Academic Journal
Statistical correlations and risk analyses techniques for a diving dual phase bubble model and data bank using massively parallel supercomputers
| Title: | Statistical correlations and risk analyses techniques for a diving dual phase bubble model and data bank using massively parallel supercomputers |
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| Authors: | Wienke, B.R.1 brw@lanl.gov, O’Leary, T.R.2 |
| Source: | Computers in Biology & Medicine. May2008, Vol. 38 Issue 5, p583-600. 18p. |
| Abstract: | Abstract: Linking model and data, we detail the LANL diving reduced gradient bubble model (RGBM), dynamical principles, and correlation with data in the LANL Data Bank. Table, profile, and meter risks are obtained from likelihood analysis and quoted for air, nitrox, helitrox no-decompression time limits, repetitive dive tables, and selected mixed gas and repetitive profiles. Application analyses include the EXPLORER decompression meter algorithm, NAUI tables, University of Wisconsin Seafood Diver tables, comparative NAUI, PADI, Oceanic NDLs and repetitive dives, comparative nitrogen and helium mixed gas risks, USS Perry deep rebreather (RB) exploration dive,world record open circuit (OC) dive, and Woodville Karst Plain Project (WKPP) extreme cave exploration profiles. The algorithm has seen extensive and utilitarian application in mixed gas diving, both in recreational and technical sectors, and forms the bases forreleased tables 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 with error norm. Appendices sketch the numerical methods, and list reports from field testing for (real) mixed gas diving. A Monte Carlo-like sampling scheme for fast numerical analysis of the data is also detailed, as a coupled variance reduction technique and additional check on the canonical approach to estimating diving risk. The method suggests alternatives to the canonical approach. This work represents a first time correlation effort linking a dynamical bubble model with deep stop data. Supercomputing resources are requisite to connect model and data in application. [Copyright &y& Elsevier] |
| Subject Terms: | *UNIVERSITIES & colleges, *ALGORITHMS, *SWIMMERS |
| Geographic Terms: | UNITED States |
| ISSN: | 00104825 |
| DOI: | 10.1016/j.compbiomed.2008.02.006 |
| Database: | Academic Search Index |
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