Sorry I didn’t reply to your last post yet - I started writing something earlier, but it’s tricky to describe without going into a bit more detail about the way the Kangaroo solver works.
First off - there’s definitely no need to add any special goals or calculations for centripetal or centrifugal forces. One great thing about simulating physics is that many more complex phenomena emerge naturally without more coding needed once the basics are set up right. In the case of centripetal force, it’s just the result of the momentum and tension of the chain.
The reason for the difference in behaviour between the simulated and real world behaviour will have more to do with the damping.
A solver like the one in Kangaroo needs a method for stepping through time, and ideally the algorithm for this should have several properties:
- Stability - it should not explode when high stiffness materials are used.
- Numerically accurate deformations - it should be possible to control precisely how strongly each of the goals are enforced relative to each other in the final result, particularly if we want to use it for structural analysis.
- Fast convergence to the equilibrium solution. For form-finding applications, we are mainly interested in reaching the final static form, and should be able to get there without needing millions of tiny time steps, or waiting for it to bounce around for a long time.
- Energy preservation control. If we want to simulate accurate dynamic behaviour, it is good to be able to control how much energy gets converted from kinetic to heat with each step.
In the first versions of Kangaroo, items 1-3 were often problematic - if you wanted to use high stiffness materials, it often required impractically small time steps to avoid making the whole simulation explode, meaning it would take forever to converge.
The new mathematical method I used for Kangaroo2 largely solved these issues, but at some cost to item 4. The new solver can use much bigger steps, even with extremely high stiffness materials, without exploding, which is great for example if you want to see how much a loaded steel truss deforms with real numbers for Young’s modulus and weight of the material, or enforce strict planarity in a panelisation, or quickly find a minimal surface for a given boundary.
However, it does tend to remove a small amount of energy from one frame to the next. Note that this is separate from the actual damping input setting - that gives you the option to add more damping, but there is always some level which is inherent to the time stepping method used, and it is currently not possible to reduce this to zero.
For form-finding (which is the primary purpose of Kangaroo), damping is actually good - we want to quickly get rid of all the kinetic energy so we can find a stable minimum of the total potential energy. What we definitely never want is simulations that artificially gain energy out of nowhere.
Of course in the real world total energy is conserved, but when simulating things happening on earth, in air, there is always some amount of damping, both from the internal friction of elastic materials and from air resistance, and for some dynamic applications where the inherent solver damping is close to or lower than this we can get a good approximation of the behaviour, adding even more damping if necessary.
For something like this relatively stiff, elastic, dense, and thin chain of your sculptures though, the real natural energy losses are probably quite a bit lower than the inherent damping of the currently available solver options.
One other example of something which is not really possible to do so well in Kangaroo2 is orbital simulation of planetary systems. The old solver was really good at this because it used a symplectic integration method. This was a fairly tangential and rarely used application though, so I’d considered it an acceptable sacrifice.
I have been working on ways of improving the solver methods to try and get the best of both - ideally Kangaroo would include options allowing you to precisely control damping, with the possibility to take it all the way down to exactly zero (with separate numerical controls for internal elastic friction and air resistance), while still always remaining stable, still with the option of making materials arbitrarily stiff, or even absolute constraints, and still giving numerically accurate deformations, all while keeping the goal interface simple (so custom goals can still be created just with target positions and weights, without having to mess about with Hessians and higher derivatives). This is all easier said than done though! Even cutting edge simulation research always seems to fall down on at least one of these. It may end up that multiple solver options are needed with different trade-offs, but I’d like to avoid making things too complicated.
So… perhaps more information than really needed! and I realise it doesn’t give a solution to the immediate issue, but hopefully at least gives a bit of insight.