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Wave-speed limitation on expiratory flow-a unifying concept

Dawson, S. V., Elliott, E. A.·1977·Journal of Applied Physiology

Abstract

The mechanism limiting forced expiratory flow is explained on the basis that a local flow velocity reaches the local speed of wave propagation at a point, called the choke point, in intrathoracic airways. This theoretical approach to the “waterfall effect” leads to selection of the analogy of constricted open-channel flow to apply to the elastic network of airway tubes. Quantitative results are derived for the case of negligible friction by use of the Bernoulli principle. Shapes predicted for the maximum-flow static recoil curves depend only upon the nature of the pressure-area curve at the choke point in the case of negligible friction; and the magnitude of the critical rate of flow depends on reference values of cross-sectional area and elastic modulus at the choke point, on gas density, and on the static recoil pressure. The present theoretical results are used to interpret previous experiments, but quantitative applicability is limited because of frictional effects and lack of knowledge of choke point conditions.

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Metadata

Authors
Dawson, S. V., & Elliott, E. A.
Venue
Journal of Applied Physiology
Published
1977-09-01
Volume
43
Issue
3
Pages
498-515
ISSN
  • 0161-7567 (ISSN-L)
  • 8750-7587
  • 1522-1601
OpenAlex
W2260843855
Cited by
395
References
0
FWCI
12.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Wave-speed limitation on expiratory flow-a unifying concept. https://app.cochat.ai/papers/10.1152/jappl.1977.43.3.498
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Dawson, S. V., & Elliott, E. A. (1977). Wave-speed limitation on expiratory flow-a unifying concept. Journal of Applied Physiology. https://doi.org/10.1152/jappl.1977.43.3.498

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.