RESEARCH ARTICLE


On Validation of a Popular Sport Diving Decompression Model



B.R. Wienke*
Applied and Computational Physics Division, Los Alamos National Laboratory Los Alamos, NM 87545, USA


© 2009 Wienke et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Applied And Computational Physics Division, Los Alamos National Laboratory Los Alamos, N.M. 87545, USA; E-mail: brw@lanl.gov


Abstract

Linking deep stop model and data, we detail the LANL reduced gradient bubble model (RGBM), dynamical principles, and correlation with the LANL Data Bank. Table, profile, and meter risks are obtained from likelihood analysis, and pertinent applications include nonstop air diving, the Bennett and Maronni 2.5 minute recreational deep stop, C & C Team 450/20 multiple RB dive sequence at 1.4 atm, NEDU deep stop tests, and French Navy deep stop profiles. The 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. The 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 model is thus validated within the LANL Data Bank. Extensive and safe utilization of the model as reflected in user statistics for tables, meters, and software also points to real world validation, that is, one without noted nor reported DCS spikes among RGBM divers. Collecting real world diving data is a global alternative to differential wet and dry testing, a very precise but limited statistical procedure. The approach here for technical, mixed gas, and serious decompression diving parallels the Project Dive Exploration (PDE) effort at DAN for recreational air and nitrox diving, but does not overlap quite obviously. The issue of deep stops versus shallow stops in diving is a hotly debated topic today, and this study reaffirms the efficacy of deep stops, especially as they link naturally to the LANL dual phase bubble model and data. The operational issue of deep stops and staging is one of timing, with questions of time and depth at all stops only addressed within consistent model and ranging data frameworks.

Keywords: Decompression diving, data correlations, model validation, RGBM Data Bank, dual phase models, maximum likelihood.