Either im unable to type, you have some typos, your formular is wrong or i just got wrong what you tried to tell me.
And the solution was: I\'m just retarded^^ Thanks
And the solution was: I\'m just retarded^^ Thanks
Power Generation - Cracking the code
- Thread starter Lix
- Start date
- Joined
- May 26, 2013
- Messages
- 1,176
- Reaction score
- 938
The way i understand power generation to work is that box dimension (or cube skeleton) generators will only work to about 1,000,000 to 1,200,000 e/sec. As they get closer to this number, the gain plaetos out to be a solid 25e/sec per block.
If what you\'re suggesting is true (always 25 e/sec per block) than it would be impossible to build a ship to sustain stealth and cloak indefinately. The idea with behind the boxdim method is that you can get powerful reactors without gaining a lot of mass.
For those that don\'t know, cloaking alone costs 1000e/sec per 1 mass.
Correct me if i\'m wrong.
If what you\'re suggesting is true (always 25 e/sec per block) than it would be impossible to build a ship to sustain stealth and cloak indefinately. The idea with behind the boxdim method is that you can get powerful reactors without gaining a lot of mass.
For those that don\'t know, cloaking alone costs 1000e/sec per 1 mass.
Correct me if i\'m wrong.
- Joined
- Jul 5, 2013
- Messages
- 1
- Reaction score
- 0
After refurbishing my 600 mass warship into a jamming capable one, the cloaking seems to be 1000 per mass for cloak and 500 per mass for jam.
Easy way to then tell if your ship is capable of it is using mass times
Also using your energy regen(e/sec) divided by the above you can tell how many blocks your ship can support. Meaning that that the max mass for cloak and jam ship is 1,000,000 e/sec(effective energy regen cap) divided by 1500 which gives 666,66 mass=6666,6 blocks
Easy way to then tell if your ship is capable of it is using mass times
- X500 for jam
- X1000 for cloak
- X1500 for both
Also using your energy regen(e/sec) divided by the above you can tell how many blocks your ship can support. Meaning that that the max mass for cloak and jam ship is 1,000,000 e/sec(effective energy regen cap) divided by 1500 which gives 666,66 mass=6666,6 blocks
- Joined
- Jun 26, 2013
- Messages
- 262
- Reaction score
- 15
Your understanding (your first paragraph) is correct, and this is what the equations say as well (when they\'re entered correctly, and nothing is throwing a fit).
The power generated with efficient generator setups is enough to power cloak up to substantial ship sizes, but as you increase mass (e.g. block count), you have to dedicate more and more of the blocks to power generation, until you finally reach the point at which it becomes impossible to maintain permacloak+radar jamming because energy regen efficiency has gotten too low (because of the decreasing efficiency of the main curve, and the 25 e/sec being the only thing powering additional power generators that are added), and then the point at which it becomes impossible to maintain permacloak alone. You can maintain radar jamming up to some rather large block counts, however.
If the math is accurate, it looks like 1,333,333 e/sec is the hard limit for permacloaking (100 e/sec/block), the point at which above which it becomes impossible to cloak even a ship composed of 100% power generators, regardless of how they are arranged or how long they are (or the point at which, if a ship core\'s mass has to be cloaked too).
For reference, the equation I solved (for n) is epb=((1/(1+1.00069^-((x/3)^1.7005*n*0.333))-0.5)*2*1000000 + 25 * (x-2)*n) / ((x-2)*n). Entering any x (I tested 600 and 3000 even though 3000 would be a 1000m-in-every-direction power generator), and 100 for epb, resulted in a value for n, which I then put back into ((1/(1+1.00069^-((x/3)^1.7005*n*0.333))-0.5)*2*1000000 + 25 * (x-2)*n) to get a value for the actual e/sec, which was 1.33333e6 (1,333,333 e/sec) with both x=600 and x=3000.
For epb=150 (cloaking+radar jamming), I get 1,200,000 e/sec as the hard limit.
The power generated with efficient generator setups is enough to power cloak up to substantial ship sizes, but as you increase mass (e.g. block count), you have to dedicate more and more of the blocks to power generation, until you finally reach the point at which it becomes impossible to maintain permacloak+radar jamming because energy regen efficiency has gotten too low (because of the decreasing efficiency of the main curve, and the 25 e/sec being the only thing powering additional power generators that are added), and then the point at which it becomes impossible to maintain permacloak alone. You can maintain radar jamming up to some rather large block counts, however.
If the math is accurate, it looks like 1,333,333 e/sec is the hard limit for permacloaking (100 e/sec/block), the point at which above which it becomes impossible to cloak even a ship composed of 100% power generators, regardless of how they are arranged or how long they are (or the point at which, if a ship core\'s mass has to be cloaked too).
For reference, the equation I solved (for n) is epb=((1/(1+1.00069^-((x/3)^1.7005*n*0.333))-0.5)*2*1000000 + 25 * (x-2)*n) / ((x-2)*n). Entering any x (I tested 600 and 3000 even though 3000 would be a 1000m-in-every-direction power generator), and 100 for epb, resulted in a value for n, which I then put back into ((1/(1+1.00069^-((x/3)^1.7005*n*0.333))-0.5)*2*1000000 + 25 * (x-2)*n) to get a value for the actual e/sec, which was 1.33333e6 (1,333,333 e/sec) with both x=600 and x=3000.
For epb=150 (cloaking+radar jamming), I get 1,200,000 e/sec as the hard limit.
- Joined
- Jun 20, 2013
- Messages
- 9
- Reaction score
- 0
Greetings,
I thought I would chip in because my work with Reactor Breeder is apropos to this discussion. Lix and I have been PMing each other about this subject and I thought I might my approach, which complements the above work. The first things I noticed when I was working on figuring this out were that:
From these facts, I was able to derive the following model for Pt:
Pt(U, N) = aU^t + 25N + 1, 1 < y < 2.
Where t, a are empirical constants. The value of a seems to be pretty easy to take a stab at from the Pu for a 1x1x1 cluster, which is 114.8, so it seems reasonable that a should also be the value. The value of t is a bit more ellusive; appropriate values seem to be 1.7028 < t < 1.7029. I\'m using the latter, which favors smaller reactors and which generally results in less than 1% relative error. So my equation is:
Pt(U, N) = 114.8 * U^1.7029 + 25N + 1
I think my next step is probably going to use the squeeze theorem to find a constant which reduces error, operating under the assumption that the best value of t is somewhere in that range. I\'ll let ya\'ll know what I find out.
I thought I would chip in because my work with Reactor Breeder is apropos to this discussion. Lix and I have been PMing each other about this subject and I thought I might my approach, which complements the above work. The first things I noticed when I was working on figuring this out were that:
- The ship core (even in the absence of power generators) generates a constant 1 e/s
- The contribution of N is linear. We can infer this because adding a block what does not contribute to block dimension adds a +25e/s; adding 2 contributes +50 e/s, etc.
- The portion of power attributable to block dimension (Pt) is therefore Pu = Pt - (25N + 1)
- Pt is obviously nonlinear; however, a quadratic curve quickly of the block dimension quickly overshoots this as well.
From these facts, I was able to derive the following model for Pt:
Pt(U, N) = aU^t + 25N + 1, 1 < y < 2.
Where t, a are empirical constants. The value of a seems to be pretty easy to take a stab at from the Pu for a 1x1x1 cluster, which is 114.8, so it seems reasonable that a should also be the value. The value of t is a bit more ellusive; appropriate values seem to be 1.7028 < t < 1.7029. I\'m using the latter, which favors smaller reactors and which generally results in less than 1% relative error. So my equation is:
Pt(U, N) = 114.8 * U^1.7029 + 25N + 1
I think my next step is probably going to use the squeeze theorem to find a constant which reduces error, operating under the assumption that the best value of t is somewhere in that range. I\'ll let ya\'ll know what I find out.
So I\'ve also been trying to figure out how to turn Schema\'s notes on power recharge (from May) and I\'ll agree with Trinova that I also got 116.5 e/sec for the limit of one unique block and 126.5 when I added the second term and the actual value in the game is 140.8 e/sec.
I also went on to ask how we could change Schema\'s notes to get a better model. I found something surprising. If you change the x10 to x24 and the exponent -x*0.333 into -x/3 then the first value is correct. I\'ve calculated the first 7 values and the values still diverge. Since this underestimates I\'ve also changed the base to 1.000701.
The model: (1/(1+1.000701^(-U/3))-0.5)*2e6 + 24*(U+N)
U: Actual ... Model
--------------------
1: 140.8 ... 140.8
2: 281.7 ... 281.6
3: 422.5 ... 422.4
4: 563.4 ... 563.2
5: 704.2 ... 704
6: 845.1 ... 844.8
7: 985.9 ... 985.5
Further refined model: (1/(1+1.000701305^(-U/3))-0.5)*2e6 + 24*(U+N)
This model perfectly matches the first 50 data points up to 50 cores providing 7042.1 e/s.
EDIT: Data collected by checkerboarding power cores.
I also went on to ask how we could change Schema\'s notes to get a better model. I found something surprising. If you change the x10 to x24 and the exponent -x*0.333 into -x/3 then the first value is correct. I\'ve calculated the first 7 values and the values still diverge. Since this underestimates I\'ve also changed the base to 1.000701.
The model: (1/(1+1.000701^(-U/3))-0.5)*2e6 + 24*(U+N)
U: Actual ... Model
--------------------
1: 140.8 ... 140.8
2: 281.7 ... 281.6
3: 422.5 ... 422.4
4: 563.4 ... 563.2
5: 704.2 ... 704
6: 845.1 ... 844.8
7: 985.9 ... 985.5
Further refined model: (1/(1+1.000701305^(-U/3))-0.5)*2e6 + 24*(U+N)
This model perfectly matches the first 50 data points up to 50 cores providing 7042.1 e/s.
EDIT: Data collected by checkerboarding power cores.
- Joined
- Jun 20, 2013
- Messages
- 9
- Reaction score
- 0
So I ran my squeeze theorem solver to approximate the value of t in the above equation by combining the data that Lix and I have both collected. Here are the results:
Best is 1.7030161076417545
Width Height Depth Block Dim Actual Predicted Error Error^2
1 1 1 1.000000 140.800000 140.800000 0.000000000% 0.000000000%
1 1 2 1.333333 238.900000 238.376261 0.219229312% 0.000480615%
1 1 3 1.666667 351.100000 350.002094 0.312704591% 0.000977842%
1 1 4 2.000000 476.400000 474.766884 0.342803599% 0.001175143%
1 1 5 2.333333 614.100000 611.973156 0.346335102% 0.001199480%
1 1 6 2.666667 763.800000 761.061335 0.358557923% 0.001285638%
1 1 7 3.000000 924.900000 921.567675 0.360290346% 0.001298091%
1 1 8 3.333333 1096.900000 1093.098509 0.346566760% 0.001201085%
1 1 9 3.666667 1279.700000 1275.313410 0.342782666% 0.001175000%
1 1 10 4.000000 1472.900000 1467.913618 0.338541805% 0.001146106%
1 1 11 4.333333 1676.100000 1670.633773 0.326127754% 0.001063593%
1 1 12 4.666667 1889.200000 1883.235820 0.315698720% 0.000996657%
1 1 13 5.000000 2112.000000 2105.504392 0.307557201% 0.000945914%
1 1 14 5.333333 2344.200000 2337.243239 0.296764813% 0.000880694%
1 1 15 5.666667 2585.700000 2578.272414 0.287256308% 0.000825162%
1 1 16 6.000000 2836.300000 2828.426013 0.277614746% 0.000770699%
1 1 17 6.333333 3095.500000 3087.550348 0.256813169% 0.000659530%
1 1 18 6.666667 3364.200000 3355.502436 0.258532893% 0.000668393%
1 1 19 7.000000 3641.200000 3632.148745 0.248578900% 0.000617915%
1 1 20 7.333333 3926.800000 3917.364140 0.240293868% 0.000577411%
1 1 21 7.666667 4220.700000 4211.030994 0.229085371% 0.000524801%
1 1 22 8.000000 4523.000000 4513.038420 0.220242765% 0.000485069%
1 1 23 8.333333 4833.500000 4823.281620 0.211407466% 0.000446931%
1 1 24 8.666667 5152.200000 5141.661316 0.204547262% 0.000418396%
1 1 25 9.000000 5478.800000 5468.083253 0.195603917% 0.000382609%
1 1 26 9.333333 5813.300000 5802.457766 0.186507384% 0.000347850%
1 1 27 9.666667 6155.700000 6144.699402 0.178705879% 0.000319358%
1 1 28 10.000000 6505.900000 6494.726577 0.171742928% 0.000294956%
1 1 29 10.333333 6863.700000 6852.461278 0.163741456% 0.000268113%
1 1 30 10.666667 7229.100000 7217.828795 0.155914354% 0.000243093%
1 1 31 11.000000 7602.100000 7590.757484 0.149202403% 0.000222614%
1 1 32 11.333333 7982.500000 7971.178545 0.141828433% 0.000201153%
1 1 33 11.666667 8370.300000 8359.025834 0.134692500% 0.000181421%
1 1 34 12.000000 8765.400000 8754.235681 0.127368051% 0.000162226%
1 1 35 12.333333 9167.800000 9156.746736 0.120566154% 0.000145362%
1 1 36 12.666667 9577.400000 9566.499821 0.113811467% 0.000129531%
1 1 37 13.000000 9994.100000 9983.437795 0.106684997% 0.000113817%
1 1 38 13.333333 10417.900000 10407.505438 0.099775986% 0.000099552%
1 1 39 13.666667 10848.800000 10838.649335 0.093564866% 0.000087544%
1 1 40 14.000000 11286.700000 11276.817777 0.087556353% 0.000076661%
1 1 41 14.333333 11731.500000 11721.960666 0.081313848% 0.000066119%
1 1 42 14.666667 12183.100000 12174.029427 0.074452096% 0.000055431%
1 1 43 15.000000 12641.700000 12632.976929 0.069002360% 0.000047613%
1 1 44 15.333333 13107.000000 13098.757410 0.062886928% 0.000039548%
1 1 45 15.666667 13579.000000 13571.326411 0.056510708% 0.000031935%
1 1 46 16.000000 14057.800000 14050.640706 0.050927555% 0.000025936%
1 1 47 16.333333 14543.200000 14536.658248 0.044981515% 0.000020233%
1 1 48 16.666667 15035.300000 15029.338111 0.039652610% 0.000015723%
1 1 49 17.000000 15533.900000 15528.640438 0.033858608% 0.000011464%
1 1 50 17.333333 16039.000000 16034.526392 0.027892063% 0.000007780%
1 1 51 17.666667 16550.700000 16546.958113 0.022608632% 0.000005112%
1 1 52 18.000000 17068.800000 17065.898674 0.016997835% 0.000002889%
1 1 53 18.333333 17593.400000 17591.312038 0.011867869% 0.000001408%
1 1 54 18.666667 18124.300000 18123.163028 0.006273193% 0.000000394%
1 1 55 19.000000 18661.600000 18661.417283 0.000979109% 0.000000010%
1 1 56 19.333333 19205.200000 19206.041230 -0.004380220% 0.000000192%
1 1 57 19.666667 19755.200000 19757.002053 -0.009121917% 0.000000832%
1 1 58 20.000000 20311.300000 20314.267660 -0.014610882% 0.000002135%
1 1 59 20.333333 20873.700000 20877.806658 -0.019673837% 0.000003871%
1 1 60 20.666667 21442.300000 21447.588324 -0.024663044% 0.000006083%
1 1 61 21.000000 22017.000000 22023.582583 -0.029897730% 0.000008939%
1 1 62 21.333333 22597.900000 22605.759983 -0.034781916% 0.000012098%
1 1 63 21.666667 23184.800000 23194.091670 -0.040076557% 0.000016061%
1 1 64 22.000000 23777.900000 23788.549370 -0.044786841% 0.000020059%
1 1 65 22.333333 24376.900000 24389.105370 -0.050069410% 0.000025069%
1 1 66 22.666667 24982.000000 24995.732493 -0.054969548% 0.000030217%
1 1 67 23.000000 25593.100000 25608.404084 -0.059797695% 0.000035758%
1 1 68 23.333333 26210.100000 26227.093993 -0.064837573% 0.000042039%
1 1 69 23.666667 26833.100000 26851.776555 -0.069602674% 0.000048445%
1 1 70 24.000000 27462.000000 27482.426579 -0.074381250% 0.000055326%
1 1 71 24.333333 28096.800000 28119.019327 -0.079081343% 0.000062539%
1 1 72 24.666667 28737.400000 28761.530504 -0.083968990% 0.000070508%
1 1 73 25.000000 29383.800000 29409.936244 -0.088947802% 0.000079117%
1 1 74 25.333333 30036.100000 30064.213094 -0.093597686% 0.000087605%
1 1 75 25.666667 30694.100000 30724.338004 -0.098514061% 0.000097050%
1 1 76 26.000000 31357.900000 31390.288314 -0.103285979% 0.000106680%
1 1 77 26.333333 32027.400000 32062.041742 -0.108162828% 0.000116992%
1 1 78 26.666667 32702.600000 32739.576373 -0.113068604% 0.000127845%
1 1 79 27.000000 33383.600000 33422.870652 -0.117634562% 0.000138379%
1 1 80 27.333333 34070.100000 34111.903366 -0.122698103% 0.000150548%
1 1 81 27.666667 34762.400000 34806.653643 -0.127303188% 0.000162061%
1 1 82 28.000000 35460.200000 35507.100937 -0.132263600% 0.000174937%
1 1 83 28.333333 36163.700000 36213.225020 -0.136946772% 0.000187544%
1 1 84 28.666667 36872.700000 36925.005974 -0.141855557% 0.000201230%
1 1 85 29.000000 37587.300000 37642.424184 -0.146656408% 0.000215081%
1 1 86 29.333333 38307.400000 38365.460328 -0.151564261% 0.000229717%
1 1 87 29.666667 39033.100000 39094.095369 -0.156265757% 0.000244190%
1 1 88 30.000000 39764.200000 39828.310550 -0.161226806% 0.000259941%
1 1 89 30.333333 40500.800000 40568.087384 -0.166138406% 0.000276020%
1 1 90 30.666667 41242.900000 41313.407649 -0.170957060% 0.000292263%
1 1 91 31.000000 41990.400000 42064.253382 -0.175881588% 0.000309343%
1 1 92 31.333333 42743.300000 42820.606870 -0.180863130% 0.000327115%
1 1 93 31.666667 43501.600000 43582.450646 -0.185856719% 0.000345427%
1 1 94 32.000000 44265.300000 44349.767483 -0.190820988% 0.000364126%
1 1 95 32.333333 45034.400000 45122.540385 -0.195717907% 0.000383055%
1 1 96 32.666667 45808.800000 45900.752588 -0.200731274% 0.000402930%
1 1 97 33.000000 46588.800000 46684.387547 -0.205172803% 0.000420959%
1 1 98 33.333333 47373.500000 47473.428937 -0.210938472% 0.000444950%
1 1 99 33.666667 48163.800000 48267.860644 -0.216055719% 0.000466801%
1 1 100 34.000000 48959.400000 49067.666764 -0.221135806% 0.000489010%
2 2 2 2.000000 476.400000 474.766884 0.342803599% 0.001175143%
3 3 3 3.000000 924.900000 921.567675 0.360290346% 0.001298091%
4 4 4 4.000000 1472.900000 1467.913618 0.338541805% 0.001146106%
5 5 5 5.000000 2112.000000 2105.504392 0.307557201% 0.000945914%
6 6 6 6.000000 2836.300000 2828.426013 0.277614746% 0.000770699%
7 7 7 7.000000 3641.200000 3632.148745 0.248578900% 0.000617915%
8 8 8 8.000000 4523.000000 4513.038420 0.220242765% 0.000485069%
9 9 9 9.000000 5478.800000 5468.083253 0.195603917% 0.000382609%
10 10 10 10.000000 6505.900000 6494.726577 0.171742928% 0.000294956%
11 11 11 11.000000 7602.100000 7590.757484 0.149202403% 0.000222614%
12 12 12 12.000000 8765.400000 8754.235681 0.127368051% 0.000162226%
13 13 13 13.000000 9994.100000 9983.437795 0.106684997% 0.000113817%
14 14 14 14.000000 11286.700000 11276.817777 0.087556353% 0.000076661%
15 15 15 15.000000 12641.700000 12632.976929 0.069002360% 0.000047613%
16 16 16 16.000000 14057.800000 14050.640706 0.050927555% 0.000025936%
17 17 17 17.000000 15533.900000 15528.640438 0.033858608% 0.000011464%
18 18 18 18.000000 17068.800000 17065.898674 0.016997835% 0.000002889%
19 19 19 19.000000 18661.600000 18661.417283 0.000979109% 0.000000010%
20 20 20 20.000000 20311.300000 20314.267660 -0.014610882% 0.000002135%
21 21 21 21.000000 22017.000000 22023.582583 -0.029897730% 0.000008939%
22 22 22 22.000000 23777.900000 23788.549370 -0.044786841% 0.000020059%
23 23 23 23.000000 25593.100000 25608.404084 -0.059797695% 0.000035758%
24 24 24 24.000000 27462.000000 27482.426579 -0.074381250% 0.000055326%
25 25 25 25.000000 29383.800000 29409.936244 -0.088947802% 0.000079117%
26 26 26 26.000000 31357.900000 31390.288314 -0.103285979% 0.000106680%
27 27 27 27.000000 33383.600000 33422.870652 -0.117634562% 0.000138379%
28 28 28 28.000000 35460.200000 35507.100937 -0.132263600% 0.000174937%
29 29 29 29.000000 37587.200000 37642.424184 -0.146922846% 0.000215863%
30 30 30 30.000000 39764.200000 39828.310550 -0.161226806% 0.000259941%
Total error: 0.050609609%
Total error^2: 0.000308880%
The bounds I used were t in [1.7, 1.8], and I performed 10000 iterations. WE could of course use more data, but at this point I am not sure if we can get any better, given the precision of double.
Best is 1.7030161076417545
Width Height Depth Block Dim Actual Predicted Error Error^2
1 1 1 1.000000 140.800000 140.800000 0.000000000% 0.000000000%
1 1 2 1.333333 238.900000 238.376261 0.219229312% 0.000480615%
1 1 3 1.666667 351.100000 350.002094 0.312704591% 0.000977842%
1 1 4 2.000000 476.400000 474.766884 0.342803599% 0.001175143%
1 1 5 2.333333 614.100000 611.973156 0.346335102% 0.001199480%
1 1 6 2.666667 763.800000 761.061335 0.358557923% 0.001285638%
1 1 7 3.000000 924.900000 921.567675 0.360290346% 0.001298091%
1 1 8 3.333333 1096.900000 1093.098509 0.346566760% 0.001201085%
1 1 9 3.666667 1279.700000 1275.313410 0.342782666% 0.001175000%
1 1 10 4.000000 1472.900000 1467.913618 0.338541805% 0.001146106%
1 1 11 4.333333 1676.100000 1670.633773 0.326127754% 0.001063593%
1 1 12 4.666667 1889.200000 1883.235820 0.315698720% 0.000996657%
1 1 13 5.000000 2112.000000 2105.504392 0.307557201% 0.000945914%
1 1 14 5.333333 2344.200000 2337.243239 0.296764813% 0.000880694%
1 1 15 5.666667 2585.700000 2578.272414 0.287256308% 0.000825162%
1 1 16 6.000000 2836.300000 2828.426013 0.277614746% 0.000770699%
1 1 17 6.333333 3095.500000 3087.550348 0.256813169% 0.000659530%
1 1 18 6.666667 3364.200000 3355.502436 0.258532893% 0.000668393%
1 1 19 7.000000 3641.200000 3632.148745 0.248578900% 0.000617915%
1 1 20 7.333333 3926.800000 3917.364140 0.240293868% 0.000577411%
1 1 21 7.666667 4220.700000 4211.030994 0.229085371% 0.000524801%
1 1 22 8.000000 4523.000000 4513.038420 0.220242765% 0.000485069%
1 1 23 8.333333 4833.500000 4823.281620 0.211407466% 0.000446931%
1 1 24 8.666667 5152.200000 5141.661316 0.204547262% 0.000418396%
1 1 25 9.000000 5478.800000 5468.083253 0.195603917% 0.000382609%
1 1 26 9.333333 5813.300000 5802.457766 0.186507384% 0.000347850%
1 1 27 9.666667 6155.700000 6144.699402 0.178705879% 0.000319358%
1 1 28 10.000000 6505.900000 6494.726577 0.171742928% 0.000294956%
1 1 29 10.333333 6863.700000 6852.461278 0.163741456% 0.000268113%
1 1 30 10.666667 7229.100000 7217.828795 0.155914354% 0.000243093%
1 1 31 11.000000 7602.100000 7590.757484 0.149202403% 0.000222614%
1 1 32 11.333333 7982.500000 7971.178545 0.141828433% 0.000201153%
1 1 33 11.666667 8370.300000 8359.025834 0.134692500% 0.000181421%
1 1 34 12.000000 8765.400000 8754.235681 0.127368051% 0.000162226%
1 1 35 12.333333 9167.800000 9156.746736 0.120566154% 0.000145362%
1 1 36 12.666667 9577.400000 9566.499821 0.113811467% 0.000129531%
1 1 37 13.000000 9994.100000 9983.437795 0.106684997% 0.000113817%
1 1 38 13.333333 10417.900000 10407.505438 0.099775986% 0.000099552%
1 1 39 13.666667 10848.800000 10838.649335 0.093564866% 0.000087544%
1 1 40 14.000000 11286.700000 11276.817777 0.087556353% 0.000076661%
1 1 41 14.333333 11731.500000 11721.960666 0.081313848% 0.000066119%
1 1 42 14.666667 12183.100000 12174.029427 0.074452096% 0.000055431%
1 1 43 15.000000 12641.700000 12632.976929 0.069002360% 0.000047613%
1 1 44 15.333333 13107.000000 13098.757410 0.062886928% 0.000039548%
1 1 45 15.666667 13579.000000 13571.326411 0.056510708% 0.000031935%
1 1 46 16.000000 14057.800000 14050.640706 0.050927555% 0.000025936%
1 1 47 16.333333 14543.200000 14536.658248 0.044981515% 0.000020233%
1 1 48 16.666667 15035.300000 15029.338111 0.039652610% 0.000015723%
1 1 49 17.000000 15533.900000 15528.640438 0.033858608% 0.000011464%
1 1 50 17.333333 16039.000000 16034.526392 0.027892063% 0.000007780%
1 1 51 17.666667 16550.700000 16546.958113 0.022608632% 0.000005112%
1 1 52 18.000000 17068.800000 17065.898674 0.016997835% 0.000002889%
1 1 53 18.333333 17593.400000 17591.312038 0.011867869% 0.000001408%
1 1 54 18.666667 18124.300000 18123.163028 0.006273193% 0.000000394%
1 1 55 19.000000 18661.600000 18661.417283 0.000979109% 0.000000010%
1 1 56 19.333333 19205.200000 19206.041230 -0.004380220% 0.000000192%
1 1 57 19.666667 19755.200000 19757.002053 -0.009121917% 0.000000832%
1 1 58 20.000000 20311.300000 20314.267660 -0.014610882% 0.000002135%
1 1 59 20.333333 20873.700000 20877.806658 -0.019673837% 0.000003871%
1 1 60 20.666667 21442.300000 21447.588324 -0.024663044% 0.000006083%
1 1 61 21.000000 22017.000000 22023.582583 -0.029897730% 0.000008939%
1 1 62 21.333333 22597.900000 22605.759983 -0.034781916% 0.000012098%
1 1 63 21.666667 23184.800000 23194.091670 -0.040076557% 0.000016061%
1 1 64 22.000000 23777.900000 23788.549370 -0.044786841% 0.000020059%
1 1 65 22.333333 24376.900000 24389.105370 -0.050069410% 0.000025069%
1 1 66 22.666667 24982.000000 24995.732493 -0.054969548% 0.000030217%
1 1 67 23.000000 25593.100000 25608.404084 -0.059797695% 0.000035758%
1 1 68 23.333333 26210.100000 26227.093993 -0.064837573% 0.000042039%
1 1 69 23.666667 26833.100000 26851.776555 -0.069602674% 0.000048445%
1 1 70 24.000000 27462.000000 27482.426579 -0.074381250% 0.000055326%
1 1 71 24.333333 28096.800000 28119.019327 -0.079081343% 0.000062539%
1 1 72 24.666667 28737.400000 28761.530504 -0.083968990% 0.000070508%
1 1 73 25.000000 29383.800000 29409.936244 -0.088947802% 0.000079117%
1 1 74 25.333333 30036.100000 30064.213094 -0.093597686% 0.000087605%
1 1 75 25.666667 30694.100000 30724.338004 -0.098514061% 0.000097050%
1 1 76 26.000000 31357.900000 31390.288314 -0.103285979% 0.000106680%
1 1 77 26.333333 32027.400000 32062.041742 -0.108162828% 0.000116992%
1 1 78 26.666667 32702.600000 32739.576373 -0.113068604% 0.000127845%
1 1 79 27.000000 33383.600000 33422.870652 -0.117634562% 0.000138379%
1 1 80 27.333333 34070.100000 34111.903366 -0.122698103% 0.000150548%
1 1 81 27.666667 34762.400000 34806.653643 -0.127303188% 0.000162061%
1 1 82 28.000000 35460.200000 35507.100937 -0.132263600% 0.000174937%
1 1 83 28.333333 36163.700000 36213.225020 -0.136946772% 0.000187544%
1 1 84 28.666667 36872.700000 36925.005974 -0.141855557% 0.000201230%
1 1 85 29.000000 37587.300000 37642.424184 -0.146656408% 0.000215081%
1 1 86 29.333333 38307.400000 38365.460328 -0.151564261% 0.000229717%
1 1 87 29.666667 39033.100000 39094.095369 -0.156265757% 0.000244190%
1 1 88 30.000000 39764.200000 39828.310550 -0.161226806% 0.000259941%
1 1 89 30.333333 40500.800000 40568.087384 -0.166138406% 0.000276020%
1 1 90 30.666667 41242.900000 41313.407649 -0.170957060% 0.000292263%
1 1 91 31.000000 41990.400000 42064.253382 -0.175881588% 0.000309343%
1 1 92 31.333333 42743.300000 42820.606870 -0.180863130% 0.000327115%
1 1 93 31.666667 43501.600000 43582.450646 -0.185856719% 0.000345427%
1 1 94 32.000000 44265.300000 44349.767483 -0.190820988% 0.000364126%
1 1 95 32.333333 45034.400000 45122.540385 -0.195717907% 0.000383055%
1 1 96 32.666667 45808.800000 45900.752588 -0.200731274% 0.000402930%
1 1 97 33.000000 46588.800000 46684.387547 -0.205172803% 0.000420959%
1 1 98 33.333333 47373.500000 47473.428937 -0.210938472% 0.000444950%
1 1 99 33.666667 48163.800000 48267.860644 -0.216055719% 0.000466801%
1 1 100 34.000000 48959.400000 49067.666764 -0.221135806% 0.000489010%
2 2 2 2.000000 476.400000 474.766884 0.342803599% 0.001175143%
3 3 3 3.000000 924.900000 921.567675 0.360290346% 0.001298091%
4 4 4 4.000000 1472.900000 1467.913618 0.338541805% 0.001146106%
5 5 5 5.000000 2112.000000 2105.504392 0.307557201% 0.000945914%
6 6 6 6.000000 2836.300000 2828.426013 0.277614746% 0.000770699%
7 7 7 7.000000 3641.200000 3632.148745 0.248578900% 0.000617915%
8 8 8 8.000000 4523.000000 4513.038420 0.220242765% 0.000485069%
9 9 9 9.000000 5478.800000 5468.083253 0.195603917% 0.000382609%
10 10 10 10.000000 6505.900000 6494.726577 0.171742928% 0.000294956%
11 11 11 11.000000 7602.100000 7590.757484 0.149202403% 0.000222614%
12 12 12 12.000000 8765.400000 8754.235681 0.127368051% 0.000162226%
13 13 13 13.000000 9994.100000 9983.437795 0.106684997% 0.000113817%
14 14 14 14.000000 11286.700000 11276.817777 0.087556353% 0.000076661%
15 15 15 15.000000 12641.700000 12632.976929 0.069002360% 0.000047613%
16 16 16 16.000000 14057.800000 14050.640706 0.050927555% 0.000025936%
17 17 17 17.000000 15533.900000 15528.640438 0.033858608% 0.000011464%
18 18 18 18.000000 17068.800000 17065.898674 0.016997835% 0.000002889%
19 19 19 19.000000 18661.600000 18661.417283 0.000979109% 0.000000010%
20 20 20 20.000000 20311.300000 20314.267660 -0.014610882% 0.000002135%
21 21 21 21.000000 22017.000000 22023.582583 -0.029897730% 0.000008939%
22 22 22 22.000000 23777.900000 23788.549370 -0.044786841% 0.000020059%
23 23 23 23.000000 25593.100000 25608.404084 -0.059797695% 0.000035758%
24 24 24 24.000000 27462.000000 27482.426579 -0.074381250% 0.000055326%
25 25 25 25.000000 29383.800000 29409.936244 -0.088947802% 0.000079117%
26 26 26 26.000000 31357.900000 31390.288314 -0.103285979% 0.000106680%
27 27 27 27.000000 33383.600000 33422.870652 -0.117634562% 0.000138379%
28 28 28 28.000000 35460.200000 35507.100937 -0.132263600% 0.000174937%
29 29 29 29.000000 37587.200000 37642.424184 -0.146922846% 0.000215863%
30 30 30 30.000000 39764.200000 39828.310550 -0.161226806% 0.000259941%
Total error: 0.050609609%
Total error^2: 0.000308880%
The bounds I used were t in [1.7, 1.8], and I performed 10000 iterations. WE could of course use more data, but at this point I am not sure if we can get any better, given the precision of double.
Thanks to ThyLordRoot for his work! I know how time consuming it can be to obtain experimental data. I\'m curious how you have obtained actual values with more than one decimal place because that is all I could get the game to tell me.
I had an insight while I was refining this morning. 1.0007 almost fit the first value but underestimated the rest. 1.000701 fit the first perfectly and was much closer but still underestimating. 1.0007013 fit the first 12 perfectly and then overestimated. 1.00070129 was a worse fit. 1.000701305 fit the first 50 perfectly and I gave up before finding an error and the reason relates to something you said: \"WE could of course use more data, but at this point I am not sure if we can get any better.\"
I think 100 samples might be an adequete number but the ones you have are all clustered together and don\'t cover more interesting parts of the curve. I just have no idea where 1% or 10% loss happens; answering that question is one of the reasons I\'m trying to get my head around how power regeneration works.
I had an insight while I was refining this morning. 1.0007 almost fit the first value but underestimated the rest. 1.000701 fit the first perfectly and was much closer but still underestimating. 1.0007013 fit the first 12 perfectly and then overestimated. 1.00070129 was a worse fit. 1.000701305 fit the first 50 perfectly and I gave up before finding an error and the reason relates to something you said: \"WE could of course use more data, but at this point I am not sure if we can get any better.\"
I think 100 samples might be an adequete number but the ones you have are all clustered together and don\'t cover more interesting parts of the curve. I just have no idea where 1% or 10% loss happens; answering that question is one of the reasons I\'m trying to get my head around how power regeneration works.
- Joined
- Jun 26, 2013
- Messages
- 262
- Reaction score
- 15
It appears that CyaNox has the correct numbers.
Tested with this on my TI-89 to represent the power structures in the construction I\'m working on currently:
{179*3, 178*3, 177*3, 176*3, 42, 5} -> x: (1/(1+(1.000696)^(sum(-(x/3)^(1.7))*.333))-.5)*2000000+25*(sum(x)-2*dim(x))
(Where 42 indicates a 40 long, 2 wide, 1 high line, and 5 indicates a 3x1x1 line, and the others are 3d and go an equal distance in all directions, but none of them have non-optimal (wasted) blocks)
Result: 1050057.1038168.
StarMade\'s build mode says: 1050057.1 e/sec.
Verdict: Magnificent.
(Also worked for small amounts of power generators, etc.)
Tested with this on my TI-89 to represent the power structures in the construction I\'m working on currently:
{179*3, 178*3, 177*3, 176*3, 42, 5} -> x: (1/(1+(1.000696)^(sum(-(x/3)^(1.7))*.333))-.5)*2000000+25*(sum(x)-2*dim(x))
(Where 42 indicates a 40 long, 2 wide, 1 high line, and 5 indicates a 3x1x1 line, and the others are 3d and go an equal distance in all directions, but none of them have non-optimal (wasted) blocks)
Result: 1050057.1038168.
StarMade\'s build mode says: 1050057.1 e/sec.
Verdict: Magnificent.
(Also worked for small amounts of power generators, etc.)
I can\'t math so I prefer staggering power blocks with each block being \"unique\" as above. What\'s the efficiency difference between doing this overall or on massive ships amassing power storage?
XOXOX
OXOXO
XOXOX
OXOXO
XOXOX
And alternating for the next level on the grid such that the power blocks never touch:
OXOXO
XOXOX
OXOXO
XOXOX
OXOXO
In a 5x5x5 this gets me 8873e/s using 63 power blocks.
In the setup I have on my ship (16000 mass) as far as cloaking and jamming is concerned I amassed power storage, which is all linked giving me roughly 7-10 seconds of cloak/jam... which I don\'t believe is too shabby for a ship that size. I\'m not keen on how many storage blocks I have, but each additional adds another ~3,000 power storage which seems a lot better per space than adding additional power generators (140e/s).
I suppose what I\'m really getting at is: at which point is power storage more efficient that power generation on larger scales?
XOXOX
OXOXO
XOXOX
OXOXO
XOXOX
And alternating for the next level on the grid such that the power blocks never touch:
OXOXO
XOXOX
OXOXO
XOXOX
OXOXO
In a 5x5x5 this gets me 8873e/s using 63 power blocks.
In the setup I have on my ship (16000 mass) as far as cloaking and jamming is concerned I amassed power storage, which is all linked giving me roughly 7-10 seconds of cloak/jam... which I don\'t believe is too shabby for a ship that size. I\'m not keen on how many storage blocks I have, but each additional adds another ~3,000 power storage which seems a lot better per space than adding additional power generators (140e/s).
I suppose what I\'m really getting at is: at which point is power storage more efficient that power generation on larger scales?
MrFURB
Madman of the Girders
- Joined
- Jan 31, 2013
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- 413
The box dimension bonuses for energy regen stop at 1,000,000 bonus, not ocunting the stable 25 energy regen per block. Once each regen block you place only gives you 25 regen, then it\'s better to dump space into storage because if you can get enough storage it can last you through a whole fight. I\'ve seen projects easily get quite a few million energy storage.
...but that 1 million doesn\'t prevent you from having multiple box dimensions I would imagine... which makes the former really inefficient for the space, depending on how many blocks it takes to make a box or boxes with that amount of regen.
What I\'m running has 121 million power capacity, but I think the 12,000 blocks that I put into power generators is running pretty poorly for the space considering I have only 1.2mil e/s. Single block generators still suffer from diminishing returns in higher numbers.
Since the idea of having massive energy output is generally geared towards cloaking/jamming permanence isn\'t really an option when you need 24 million regen to stay cloaked permanently, but I\'ve been doing it all wrong sooooo maybe? (the goal I had was 30 seconds).
What I\'m running has 121 million power capacity, but I think the 12,000 blocks that I put into power generators is running pretty poorly for the space considering I have only 1.2mil e/s. Single block generators still suffer from diminishing returns in higher numbers.
Since the idea of having massive energy output is generally geared towards cloaking/jamming permanence isn\'t really an option when you need 24 million regen to stay cloaked permanently, but I\'ve been doing it all wrong sooooo maybe? (the goal I had was 30 seconds).
You really, really want enough power regen to run the thrusters constantly. A very massive ship that doesn\'t have any power past 1.2 million is going to be kind of slow.
A reasonable compromise is to have enough power to accelerate or shoot but not both at the same time, and after that you want to connect enough storage to carry you through rough spots where you need to do both for a few seconds.
A reasonable compromise is to have enough power to accelerate or shoot but not both at the same time, and after that you want to connect enough storage to carry you through rough spots where you need to do both for a few seconds.
All you need to know about power regeneration, is that: \"When you add 1 power block to power group, it must increase the overall size of the group in X, Y or Z direction.\" By doing so, your energy regen will grow at exponential rate. 1 large group is much more power than 2 equal sized groups side-by-side. This means that these are both valid formations, since each block increases dimensions:
xxxxx
..x
..x
xxx
....x
....x
....xxxxx
...........xx
.............xx
xxxxx
..x
..x
xxx
....x
....x
....xxxxx
...........xx
.............xx
- Joined
- Jul 21, 2013
- Messages
- 156
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- 5
and people wonder why I put \'wings\' on my ships.... to hold large box dimension arrays, of course!
- Joined
- Dec 19, 2012
- Messages
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- 0
I\'m sorry, I\'m getting confused, here. It may be because I\'m not familiar with the TI-89.
What does this mean? \"{179*3, 178*3, 177*3, 176*3, 42, 5} -> x:\" Is your calculator taking \"179*3\", converting that into 537, putting 536 into the expression for \"x\", spitting out a result, and then moving on to the next input \"178*3\"? Is your calculator spitting out a list of 6 outputs?
How are you defining \"sum(x)\" and \"dim(x)\", here? I\'m especially confused by \"sum(-(x/3)^(1.7))\". In my mind, \"sum(1,2,3,4,5) = 15\", but I see no commas inside the parentheses in your equation.
I was under the assumption that different power generator groups had nothing to do with each other so long as they didn\'t directly connect, diagonals not counting. (If they don\'t directly connect, then they\'re different power groups, by definition.) The total power generation of the ship is merely the sum of the regenerations of the different power groups, whatever the calculation for each power group happens to be. Is that correct or wrong?
I\'m trying to put this into a spreadsheet to test it against my data, but I can\'t understand the formula enough to get my spreadsheet to understand it. Thank you in advance.
What does this mean? \"{179*3, 178*3, 177*3, 176*3, 42, 5} -> x:\" Is your calculator taking \"179*3\", converting that into 537, putting 536 into the expression for \"x\", spitting out a result, and then moving on to the next input \"178*3\"? Is your calculator spitting out a list of 6 outputs?
How are you defining \"sum(x)\" and \"dim(x)\", here? I\'m especially confused by \"sum(-(x/3)^(1.7))\". In my mind, \"sum(1,2,3,4,5) = 15\", but I see no commas inside the parentheses in your equation.
I was under the assumption that different power generator groups had nothing to do with each other so long as they didn\'t directly connect, diagonals not counting. (If they don\'t directly connect, then they\'re different power groups, by definition.) The total power generation of the ship is merely the sum of the regenerations of the different power groups, whatever the calculation for each power group happens to be. Is that correct or wrong?
I\'m trying to put this into a spreadsheet to test it against my data, but I can\'t understand the formula enough to get my spreadsheet to understand it. Thank you in advance.