Buhlmann questions

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Never seen a "ZH-L8", know nothing about it.)
ZH-L8 (ADT) was implemeted bei one Bühlmanns co-authors, E.B. Völlm, in a wide range of Uwatec computers, e.g. the SMART series and the original Gallileo series. It only was replaced with a ZH-L16 model with the advent of the Uwatec trimix models.
 
ZH-L8 (ADT) was implemeted bei one Bühlmanns co-authors, E.B. Völlm, in a wide range of Uwatec computers, e.g. the SMART series and the original Gallileo series. It only was replaced with a ZH-L16 model with the advent of the Uwatec trimix models.

Thanks but that doesn't change what I said: I still don't know anything about it.

E.g. I know from "Decompression - Decompression Sickness" that ZH-L12 is based on empirical data, has 16 tissue compartments but only 12 sets of M-values, compartment half-times start at 2.65 minutes rather than 4 or 5, and I have the actual numbers.

All I know about ZH-L8ADT is what I read on Wikipedia.
 
Thanks but that doesn't change what I said: I still don't know anything about it.

E.g. I know from "Decompression - Decompression Sickness" that ZH-L12 is based on empirical data, has 16 tissue compartments but only 12 sets of M-values, compartment half-times start at 2.65 minutes rather than 4 or 5, and I have the actual numbers.

All I know about ZH-L8ADT is what I read on Wikipedia.
Don’t trust the wiki pages. They are not very good with this specific subject. I seem to remember there was also a claim that C was simpler than B for performance reasons. That has gone now.
 
I think the GF solution is an elegant "hack" in that it provides a smooth adjustment of the entire offgassing gradient from first stop all the way to the surface. There are others ways, less elegant, that provide conservatism such as adding depth or time at various parts of the dive. It's also fairly easy to understand the application of GF's when looking at plots of depth vs. TC pressure. The GF's as percentages are easily applied to the existing Buhlmann formulas and dive algorithms. The hard part is figuring out how they apply to individual physiology for any given dive profile.

Raw Buhlmann already changes the m values from the first stop to the surface. What GF does is essentially saying that Buhlmann’s choices of a and b coefficients were wrong. Now he was the bloke with the human subjects so it is hard to justify ignoring his numbers. GF sort of abdicates responsibility for changing the coefficients. Have the user make the choice and they can take the blame for their own bad choices.

We could invent a new scheme FG which is based on an arbitrary set of a/b pairs I make up before breakfast, then tune (with FG lo and FG hi) it to come up with plans that look like sensible plans. It would owe nothing to Buhlmann. We would conclude that with an NDL at 30m of 22 a FG hi of 60 was a bit strong and 55 was sensible etc etc.
 
I don't think using gradient factors (GF's) means Buhlmann's choice of a and b coefficients were wrong at all. After all, you cite the experiments with humans (and goats). Right now I'm too lazy to look it up but I remember reading that Buhlmann's raw values (GF = 100/100) were fine for most dives up to some depth. It was deeper and repetitive dives that required more conservatism so GF's were introduced to provide the necessary conservatism. I don't think we need to throw the baby out with the bath water!
 
I don't think using gradient factors (GF's) means Buhlmann's choice of a and b coefficients were wrong at all. After all, you cite the experiments with humans (and goats). Right now I'm too lazy to look it up but I remember reading that Buhlmann's raw values (GF = 100/100) were fine for most dives up to some depth. It was deeper and repetitive dives that required more conservatism so GF's were introduced to provide the necessary conservatism. I don't think we need to throw the baby out with the bath water!
So I may be totally wrong but if you suddenly found that the model had issues with some depth.

Would you:
  1. examine the underlying assumptions and check if something was missed in the original study
  2. Slap a quick fix to make it more conservative by drawing a line in the middle of two other lines
I am not saying #2 is stupid, as it is better to be alive than dead, but by putting quick fixes you may sometimes be at risk of missing something more fundamental ?

For example, my understanding is that people use different GFs but I have not met many people who can explain to me why they use this set of values and not something else.

This being said, I am a fairly pragmatic person and won’t lose too much sleep over that :)

Note: I didn’t read the original research and I do not know how the GFs were decided to be a good solution compared to any other alternative. Also my experience is very limited ...
 
So I may be totally wrong but if you suddenly found that the model had issues with some depth.

Would you:
  1. examine the underlying assumptions and check if something was missed in the original study
  2. Slap a quick fix to make it more conservative by drawing a line in the middle of two other lines
I am not saying #2 is stupid, as it is better to be alive than dead, but by putting quick fixes you may sometimes be at risk of missing something more fundamental ?

For example, my understanding is that people use different GFs but I have not met many people who can explain to me why they use this set of values and not something else.

This being said, I am a fairly pragmatic person and won’t lose too much sleep over that :)

Note: I didn’t read the original research and I do not know how the GFs were decided to be a good solution compared to any other alternative. Also my experience is very limited ...
There was no research for GF. It was a random guess as to how introduce some extra conservatism. At that time deep stops, Pyle stops and bubble models were popular and so part of the design was the feeling that there was a need to protect the fast tissues by stopping deeper. Some of Bakers writing says this, except maybe he doesn’t call it a guess.

As I mentioned earlier, there are some particular circumstances which completely throw the plans out the window, then what GF you happenEd to choose or how they are applied is not very important.

soon someone will come along and say to lay high low game with deco. Feel tired do more, not tired do less etc
 
I don't think using gradient factors (GF's) means Buhlmann's choice of a and b coefficients were wrong at all. After all, you cite the experiments with humans (and goats). Right now I'm too lazy to look it up but I remember reading that Buhlmann's raw values (GF = 100/100) were fine for most dives up to some depth. It was deeper and repetitive dives that required more conservatism so GF's were introduced to provide the necessary conservatism. I don't think we need to throw the baby out with the bath water!
GFs were a way to bend the Buhlmann curve to correspond to the curve generated by deep stop algorithms such as RGBM and VPM. I don't think conservatism was a factor since in it's initial iteration these models were suppose to get you out of the water faster. Sadly that turned out to be a bad idea.
 
I don't think conservatism was a factor since in it's initial iteration these models were suppose to get you out of the water faster. Sadly that turned out to be a bad idea.

You may want to re-read Erik Baker's paper a few times and think again.

The addition of deep stops in a profile will generally increase the time required at the shallow stops as well as the overall decompression time. However, if truly "sufficient decompression" is the result, then the concept of "economic decompression" is not really compromised
 
...
I am not saying #2 is stupid, as it is better to be alive than dead, but by putting quick fixes you may sometimes be at risk of missing something more fundamental ?

Remember, at ambient pressure you're not off-gassing at all, so there's zero risk of any DCS, and at M-value line you're at the point where risk of DCS becomes "too great".

It follows that anywhere in-between the risk is acceptable.

For example, my understanding is that people use different GFs but I have not met many people who can explain to me why they use this set of values and not something else.

Some actually can explain GF in 70..85 range and GF Low at 83% of GF High (or was it 86%?) -- of course their ideas may later turn out like deep stops: "sounded good at the time".
 
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