Gizmodo Takes on Pinewood Racing

Now it gets a bit strange-
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so even with a heavy car, as long as the friction on the wheel is the same in both cases- they stop the same. This even infers that as long as the DFW was on the rail all the way down the track for both a heavy or light car- same effect. Now back to a front weighted car- it might need to be tuned a bit to roll the same as a rear weighted car- but the speed difference is totally within the range predicted by the slight height above the track difference calculation.
So when we tune and see a big difference in times, is it because when close to zero drift, the car can hit the track any number of times losing a lot of energy each time, and as we add drift we get it to roll completely snug all the way down the track. Now OVER steer will put the edge of the wheel in contact with the rail where it grinds as opposed to rolls on the bottom and the friction increases fast. The back wheels are canted where any touch at all grinds the wheel against the rail and that friction is way higher than rolling friction.

So to summarize- heavy car is faster we know- but the friction laws do not explain it and gravity does not explain it- Inertia of the car down the straight is not quite the effect- except more momentum does help against air friction. Is it all air friction? If we ran these cars in a vacuum would they come out equal?

 
txchemist said:

I was looking at the data you posted and saw a very little improvement with COG. Below is my Scout demonstration car. I've used it to demonstrate the differences in speed vs the COG location. It is a 3 wheeled car at the 5oz mark, but does have bent rear axles for an alignment demonstration (for another discussion). I ran a test with the COG in various locations and recorded the times. The car was not "race preped", nor was the alignment perfect, but the rear did stay off the rail and it did not wiggle.

COG_Car_zps4e2251f9.jpg


So here are the times vs. the COG. What I'm showing you is the COG seems to matter more than what is depeicted in your chart. From the weight "front to back", there only a few hundreds of a second difference. In my testing, I show a difference of .122 seconds with the COG only changing 2", which is significantly different. I can not even get the weight all the way up front.

COG_Test1_zps725a938f.jpg
 
txchemist said:
Inertia of the car down the straight is not quite the effect- except more momentum does help against air friction. Is it all air friction? If we ran these cars in a vacuum would they come out equal?
This is an interesting test. I used to do a test where I use a 3x5 card to show the effect of aero-dynamics (using the same orange car above). I can not test the spped of a derby car in a vacuum, but it would be interesting test to do this for a light weight car, if anyone if interested...
 
OK, MY data is different than Shamwow and Scott Acton data

MY DATA
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7tmCvrSuVxwq98O9jwrfuiciRm2LYrjgAfusPCdGTrypi86P2rP3ErM3M7UDq78eRd33yFydNWA1XRyHT5kvfbz4DDbjB1Tr3rfrQOHP8HVdfWn07aG3zuN2Ha8Stm6Yz87ref+PePX1mPf/+DNeArm+pxu6XDoEidh6/+r/FjHtJLV18MwFlzqoyCju1vUzd+Cxx6oxUMiD8UHsoj3dmh//Cb168A4+tWrga7ggPA2yb8XmDoGA7lrafsDsA37tSei600sKC+93MAHHVDMa/1fZccvRYAML4e/DsYB2D9/mD84//9WBV4RWjbU0gm6J6vrd7zi3f4A9w9+8dgbNUnH0Ps9q8A8OnLBkjP1kIVkaiZLyf9t8VON0td2+406mh06wmr9vjqTSaM3Iw/ibQccoHlDp9bun3ZAasAAKvWvGwleBH4+tV4Jho9dDIAp/xFLLpDm+vn4fGYbdx28YqDjrxxayBgYWvihhHhptTquv/ea/1nrlPNupeNJn70cXDd+aHL+HWn7A2Ou05rmkmkyXR9FECVGN3ZrSfustfn/loXDSMJC4jbhtKL1x+34S1XbLrpwsMvepSp+6485LMXNG79FPjClM0tKTGmD39lNfjs47M+7pB88/cOXfvtrQRFKEpk895fH73HaVf0elDGbY3//AFw7CXT5iCMEmKbll9mO847CRwA9tp/AwDrwXG3hf2E6NrsaUes+uldsEBuI4azoT/x4RWHXzmhs6lfvR2ceIUKtbndJFQ37//A2JFnTk8tbT/vVeCI32elOyc6VKsbzh370Oc3NxSEXogU9qKpv577cnDMrU65oDTe+M3XjZ9+H4yexKyPbzoTvPacK78DwBm3FJHjZwEXd3wd/MsJj015vFn/45HgmzcSlKKijOOomQfstgBqZCsd9bdTW2696f3g0KseTGpNrh195Xd22QeAFeCo8y1f2trLCubxbttZTvpv26Guaw7tcFYRTZybTgYrPvZQUXiiT7MJObSbaS/uO7deAMY/NoMdy+qSxy4+Ys+L70T8cX/rN8Z2PfO6qgNTK4LPw5tYrWxh9sdH7AFecfP9jWmpSBjfef6xx/7EHdx4HACfnYGWAwc3nwHA6vO3GFzK+z4L1v/ggalZqDtz9mLStCynlVZpu3n4p19w0/XUnCpl5LX8xSi18to1n9/w6bNOP/nYa3SI4OOngvec+o0Xffl+goPKJUQmD30KgDMnelbk0PbWC951wEEXMGY+4c+XZXjWf4Iv3NZbYpKKLvvLJavf+P2awesj3Zl+HCMt//KR3U/dFKOU1/M49FI00nNL9WzTBe8EB372tg7uzxp688UngndeuiVKNbZveDMAx90z2dV6xxObrjkBHH7GQ0Tr2t2nrgRH/uiJqUjrR7+32ws2vPKl3/OVrMFHla8zaT987ydeBd7zIPEnnYWNJ65fs+aqW1lN1dJHPw/Alze7kXn1oeCFJ2wrYLLNbdx5xrqx026lRpbQ6Lsf2PtzX7ugT6JKNZ/AWquFCdxnJBM8knOmS523bXjBUSd+fGSFDksm7rl6VwDA+IHfuXEHbVaq3URhwO2nPR6LaUf4Xd+jCHMVR7aTeNaIC5SxsOPMu8KJayNPdFu1uQ6NmDPoS9XyF5kohy3qQ1aTmrhCQBm6FTIpJiwxNea5hxcMX83L0oN17c37rSm/1Ea7O09xKQRyu9IcJkbcKogR5oM51ZfaqltOHnHpumYQEM0t6ftNGOau0c9JAd35KhbE1oU164Uz4ZznUI1J/Z+VdOpJ5JVEabPuL2Lv1kvP3WXV2lsfnsVchba+4Yoj3varv6ZXngfWnLqjvTHA8S8+dwDY4+I7HaW3XPTx8fETH15sz3hF1P+nvCU9u5JWrS4JhrMXvhpsOBCse9+bAdj/8C9PkVbLTzae8lUwBlavAQdd9Otvr3k7nKP3J1s+AFafcc1dNtFLLLnlj5fus+s+YNVue69a9aH3G9O1jUtVnzl6h7mpU+lFZ+H8z4BdwW6/d/RCv4K/+xJYv/fXb655lkxRYtvmHV96036nXW9OO3p+YD/xxzWrXgPA6tMunfZmb778/eCMO7XbXIStR4O/fm/Dob/e3LaTaLjgRg04bMTG5acDADaAfQ8AYA348G+npyvfeMxPwqPAruvXglcDsPebPr7U0luN6V5zuHVp9lCw94r1AGwYe+H+P/JiLd2NqNrSfuLaF70Y7AV22R0c98TMxce+9+rt23TUoY7ZjVw9iswfffnDALx8lxeBk7507Wngw5fFs/SBX6wH7/8Lboetutv6w+7g47duaSc6/OMR+4GvXaSlHtS3PIGjn334IABeBtbt9TIANhz2E+k5nqMkjpIkK7c26jk7YsULwYaxvVbsvQ68eWsw2bj3wjdt2Ps1X7rqUaq1vRB6jz1d0tgJItaLRR5wQSHiOI/louvaAvPE6/u+4m5RRcpP9GKYu0ZkTYUwW2Ke4UWxNrLEdzTVW+IiomUYLQrFg1oQIdexCkxk2PKbgdaxL52ixSf7UAd2f9ZlIY8Lv+NHVuR0mRtA288w5kbOOMRIDBNt4KgQIbIQyzVrFqE/1zNHFqYUa8NnQbrVpJrAUYn8f1bSC1HXbCmfzFfJMJibGsH2FZf8Aqzcdy0AbwD7gjdeWWAMo+Htx4+DvccAeNEpF37gFPD1q0KzEz35TfDid9+0dajSVuNpj8Gey0mLDn5qWi8Q25Km1e90+7HXmeNq6PW49YR0k0lhlEQLZ3sPl2aUadfxMm22F2Mquj2B5hzo5wzSGm1RbmReVVLt+ciwYZfpeavMsN7CIKO4M0OZqswwLaN5x44Zdkc23M6LMOwEJvHSQTg1VVS6bQQez4fcNsli1DKiUsNW0+e6ixfuwXDQROWwrF18LDj4
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

lets just say 0.05 sec faster with weight in the back
Acton data had only 0.02 sec difference. I think that was messed up but then really messed up when Shamwow made it into 4.6 car lengths.
Now you show 0.112 or twice what the effect of increasing the height would be so other things are also changing unless you had a much different start angle.

And for sure you put the sail on a light car and it is way more drastic than a heavy car.
 
lol allright, other than the stuff I asked, I don't have the brain power to figure it out pass what has already been said LOL....

For me, the end results justifies the means LOL....weight to max limit allowed + weight in rear + use a properly drilled body and properly aligned axel holes + use as light a wheel as the rules allow + rail run + polish axels and wheels using polish prep system (which still varies between some racers !!!) + run oil = a fast car

That's a good enough equation for me for now! HAHA...
 
Looking back on the data presented by Shamwow- Other videos show the track being used is a home built wood circular arc probably 32 ft. to the timer. If so- those cars are slow. it takes a 2.23 to get 1st on the wood track our school uses very close to the design Acton built, but I do not know his length. I have seen way lower times from many on the forum for the wood den tracks. A big positive when you start to experiment with the cars is, if you mess around enough, eventually you will have a pretty fast car. A big negative is if you are not getting good alignment and rail running on purpose instead of by accident, you can get some very strange data that does not pan out when you put it to the equations of gravity. I show my data with a car with good alignment superimposed over the Acton data.
Big problems with what he got with lighter cars.
TXandshamwow.jpg

The lighter cars he ran had to have gotten out of alignment and hit the rail many more times, or got bad grinding from a front wheel not positively canted.
We both get about the same spread for moving the COG around, but for sure he is unknowingly getting hammered with big rail losses on the light car. This messes up his thinking he got a 4.6 car improvement when adding weight in the correct place. Anyone getting that big of a change has got alignment issues that almost make the data meaningless. Here is how I would first take his data, correct it to be consistent with what he measured, and then show the impact of alignment in general before you start the experiments.

datax3.jpg

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TXCHEMIST, I have a couple of UNL cars that I weighted so the COG was 3/8ths in front of rear axles (very aggressive). The body and wheels were GoatBoys, with the front end very narrow with extra long axle and guide pin. At first I set the steer at 2 inches over 8 feet. The times were not good. Then as I started increasing the steer, the cars kept getting better times. The sweet spot (steer) for one car ended up to be 9 inches, and for the other it was was 14 inches. One reason for the difference was weight placement, but that is another story.
MY QUESTION: Assuming we weighted the car so the weight was equal on the two rear wheels, and with COG at 3/8" is there a way to graph the steer?
I.e., one axis would be time, and the other axis the steer, say start at 1 inch, and work up to maybe about 15 inches.
I know that for various reasons, one car may always be a bit different from another, but a graph could give us a decent starting point.
 
OPA,
sure, might even look cool to graph both cars- just send me streer & time and I'll post it up
 
OPARENNEN said:
The sweet spot (steer) for one car ended up to be 9 inches, and for the other it was was 14 inches. One reason for the difference was weight placement, but that is another story.
MY QUESTION: Assuming we weighted the car so the weight was equal on the two rear wheels, and with COG at 3/8" is there a way to graph the steer?
I.e., one axis would be time, and the other axis the steer, say start at 1 inch, and work up to maybe about 15 inches.
Interesting, but I would be intersted in one more variable, COG. I have two similar cars, one with 3/4" COG and one with 7/8". The car with 3/4" COG needs 3" of steer to overcome the wiggle. The car with 7/8" COG neeeds 1" to 1 1/2" of steer, so little its almost hard to measure. Of course, the rear alignment can play a part (since I use a jig) and is a variable in my builds, but I have always heard that the closer the COG is to the rear axle, the more steer needed. Anything I have made has always held true to this principle. It also seems very reasonable to need 9" of steer with 3/8" COM and basically fits the jump in steer I need when moving the COG back 1/8" inch (from 7/8 to 3/4).

Assuming an aluminum track that is well taken care of, I would be curious as to what the best COG / steer combination is? I know what the starting point is (4" over 4' for what I'm assuming is 3/4" COG), but what is optimum COG/steer combination? Can you graph that or is there too many variables?