A Computer Model for Lung Pressure Transient Prediction.

Abstract

The Lung Pressure Transient (LPT) Model demonstrated that computer modeling can be used as a powerful adjunct to Man. Rating testing. A previously developed Aviator's Breathing System (ABS) used to model dynamic response of the human breathing system during rapid decompression (RD) was updated to include simple mechanical models of the lung, diaphragm, and chest wall. The resulting model known as the LPT Model was created to aid estimation of the time histories of differential pressures and transient lung expansion resulting from rapid decompression. The Air Force requested that the implementation of the LPT Model be tailored to model the Aircrew Eye-Respiratory Protective (AERP) System. The LPT Model's predicition agreed fairly closely with the unmanned experimental RDs conducted at the Air Force Research Laboratory, Human Effectiveness Directorate, Flight Stress Protection Division. Using this model, the lung expansions predicted for the MBU-19/P AERP Ensemble were higher than those predicted in simulations without added breathing resistance. However, lung expansions did not seem to be unduly high even when mask and lung peak pressures were high. The authors felt this was encouraging, but the dynamic response of the lung model still needs to be validated to determine if predicting the RDs using the LPT model is safe.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1998
Accession Number
ADA349244

Entities

People

  • John Bomar

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Basic Programming Language
  • Computer Programs
  • Computers
  • Differential Equations
  • Dynamic Response
  • Governments
  • Graphical User Interface
  • Internal Pressure
  • Mechanical Properties
  • Operating Systems
  • Respiration
  • Simulations
  • Simulators
  • Static Pressure
  • Thorax

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Oncology and Biomarker-Based Cancer Detection.
  • Underwater engineering and Marine Technology.

Technology Areas

  • Autonomy