I am a theoretical physicist doing quantum field theory for a living - I know how to compute any classical equation of motion I want and I could probably code the whole dynamics in the simulation myself. That's not what I am after.
I am software developer for nearly everything around CFD simulations (and everything else that can be programmed, even if it takes a hammer), with a background in software engineering and spaceflight technology.
I leave quantum field theory for people with more time for details, I am really happy if 1+1 results in 10 and not a probability function that might collapse at 2 on a good day.
And I look really good in black clothes. :lol:
Pretty much any computation has an approximation in some limits. And often you learn a lot about the essential physics you're governed by just by looking at approximate scalings. It'd be nice to explain this to people. It's how we teach physics to students - understand the essentials, find the relevant approximations, understand scalings.
Yes, but then there is something I use to call "engineering reality". Yes, you can be too precise. And you can be too abstract. Your model has to fit for the task you do.
Thus, as many orbiteers will confirm, in the moment you think there are just simple rules without a bunch of exceptions, you will burn, crash, bounce or get good scores in lithobraking.
There sure is. You could just run hundred trajectories through the simulation and do a plot of the maximum g-load, and you'll find that the result is a smooth and monotonous function, which means it can be parametrized by some expression, and having that expression one could estimate what to expect in a few seconds. Just nobody has done it - that doesn't mean it can't be done
For the same spacecraft, with the same ballistic parameters and the same steering algorithm.
And even then, a warning that you might have already heard before: Only because you can get something plotted from limited amounts of samples with a smooth undisrupted curve, this does not mean that there is a simple function behind it.
Sure, again my point being that at the end of the day, the relation is reasonably simple, because because so close to the Earth gravity can be assumed constant, so the shuttle at 650 km has a bit more than twice the potential energy than a shuttle at 300 km while the orbital speed is roughly the same, so they have the same kinetic energy in a circular orbit. So to make up for the difference in potential energy, I need to reduce kinetic energy, and knowing that that goes like v^2 and potential energy like h, I can solve the problem.
Wrong. Since the 300 km and 650 km are relative to the surface of Earth and the energy potential is a function of Semimajor axis and the actual gravity field around you. Tiny, important details.
Which means if you fly the shuttle and memorize a single number, i.e. how much Delta v it needs from 300 km, you can know immediately how much you need for any other altitude the shuttle can reach. You sure can work out the same thing by doing the exact numbers in the MFD of your choice - but you miss the essential simplicity of your situation then.
No, exactly that won't work out. While you have relative simple almost linear plots for piloting the shuttle for short distances, any long term targeting is impossible as there is no simple rule and simple number.
I can also compute the total Delta v budget of the shuttle if I want/need to - but the number could also be in a collection of useful pocked formulae.
No, it can't - what if you have more or less payload? Changes the Delta V. What if you deploy payload in Orbit and get lighter along the mission. Or heavier by returning a payload from orbit?
That's just what I mean - lots of problems don't require you to solve complicated differential equations (because there's reasonable simplifications you can make) or advanced math - you need to be told a single number, and then there's a scaling relation which works good enough.
That usually only works for really small intervals, that are usually too small for spaceflight applications. For example, if your time to a space station is significant lower than your orbit period, you can assume to be moving in a gravity-free space for steering, take a straight course and will arrive there. But the longer you need, the more gravity will make your course differ from the simpler straight line model and change your course.
But maybe that's just my approach to things...
As the non-existence of a collection of such scalings seems to indicate.
I see it like that: What gets the job done, gets the job done. If Galilean gravity works for an ascent guidance algorithm it is fine, if I need Newtonian gravity, its also fine.
I just can't use Galileo, when I should be using Newton.