recreational and technical sectors, and forms the bases for released tables, software, and decompression meters used by scientific, commercial, and research divers. The LANL Data Bank is described, and the methods used to deduce risk are detailed. Risk functions for dissolved gas and bubbles are summarized. Parameters that can be used to estimate profile risk are tallied. To fit data, a modified Levenberg-Marquardt routine is employed. The LANL Data Bank presently contains 2879 profiles with 20 cases of DCS across nitrox, trimix, and heliox deep and decompression diving. This work establishes needed correlation between global mixed gas diving, specific bubble model, and deep stop data. Our objective is operational diving, not clinical science. The fit of bubble model to deep stop data is chi squared significant to 93%, using the logarithmic likelihood ratio of null set (actual set) to fit set. The RGBM algorithm is thus validated within the LANL Data Bank. Extensive and safe utilization of the model reported in field user statistics for tables, meters, and software also suggests real world validation, that is, one without noted nor reported DCS spikes in the field.
Wienke
B R
BR
Applied and Computational Physics Division, Los Alamos National Laboratory, LANL MS-D409, Los Alamos, NM 87545, USA. brw@lanl.gov
D000465:Algorithms; D003198:Computer Simulation; D003628:Database Management Systems; D004242:Diving; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D012306:Risk