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Decompression theory
Papadopoulou, V.; Chatterton, J.; Popov, G.; Eckersley, R.J.; Balestra, C.; Karapantsios, T.D.; Tang, M.-X.; Cialoni, D.; Kot, J. (2017). Decompression theory, in: Balestra, C. et al. The science of diving. Things your instructor never told you. pp. [88-115]
In: Balestra, C.; Germonpré, P. (2017). The science of diving. Things your instructor never told you. Lambert Academic Publishing/Éditions Acrodacrolivres: Villers-la-Ville. ISBN 978-2-512007-36-4. [262] pp.
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| Auteurs | | Top |
- Papadopoulou, V.
- Chatterton, J.
- Popov, G.
|
- Eckersley, R.J.
- Balestra, C.
- Karapantsios, T.D.
|
- Tang, M.-X.
- Cialoni, D.
- Kot, J.
|
| Abstract |
By using dive tables or diving computers, divers are effectively using decompression algorithms to manage the risk of developing decompression sickness (DCS). They dictate the time allowed at each depth before the dive converts from no-decompression dive into a decompression dive, as well as the decompression stops needed for a decompression dive (time to spend at various depths on the way up to the surface). These algorithms are calculations that follow from the principles of a given decompression theory; of these, different ones exist with wildly different approaches. It is clear that the principles of those algorithms are very different in terms of what the mathematical modelling translates to in reality. This highlights that we still do not know exactly how bubbles form and grow in the body and when they trigger DCS. At this time, DCS risk is relatively well-managed because all the algorithms discussed have been fit to real percentages of DCS and therefore "tweaked" to be reasonably safe. However, given enough degrees of freedom (constants to fit to data), any algorithm will end up giving the same decompression profile safety, but this does not mean the "physics/physiology" modelling behind it, is correct and this is an argument for probabilistic decompression theory. In addition, since the risk for DCS has been shown dependent on numerous physiological variables, a personalised decompression schedule is desirable. The research therefore needs to focus on how physiological factors affect bubble number and bubble growth, so this information can be incorporated into decompression algorithms. |
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