Physiological Event Prediction in Evaluations of Underwater Breathing Apparatus

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Physiological Event Prediction in Evaluations of Underwater Breathing Apparatus

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Title: Physiological Event Prediction in Evaluations of Underwater Breathing Apparatus
Author: Clarke, JR
Abstract: This report describes the use of Predict, in-house designed prediction software. NEDU performance goals for underwater breathing apparatus (UBA) based on ventilatory flow rate have served the Navy well for decades. Nevertheless, gas density is a major determinant of respiratory loading at depth, based on both experimental evidence and simple models of fluid mechanics. An understanding of the influence of flow rate and gas density are vital to understanding the performance characteristics of UBA, and the probable tolerance of a diver to those influences. Over a decade ago NEDU developed a constant respiratory impedance model for determining acceptable pressure drops across UBA, and created software to predict the tolerance of divers to UBA under varying dive conditions. The so-called maximum respiratory impedance model was calibrated on Navy manned dive results, and this paper describes the use of that model and the associated Predict software to predict diver tolerance based on unmanned data. It is arguably a more complete approach compared to methods already in Navy use, and is particularly useful in estimating the risk of diving UBA made inadequate by design or accident.
Description: Citation Status: Active; Citation Classification: Unclassified; Title Classification: Unclassified; Report Classification: Unclassified; Identifier Classification: Unclassified; Abstract Classification: Unclassified; Distribution Limitation(s): 01 - APPROVED FOR PUBLIC RELEASE; Information provided by the Department of Defense and the Defense Technical Information Center (http://www.dtic.mil/) is considered public information and may be distributed or copied unless otherwise specified. Use of appropriate byline/photo/image credits is requested.
URI: http://hdl.handle.net/123456789/11065
Date: 2016

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