On a related note, I have been working on an app that helps determine the correct grinder setting when dialing in espresso. After logging two shots with the same setup (grinder, coffeee machine, basket etc), it then uses machine learning (and some other stuff that I am still improving) to predict the correct setting for your grinder based on the machine temperature, the weight of the shot etc.
Its far from perfect when it comes to predictions right now but I expect to have massive improvements over the coming weeks. For now it works ok as an espresso log at least.
I'm hoping after a few tweaks I can save people a lot of wasted coffee!
There's a simple differential equation often taught in intro calc courses, "Newton's Law of Cooling/Heating," which basically says that the rate of heat loss is proportional to the difference in temperature between a substance and its environment. I'm curious what that'd look like here. It's a very simple model, of course, not taking into account all the variables that Dynomight points out, but if a simple model can be nearly as predictive as more complex models...
I'm also curious to see the details of the models that Dynomight's LLMs produced!
It looks like a lot of them are missing something big. I'd think the two big ones are the evaporative cooling as you pour into the cup, and heating up the cup (by convection) itself. The convective cooling to the air is tertiary, but important (and conduction of the mug to the table probably isn't completely negligible). If there's only one exponential, they're definitely doing something wrong.
I'd like to see a sensitivity study to see how much those terms would need to be changed to match within a few %. Exponentials are really tweaky!
It's a mix of course, but I think it should be mainly that and evaporative cooling. Evap is _very_ effective but will fall off rapidly as you get away from boiling. The conduction into the mug will depend a lot on the mug material but will slow down a lot as the mug approaches the water temperature.
I'd be very interested in seeing separate graphs for each major component and how they add up to the total. Even asking the LLMs to separate it out might improve some of their results, would be interesting to try that too.
probably dominated by the cup as the ambient temperature initially and then as air/the counter top as the ambient temperature on the longer time scale, once the cup and the liquid near equilibrium
It does? There is a fast drop followed by a long decay, exponential in fact. The cooling rate is proportional to the temperature difference, so the drop is sharpest at the very beginning when the object is hottest.
The problem is both highly complex, but fairly easy to model. Engineers have been doing this for over a century.
Of all the cooling modes identified by the author, one will dominate. And it is almost certainly going to have an exponential relationship with time.
Once this mode decays below the next fastest will this new fastest mode will dominate.
All the LLM has to do, then, is give a reasonable estimate for the Q for:
$T = To exp(-Qt)$
This is not too hard to fit if your training set has the internet within itself.
I would have been more interested to see the equations than the plots, but I would have been most interested to see the plots in log space. There, each cooling mode is a straight line.
The data collected, btw, appears to have at least two exponential modes within it.
[The author did not list the temperature dependance of heat capacity, which for pure water is fairly constant]
It isn't that surprising that it works well, this problem is fairly well known and some simple heat equations would lead to the result, about which there is a lot of training data online.
The water temperature drops quickly because the room temperature ceramic mug is getting heated to near equilibrium with the water. If you used a vacuum sealed mug(thermos) then the water temp would drop a bit but not much at all initially.
https://apps.apple.com/ph/app/grind-finer-app/id6760079211
Its far from perfect when it comes to predictions right now but I expect to have massive improvements over the coming weeks. For now it works ok as an espresso log at least.
I'm hoping after a few tweaks I can save people a lot of wasted coffee!
I'm also curious to see the details of the models that Dynomight's LLMs produced!
I'd like to see a sensitivity study to see how much those terms would need to be changed to match within a few %. Exponentials are really tweaky!
I'd be very interested in seeing separate graphs for each major component and how they add up to the total. Even asking the LLMs to separate it out might improve some of their results, would be interesting to try that too.
dT/dt = -k(T_0 - T_room)
so T(t) = T_room + (T_0 - T_room) exp(-kt)
exp(-x) has a fast drop off then levels off.
scroll down, these graphs just don't look similar.
Of all the cooling modes identified by the author, one will dominate. And it is almost certainly going to have an exponential relationship with time.
Once this mode decays below the next fastest will this new fastest mode will dominate.
All the LLM has to do, then, is give a reasonable estimate for the Q for:
$T = To exp(-Qt)$
This is not too hard to fit if your training set has the internet within itself.
I would have been more interested to see the equations than the plots, but I would have been most interested to see the plots in log space. There, each cooling mode is a straight line.
The data collected, btw, appears to have at least two exponential modes within it.
[The author did not list the temperature dependance of heat capacity, which for pure water is fairly constant]
Imo no, this seems like something that would be in multiple scientific papers so a LLM would be able to generate the answer based on predictive text.
Impossible, since it is chaotic.
But a T(t) model should not be too hard for an LLM with a basic heat transfer book in its training set.