# PeterStock

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1. ## Aeroplane

An aeroplane created with my new game, Lucifer's Atoms. You build anything you want from simple primitives (sticks, balls, and flat sheets). By combining them together you can make interesting things, and the physics makes them work ^_^ http://www.lucifersatoms.com/
2. ## Gravity systems?

I see octrees already mentioned, but in case you don't know about it, there's a name for this technique used for this specific purpose: Barnes-Hut. You can trade accuracy for efficiency. Some links that explain more: https://en.wikipedia.org/wiki/Barnes-Hut http://www.cs.princeton.edu/courses/archive/fall04/cos126/assignments/barnes-hut.html
3. ## Combining Material Friction and Restitution Values

Physics simulations commonly use the Coulomb friction model, which requires a coefficent of friction between the materials of two interacting objects to calculate the friction force. Similarly, a coefficient of restitution is required to calculate the collision response force. But how do you determine these coefficients for each pair of materials? A common approach is to define values of friction and restitution for each individual material and then combine them to get the material-material values. Given individual material friction values $$x$$ and $$y$$, we need to define a combination function $$f(x,y)$$. Similarly, $$r(x,y)$$ for restitution (using $$x$$ and $$y$$ to mean the material restitution values this time). Function requirements The value of $$f(x,y)$$ and $$r(x,y)$$ should lie between $$x$$ and $$y$$. So $$f(x,x) = x$$ and $$r(x,x) = x$$. For any constant $$c$$, $$f(x,c)$$ and $$r(x,c)$$ should be monotonically increasing (it shouldn't ever decrease as $$x$$ increases). It should also avoid long flat sections, so as to be able to discriminate bewteen materials. For instance, putting boxes of ice and rubber on an plane and increasing the slope, the ice should slip first, whether the plane is made of ice or rubber. $$f(x,y)$$ should favour slippy over grippy, i.e. $$f(0,1) \lt 0.5$$. This corresponds to the intuitive notion that ice should have a greater effect on the combined restitution value than rubber, e.g. vehicle tyres on ice. I decided that I wanted $$r(x,y)$$ to favour the extremes, in a similar way that $$f(x,y)$$ should favour values towards $$0$$. So very bouncy or very energy-absorbing materials predominate over average ones. In a game world, you then have the option of making the world less or more bouncy for all objects within it by changing the world material, while maintaining a reasonably wide range of restitution values when the world is set to an averagely bouncy material. Friction Candidates for $$f$$ that are often used are the minimum, the arithmetic average, and the geometric average. Minimum $$f_{min}(x,y) = min(x,y)$$ This has too many flat sections and doesn't adequately discrimiate between materials, e.g. $$f(0.1,0.1) = f(0.1,1)$$. Arithmetic average $$f_{aa}(x,y) = \frac{x + y}{2}$$ This doesn't favour slippy over grippy enough. Geometric average $$f_{ga}(x,y) = \sqrt{{x}{y}}$$ This is good, but the values aren't equally spaced. For this reason, I have found an alternative function - a weighted sum of $$x$$ and $$y$$. Weighted sum $$f_{ws}(x,y) = \frac{{x}{w_{x}} + {y}{w_{y}}}{w_{x} + w_{y}}$$ where $$w_{x} = \sqrt{2}(1 - x) + 1$$ $$f_{ws}$$ has a more regular spacing between values than $$f_{ga}$$. The trade-off is that it doesn't cover the full range of values for $$f(x,1)$$ ($$f(0,1) \approx 0.3$$). However, they are approximately equal for $$0.2 \le x \le 1$$. Restitution As with friction, the minimum, the arithmetic average, and the geometric average are often used. Minimum $$r_{min}$$ has the same objections as $$f_{min}$$. Geometric average $$r_{ga}$$ is not suitable because it would require the world material to have a restiution near $$1$$ to provide a wide range of combined values. Arithmetic average $$r_{aa}$$ is better, but it doesn't give a very wide range of values for $$r(x,0.5)$$. We can improve on this range by allowing some flatness and defining $$r$$ as a piecewise min/max/sum function. Piecewise min/max/sum $$r_{pmms} = \begin{cases}min(x,y) & \text{if }x \lt 0.5 \land y \lt 0.5\\max(x,y) & \text{if }x \gt 0.5 \land y \gt 0.5\\x + y - 0.5 & \text{otherwise}\end{cases}$$ This has the same shortcomings as $$r_{min}$$ at the corners where the min and max functions are used. But you can't avoid that if you want to have the full range of values for $$r(x,0.5)$$ and still satisfy $$r(x,x) = x$$. Similar to the friction, I have created a weighted sum function that I think is better. Weighted sum $$r_{ws}(x,y) = \frac{{x}{w_{x}} + {y}{w_{y}}}{w_{x} + w_{y}}$$ where $$w_{x} = \sqrt{2}\left\vert{2x - 1}\right\vert + 1$$ As with $$f_{ws}$$, $$r_{ws}$$ sacrifices some of the range for $$r(x,0.5)$$ to provide better continuity in the corners. Why $$\sqrt{2}$$? It's the choice that maximizes the function range, while maintaining monotonicity. These graphs show what $$f_{ws}$$ looks like with $$w_{x} = c(1 - x) + 1$$, for $$c = 0.5$$, $$c = 1$$, $$c = \sqrt{2}$$, $$c = 2$$, and $$c = 4$$. For $$c \lt \sqrt{2}$$, $$f_{ws}(x,1)$$ has less range. For $$c \gt \sqrt{2}$$, $$f_{ws}$$ is no longer monotonic - near $$f_{ws}(0.1,0.9)$$ it starts to curve back, making a more grippy material have a lower combined friction! You can see this more clearly for higher values of $$c$$. $$c = 0.5$$ $$c = 1$$ $$c = \sqrt{2}$$ $$c = 2$$ $$c = 4$$ Conclusion The method to combine material friction and restitution values may seem like a minor detail, but sometimes the devil's in the details. Since the whole concept of defining values for friction and restitution for a single material and then combining them isn't physically accurate, this is obviously just a case of finding a suitable function rather than attempting to model reality. You may have different requirements for your functions, but I hope this discussion of the alternatives is useful. Links I made the graphs at the WolframAlpha web site. I find it's a pretty useful tool. Here's an example plot. Article Update Log 12 Nov 2015: Initial release
4. ## Is true time travel possible (in games)?

Games you might be interested in: [url=http://web.archive.org/web/20170314053418/www.braid-game.com/]Braid[/url] [url=http://web.archive.org/web/20111210081214/www.potato-factory.com/temporal/]Temporal[/url] [url=http://www.scarybuggames.com/chronotron.swf]Chronotron[/url]
5. ## Data alignment on ARM processors

Watch out for compiler optimisations breaking code like this (called type punning). It breaks the language aliasing rules. See this link: http://stackoverflow.com/questions/20922609/why-does-optimisation-kill-this-function/20956250#20956250
6. ## Beyond the Infinite

Have you seen Proun? http://www.proun-game.com/ Might be useful for ideas/inspiration, if you weren't aware of it. Good luck with it.
7. ## Old PC problem

I'd check out the capacitors like Matias suggests. My old motherboard broke with no beeps, but with odd behaviour, like instant reboots, blue screens and the like. Opening it up I saw all the caps next to the CPU were worse than in Matias's first picures - they looked like little volcanoes that had erupted!

9. ## Clinical studies on overlooking stupid bugs

I think most compilers have a warning setting to catch things like if (x); { y; } Maybe turn up your warning level? Or use some static code checking tool, like PVS-Studio or Cppcheck. The compiler find most of my mistakes like this. The worrying time is when it compiles successfully and you know you're missing something, but can't remember what :-)
10. ## time to angle

Ah, I see. I thought that seemed too simple :-)
11. ## Contact tangents

I'd say do option 1. The tangent vector is for applying contact friction, so surely it has to be calculated from the relative velocities of the contact points? I had a read through the paper and it seems to me that the tangent vector is described in the middle of page 4, as this. That's quite a nice, readable paper :-)
12. ## time to angle

Can't you just use sin or cos?
13. ## Replay & recorded games

l0calh05t: Quite right - I had assumed single-threaded code. That's the biggest performance trade-off, in my opinion. You could do multi-threaded computation, but like you say, you'd have to sort the results when combining. frob: Now I realise I had made an assumption which I may not have made clear enough - I was referring to 'floating point' as just the bits covered by the IEEE 754 standard. I accept that everything outside it (like sin, inv sqrt etc) may not always give the same result across all processors.
14. ## Replay & recorded games

No, all the cases you mention are caught by: Yes, this prevents the compiler using fancy instructions like fused-multiply-add and reciprocal sqrt. But I don't consider the performance penalty significant, given the benefits it gives for networking model it enables. As evidence of my claim of IEEE 754 specifying the exact binary result of calculations, I cite 'What Every Computer Scientist Should Know About Floating-Point Arithmetic' by David Goldberg: http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html Which says: "The IEEE standard requires that the result of addition, subtraction, multiplication and division be exactly rounded. That is, the result must be computed exactly and then rounded to the nearest floating-point number (using round to even)." "One reason for completely specifying the results of arithmetic operations is to improve the portability of software. When a program is moved between two machines and both support IEEE arithmetic, then if any intermediate result differs, it must be because of software bugs, not from differences in arithmetic." "There is not complete agreement on what operations a floating-point standard should cover. In addition to the basic operations +, -, × and /, the IEEE standard also specifies that square root, remainder, and conversion between integer and floating-point be correctly rounded. It also requires that conversion between internal formats and decimal be correctly rounded (except for very large numbers)."
15. ## Culling? Duplicate Impulses during Collision Resolution.

It ends up with 2x the velocity it should have. If there were 3 simultaneous collisions, it would have 3x the velocity. Solution: scale each collision response by a factor of 1/n when you have n simultaneous collisions. This requires that you find all the collisions first, then do the resolutions as a separate step afterwards, so you know what n is for each object before you start adding any forces.