#81

Ok,

I understand what you understand - nothing special meant here. Let me make things less abstract.

Basically what Monte Carlo does is to generate "outcomes", e.g. where a bullet ends, for, varying conditions - the input parameters. The input parameters are varied according to a certain probability distribution. If for example a shooter is able to aim with an accuracy of 1 MoA then people like me model that as a bell-shaped (Gaussian) probability distribution that averages to 1 MoA mean inaccuracy. With this we acknowledge the fact that in some cases the shooter aims very well and in others badly.

In a Monte Carlo simulation then, the computer draws a random number and converts that to an inital horizontal and vertical aiming angle. This is done in such a way that average inaccuracy is 1 MoA. After the horizontal and vertical launch angles are chosen, the bullets trajectory is calculated.

If, for instance, in addition the effects of a non-constant wind is being studied, one might model that primitively as a wind distribution that is half of the time zero and the other half 1 m/s. In a Monte Carlo calculation then one first draws a random number that determines the launch angle, and an another random number is used to determine if there is wind or not in the trajectory calculation (with the afforementioned spreadsheet I deal on a more sophisticated way with non constant wind along a trajectory). Hence we end up with a set of bullet end coordinates that were affected by both wind and the shooters abilities.

In practise, the more inputs one generates according to a probability distribution, the less the details of a probability distribution matters. The distribution might as well be flat, meaning that the probability for a certain input value is constant in a certain range and zero elsewhere.

I understand what you understand - nothing special meant here. Let me make things less abstract.

Basically what Monte Carlo does is to generate "outcomes", e.g. where a bullet ends, for, varying conditions - the input parameters. The input parameters are varied according to a certain probability distribution. If for example a shooter is able to aim with an accuracy of 1 MoA then people like me model that as a bell-shaped (Gaussian) probability distribution that averages to 1 MoA mean inaccuracy. With this we acknowledge the fact that in some cases the shooter aims very well and in others badly.

In a Monte Carlo simulation then, the computer draws a random number and converts that to an inital horizontal and vertical aiming angle. This is done in such a way that average inaccuracy is 1 MoA. After the horizontal and vertical launch angles are chosen, the bullets trajectory is calculated.

If, for instance, in addition the effects of a non-constant wind is being studied, one might model that primitively as a wind distribution that is half of the time zero and the other half 1 m/s. In a Monte Carlo calculation then one first draws a random number that determines the launch angle, and an another random number is used to determine if there is wind or not in the trajectory calculation (with the afforementioned spreadsheet I deal on a more sophisticated way with non constant wind along a trajectory). Hence we end up with a set of bullet end coordinates that were affected by both wind and the shooters abilities.

In practise, the more inputs one generates according to a probability distribution, the less the details of a probability distribution matters. The distribution might as well be flat, meaning that the probability for a certain input value is constant in a certain range and zero elsewhere.

#82

Quote from: admin on June 12, 2014, 01:38:37 PM

Maybe "Monte Carlo" is a definition issue. In this case, i imagined that input parameters are generated in such a way that they are consistent with a certain probability distribution. Then a trajectory calculated and evaluated. The process is repeated hundreds of times. This is what one might describe as Monte Carlo.

Nice reply, much clear now. Will take a look to your workbook and for sure coming back with more questions, if you don't mind

#83

Maybe "Monte Carlo" is a definition issue. In this case, i imagined that input parameters are generated in such a way that they are consistent with a certain probability distribution. Then a trajectory calculated and evaluated. The process is repeated hundreds of times. This is what one might describe as Monte Carlo.

#84

Quote from: admin on June 11, 2014, 01:16:00 AM

WEZ is a Monte Carlo simulation.

I find it hard to opinate about it - it does what it says.

Quite frequently I use a similar tool understand ballistic effects in a target shoot competition setting.

It is available as download on the BfX site. Look for the descriptive text:

"This workbook hosts a simulator with which you can estimate your results in a (multi distance, multi target) match, e.g. the NRA 90 shot full bore regional match course."

Robert,

Thanks for the answer, however how do you know WEZ is based off of Montecarlo? I've read all the available literature by Litz and that word is not present even once.

thanks!

#85

WEZ is a Monte Carlo simulation.

I find it hard to opinate about it - it does what it says.

Quite frequently I use a similar tool understand ballistic effects in a target shoot competition setting.

It is available as download on the BfX site. Look for the descriptive text:

"This workbook hosts a simulator with which you can estimate your results in a (multi distance, multi target) match, e.g. the NRA 90 shot full bore regional match course."

I find it hard to opinate about it - it does what it says.

Quite frequently I use a similar tool understand ballistic effects in a target shoot competition setting.

It is available as download on the BfX site. Look for the descriptive text:

"This workbook hosts a simulator with which you can estimate your results in a (multi distance, multi target) match, e.g. the NRA 90 shot full bore regional match course."

#86

Hi there,

I'd assume most have read about the WEZ analysis Litz is now including as a feature in its PC software.

Would like to hear about opinions on its value as "hit probability" tool, if it's such, and mostly about the math behind it, if it makes sense. So far, I was only aware of Montecarlo simulations but apparently this feaure is not based off of it.

Any comments are greatly appreciated.

I'd assume most have read about the WEZ analysis Litz is now including as a feature in its PC software.

Would like to hear about opinions on its value as "hit probability" tool, if it's such, and mostly about the math behind it, if it makes sense. So far, I was only aware of Montecarlo simulations but apparently this feaure is not based off of it.

Any comments are greatly appreciated.

#87

The latest version has quite a lot of bug fixes and tweaks and improvements.

https://drive.google.com/folderview?id=0B2_BBw2VMf2-VUJZNXVTM0M1akE&usp=sharing

https://drive.google.com/folderview?id=0B2_BBw2VMf2-VUJZNXVTM0M1akE&usp=sharing

#88

#89

cool!

what I need are parametrizations of 6dof coefficients for boat tail bullets. No other projectiles.

what I need are parametrizations of 6dof coefficients for boat tail bullets. No other projectiles.

#90

Just a little teaser about what I'm currently working on.

I've been studying CFD about a year now and due to my work I have access to CFD programs. At the moment results are promising. Errors in Cd are less than 5 %.

I've been studying CFD about a year now and due to my work I have access to CFD programs. At the moment results are promising. Errors in Cd are less than 5 %.