Testing science- and energyefficiency of chained science lab

[Distelzombie]

@Distelzombie Are your setups running at 100% electricity satisfaction for the duration of the test (and then immediately falling to zero when energy runs out)? Or are there prolonged periods of operation of the test setups below 100% electricity satisfaction. Production rates of beacon-based setups are more harshly penalized by <100% electricity satisfaction than those with no beacons. Also, can you elaborate on inaccuracies in the in-game power graph? Would be helpful to be aware of any issues generally speaking.

Yes, they are. Thats just how accumulators work. Always 100% until theyre empty.
Inaccuracy in the power graph menu thing are:
A thing can use 1.99MW and the graph would show 1.9MW.
A thing can use 10.9MW and the graph would show 10MW.

Thats why I first tried to stay under 1MW.

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EDIT: Ok i found a way to count the packs. Coincidentally even individually. Im now testing if they work consistently.
However, apparently these inaccuracys are in every number ingame. So also in the count im doing.

[iceman_1212]

@Distelzombie Are your setups running at 100% electricity satisfaction for the duration of the test (and then immediately falling to zero when energy runs out)? Or are there prolonged periods of operation of the test setups below 100% electricity satisfaction. Production rates of beacon-based setups are more harshly penalized by <100% electricity satisfaction than those with no beacons. Also, can you elaborate on inaccuracies in the in-game power graph? Would be helpful to be aware of any issues generally speaking.

Yes, they are. Thats just how accumulators work. Always 100% until theyre empty.

Makes sense

Inaccuracy in the power graph menu thing are:
A thing can use 1.99MW and the graph would show 1.9MW.
A thing can use 10.9MW and the graph would show 10MW.

Thats why I first tried to stay under 1MW.

Ah okay, was not aware of this truncation. Good to know - thanks.

Would you say it makes more sense to read the science packs consumed instead of the progress bars? It appears to me as not as accurate though... hm I didnt entirely understand consumption of packs.

Regarding the science pack consumption rate - it's simply the rate at which the labs are "eating up" science packs. It would be like using iron ore consumption rate to deduce iron plate smelting rate, if we weren't provided the latter. Since we aren't shown "research cycles completed per second" in the production tab, I chose the science pack consumption rate instead as it's quantifiable and converges. I generally prefer to let stuff run in 4-8x+ for an hour but it wasn't needed in this case. (Note: science pack consumption rate is an exact indicator only when the two setups being compared have the same productivity boost %s. In our comparison above, all setups have two productivity 3s modules in the labs, so the numbers were clean.)

P.S. Whether or not to use it depends on what you're trying to measure. If trying to measure energy efficiency, I think that your method of limiting energy and looking at progress of green bar is actually quite elegant, especially in context of truncation errors at small power values.

[Distelzombie]

Did some testing with setups that count packs using combinators. Got quite some inconsistent results. For example one run used 2891 packs and the next used only 2034 while essentially doing the same work.
I increased the accumulators to 3.2GJ and that decreased the fluctuations down to 10%... still really bad. They use about 11k packs. (At least I can ramp up game speed)
I realized that measuring the science pack consumption does not include the production module bonus. Im not sure if this can be easily calculated. And the inaccuracies are bad too.
Best thing to do is use my earlier way - the progress bar! Yay!

Second thing I realized is that the 5x5 grid setup is quite bad when researching "follower robot count". It doesnt look worth it. 4x4 should be max.

If you are interested in my test setup using combinators:

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

You have to use Creative mod. Use the constant-combinator to start a run. Reset can be done by rebuilding the row of combinators. Just make a blueprint, its pretts easy and fast.

[MeduSalem]

Did some testing with setups that count packs using combinators. Got quite some inconsistent results. For example one run used 2891 packs and the next used only 2034 while essentially doing the same work.
I increased the accumulators to 3.2GJ and that decreased the fluctuations down to 10%... still really bad. They use about 11k packs. (At least I can ramp up game speed)
I realized that measuring the science pack consumption does not include the production module bonus. Im not sure if this can be easily calculated. And the inaccuracies are bad too.

May be a dumb question, but did you reset the labs after each run?

Because they might stop with whatever percent of the productivity progress bar filled.

And when you run the labs the next time they will obviously yield a different consumption rate because of that since they might carry some of the productivity effects from the previous run over.

The issue can only be resolved with removing all Productivity modules and inserting them again.

And there's no fast way to determine in which state the productivity bars end up with after each test run except if you want 50 combinators and stuff. So the only way is manual reset.

[Distelzombie]

May be a dumb question, but did you reset the labs after each run?

Nope. 5x5 grid would get a disadvantege. The first time I make tests with a new setup I let it run a while and then make a couple of tests to see if the results aer comcomcomcomcomcomcmo,..,.,..,, apparently im an idiot. The tests to see if results are compareable am i doing from a certain Savestate. This obviously results in about the same numbers all the time.

Let me do the last test again... *rolleyes*

[Distelzombie]

Ok I changed my setups. I also made it easier for me to start the test run. I created a flip-flop that kills power from the creative-source to accus and immediately connects power from accus to science setups. I changed GUI to 140% to lenghten the bar.

This time I didnt bother with times. It doesnt really matter in this test. I ran it with 240 UPS.
The accumulators have a capacity of 1750MJ. The amount of research done with this amount of Energy is shown in the picture.
This should be as accurate as possible.

Want me to test another setup?

[MeduSalem]

Looks good...

And yeah... would you like to test those 2 configurations?

setups

3x8.jpg (317.25 KiB) Viewed 4186 times

4x6.jpg (269.26 KiB) Viewed 4186 times

I'd like to see how the shape/organization of the beaconized setups actually influences the result.

[Lav]

Please test pure lab setup as well - no modules, no beacons. Otherwise you have no proper yardstick.

[MeduSalem]

Please test pure lab setup as well - no modules, no beacons. Otherwise you have no proper yardstick.

Yeah, would also be good to know.

But without PMs you will need 1.2x as many science packs... and hence 1.2x times as many resources (which then will require more energy to craft elsewhere). And that's also not a negligible factor for a lot of people. Also the space requirements are greater of course (though space is basically for free).

But from the math I did years ago a setup without PMs inside will automatically result in better energy usage and it becomes even more ridiculous with efficiency modules. It will be able to do 5 times the amount of research with the same amount of energy when compared to without any modules.


The problem there is that energy is basically free, but resources required for science packs are not... which is why eventually one is happy to save on 20% of the resources required, which is why Productivity modules are that interesting.... especially when stacked up over the crafting cascade on all intermediate items.

[iceman_1212]

I would also stick to the accumulator setup to test energy efficiency.

Just as an FYI, in order to adjust science pack consumption to reflect impact of productivity modules, we only need to multiply by science pack consumption rate by (1 + productivity %) to have an apples-to-apples comparison with a non-productivity moduled figure. So if we have setup A with 100 science packs consumed per minute with two module 3s in the labs (for a 20% production bonus) and we have setup B also with 100 science packs consumed per minute with no prod mods, set-up A has an effective research rate that is 20% higher than that of setup B. It's the same math as when we make late-game setups with beaconed and prod moduled assemblers/furnaces and we want to take the altered ratios into account.

[Distelzombie]

Looks good...

And yeah... would you like to test those 2 configurations?

I'd like to see how the shape/organization of the beaconized setups actually influences the result.

Shape does not affect efficiency. Number of beacons does:
The best setup uses only Beacons that reach at least 6 Labs.
Thats why all setups perform less good than the best from the last test.

Please test pure lab setup as well - no modules, no beacons. Otherwise you have no proper yardstick.

This one is not compareable. It reached "follower robot count 11"!

1.2x as many science packs

Huh, are you sure? I would say only 80% as much.

But from the math I did years ago a setup without PMs inside will automatically result in better energy usage and it becomes even more ridiculous with efficiency modules. It will be able to do 5 times the amount of research with the same amount of energy when compared to without any modules.

Thats about what i found.

The problem there is that energy is basically free, but resources required for science packs are not... which is why eventually one is happy to save on 20% of the resources required, which is why Productivity modules are that interesting.... especially when stacked up over the crafting cascade on all intermediate items.

I guess the last sentence was wrong? (1.2x as many science packs)

------------------------
Again: The best setup uses only Beacons that reach at least 6 Labs. Thats why the best from the last test won again.

[MeduSalem]

Looks good...

And yeah... would you like to test those 2 configurations?

I'd like to see how the shape/organization of the beaconized setups actually influences the result.

Shape does not affect efficiency. Number of beacons does:
The best setup uses only Beacons that reach at least 6 Labs.
Thats why all setups perform less good than the best from the last test.

Seems like so jugding from the numbers...

That said one is probably unable to build an endless row of Labs when expanding... and eventually one would want to make a second or third row... and then it would be favorably to share beacons between them to maximize the efficiency. Because it seems like that setup 2 with the 3 rows of labs is almost on par with setup 4 using only 2 row of labs.

Also I think you used the wrong picture for the setup 4... because it says 24 labs, 11 beacons, but there are no beacons on the picture of the setup? xD

1.2x as many science packs

Huh, are you sure? I would say only 80% as much...

The problem there is that energy is basically free, but resources required for science packs are not... which is why eventually one is happy to save on 20% of the resources required, which is why Productivity modules are that interesting.... especially when stacked up over the crafting cascade on all intermediate items.

I guess the last sentence was wrong? (1.2x as many science packs)

I meant the setup without PMs will require more science packs for the same amount of research. But one can also say that the setup with PMs requires less science packs for the same amount research... depending on the viewpoint. Either way around the PM setup will be more resource efficient.

[Distelzombie]

I meant the setup without PMs will require more science packs for the same amount of research. But one can also say that the setup with PMs requires less science packs for the same amount research... depending on the viewpoint. Either way around the PM setup will be more resource efficient.

What? Can you explain that? Make no sense to me

[MeduSalem]

I meant the setup without PMs will require more science packs for the same amount of research. But one can also say that the setup with PMs requires less science packs for the same amount research... depending on the viewpoint. Either way around the PM setup will be more resource efficient.

What? Can you explain that? Make no sense to me

You see...

The Productiviy Modules with 20% generate 1 extra research out of thin air for every 5 science packs spent. So it will eventually complete using less science packs, hence less resources.

The setup without PMs doesn't have the benefit of 1 extra research out of thin air... so you effectively you have to spent more Science packs to complete the research, hence more resources.

When compared with one another of course.

[Distelzombie]

Oh uups. I read your post wrong. I thought you meant the same setup in both cases.

I am currently trying to find a setup between 11 and 14 beacons, where they all reach at least 6 labs. (My theory: They are so similiar that it makes sense to assume there is one with better results in between) I found one with 12 beacons, but its worse than the one with 15. :/