What did you use for closed loop guidance? If you aren't aware of it NASA has a lovely paper called UPFG "Unified Powered Flight Guidance" [1] used for the space shuttle. This was implemented and made more universal for KSP (of course) by Przemysław "Noiredd" Dolata as PEGAS "Powered Explicit Guidance Ascent System" [2]
If it's your thing you could try implementing it and getting a single continuous burn to final orbit.
For this model I kinda just made up my own system to play around with it, while it wasn't very effective it was pretty fun. That being said thanks a lot for the recommendations, I wasn't very aware of the options I had for it.
I think it's a good start, but the user experience and the color theme make the simulation cumbersome to absorb. Maybe you can do a theming exercise with an AI. The zoom feature is highly sensitive. It's not intuitive at first on what the user should do or take away from the simulation. The events feature is great, and essential.
Looking forward to seeing the next iteration. Nice work.
Regarding other comments about AI use, I tend to agree but can maybe add some context.
I finished my ME degree this fall and found dynamics to be the most enlightening course for me by far. The ability to build a reasonable approximation of real-world systems from a set of reference frames and ODEs is incredible, and I find that it has affected the way I see the world in a meaningful way.
That said, a well-defined set of reference frames in particular are the "unsexy" backbone of modeling & sim work, and I acknowledge that the barrier for entry is somewhat high (cumbersome notation, some linear algebra/ODE prerequisites). It seems like you may already have run into a reference frame issue based on some of the other comments. I still think that this is worth learning unassisted by AI! If you're a textbook guy, Kasdin and Paley's Introduction to Engineering Dynamics is how I learned. If not, I'm sure many other online sources are useful.
And once again, great work here. I have no doubt that this will be useful for your college search, especially with a well-understood dynamics foundation. Best of luck!
This is really neat and pretty fun to play around with!
How accurate are the simulations? I'm able to get orbit by turning 45 degrees as soon as I launch and then doing some minor burns at the height of the initial trajectory.
I don't feel like this strategy would work in real life.
Subtracting gravity losses, the largest effect of your 45 degree thrust vector would be sideways acceleration. Which is generally what you want, to get up to speed as quickly as possible, instead of wasting energy ascending vertically.
We just don't do it on Earth because we need to get out of the atmosphere first for efficiency and structural reasons. But on the moon or another vacuum body, "diagonal kick followed by minor circularization burns at apogee" is pretty close to the optimal strategy. Even on Earth, it's similar to the trajectories proposed by SpinLaunch and other "space cannon" concepts.
Its fairly accurate as far as I can tell. Your right about the fact that It wouldnt work in real life due to the dynamic pressure your exerting on your rocket, using your method you get to about 150 kPa, Falcon 9 usually maxes out around 35 kPa so it would explode. Also it is a fairly fuel inneficent way of getting into orbit, but yes it is possible
I've been toying with the idea of building something similiar but with a bunch of different space stuff, like a calculator for different rotating space station geometries, mars/lunar cycler orbits, or solar shade sizes/distances.
It's been many years since I've done this kind of stuff in school and it's great to be able to refresh yourself on this stuff. The kind of UI you're using makes it really friendly and approachable, like a game.
Thats due to the rotation of the earth, for the purposes of the simulation the earth is rotating at 408 m/s (earths rotation at cape canaveral) so thats why It appears its going sideways
When the rocket launches it already moves with earth's rotation at that latitude (and is stationary with respect to the ground).
That's in fact the reason why many rocket launch sites are near the equator: Free velocity.
But the rotational velocity of the rocket is going to be very similar to Earth's at the launch site. Eventually the rotational velocity between ground and the rocket will begin to noticeably differ, but the launch trajectory starting at 408 m/s would look terrifying if it were that way in real life.
I found the problem, It appears that I had the earth staying stationary but gave its rotational velocity to the rocket, What i think needs to happen is I need to also be rotating the earth so the inertial frame stays the same. Thanks for bringing this up
Around 90% AI for syntax, I did alot of debugging manually. For implementing new features I would design them and do the reasearch then have a AI write lines for me and verify the work
It's a quick path to having something to play with but I'd encourage folks specifically wanting to learn something new or create something for a portfolio to try to have it be more the other way around: write 90% by hand, implement the features yourself, and have AI help you with the research and debugging (but not try to automatically write the code to fix the bugs). AI is really great at all of the things listed, but I guarantee if you were in an interview and someone asked you about this project your responses about how and why everything works would be 2x-3x stronger.
If this was your 3rd time implementing an orbit simulator and you just wanted to get the bulk done quick so you could move on to work on new stuff then that's when having AI write the bulk & doing checking really works best as you're already an expert in what needs to be done rather than trying to learn about what needs to be done from someone/something else doing it for you.
I say this as an orbital dynamics simulator in C++ having been one of my first big projects I put in a portfolio myself :). And I also like to use AI often, I'm not an AI hater in general, just I like to use in certain specific ways sometimes.
Feedback on the UI side: It'd be cool if the stars used more random positions rather than sine/cosine looking patterns. There are lots of different approaches one could try (from simple random positions to very complex types of clustered randomness and brightness variation to realistic star maps). My suggestion would be to ask AI what types of approaches could be taken and try implementing whichever approach sounds the most fun!
I completely agree with what your saying, with one caveat. If I understand how the underlying physics and dynamics are working but don't know how to write the code then theres no world where I could make something like this. The way that I see it is that AI is a tool that really depends on the person.
If I were to be someone who just tells AI "implement drag" and lets it do it then sure, im not learning, but if I do my research outside and just use AI to translate what I give it into the language of computers I feel like im not only building something cool but im understanding whats happening because AI is just translating.
So basically while yes I do believe that AI can be harmful if you approach it inproperly, it helps novice programers implement cool things by just using english.
>If I understand how the underlying physics and dynamics are working but don't know how to write the code then theres no world where I could make something like this.
You could learn coding same way as you learned the physics and dynamics. Programming and physics aren't mutually exclusive. Actually every physicist is (was?) required to know (multiple) programming (languages).
>If I were to be someone who just tells AI "implement drag"
That'll mean at least you understand drag. Could do even worse, regards to learning at least rather result, prompting something like "make a cool-looking physically-realistic 2d rocket launch simulator", which we're at point that will most certainly return a functional app.
>but im understanding whats happening
Do you though? You depend on AI correctly translating your natural language input to code. Though arguably this is something LLMs excel at, since math (logic) also plays role you've to be able to at least read and review the resulting code for correctness. (Assuming you actually care about the physical accuracy that is.)
Curious what age bracket you’re in? I understand where you’re coming from, but I don’t think it will be the paradigm for the future. My own experience is that you actually learn a lot by having AI write piecemeal code and then do overall review.
Author 17, me 31. Having AI write your physics code by telling it what to do is a relatively poor way to learn either physics or code, but I agree there are ways to use AI to learn things - just not necessarily this particular way here.
The main thing is the author did normal research and then gave it to AI to make it happen and verified it seemed to match what they asked for. In that, AI helps build the demo but it's not really adding anything to the understanding of the physics than reading about it in the first place did (akin to reading a post of someone else doing something, reviewing their code, and putting it in your project rather than trying to work with the new ideas yourself). Similarly, having AI write 90% of the code is not really helping you get familiar with the code any more than telling it which physics formulas to implement helps you get more familiar with the formulas.
OTOH, asking AI to help validate your research and understanding before going in to write the code helps you learn things like "the rocket shouldn't be flying right at takeoff just because the planet is spinning". Having AI help debug code issues you can't figure out in the code you wrote yourself (but not having it fix them for you directly) helps you really test 100% of the understanding you're trying to demonstrate instead of testing if you can say another's implementation seems reasonable.
If the only goal is to get to generate output instead of learn, then the calculus of course changes. E.g. yesterday I wanted to run an optical flow method for heart rate posted here in my browser. I wasn't really seeking to learn about it, I just didn't have python set up on my machine with the good webcam and wanted to play with the author's demo live. So I copied the article into AI and told it to make it into a single page HTML app. I didn't learn much of anything more than the article already covered in doing so, however I did achieve my goal of having some output to mess with instead of learning.
Perhaps worth adding I just completed my first college degree last year (figured it'd be fun to get one), so perhaps a bit more recent experience with the learning eras they may be about to enter than a typical 30-40 year old range.
Fortunately for it, that city is mounted on some antigrav sled and moves around to always stay ahead of the launch vehicle, safe from my totally not intentional attempts at crashing into it.
Completely unrelated bug: pitch control can go down only to -5 degrees.
yup, derived most of the equations myself then double checked them before implementing them. That being said all of the positining is done through time steps and not so much equations
What did you use for closed loop guidance? If you aren't aware of it NASA has a lovely paper called UPFG "Unified Powered Flight Guidance" [1] used for the space shuttle. This was implemented and made more universal for KSP (of course) by Przemysław "Noiredd" Dolata as PEGAS "Powered Explicit Guidance Ascent System" [2]
If it's your thing you could try implementing it and getting a single continuous burn to final orbit.
Awesome work BTW!
[1] https://ntrs.nasa.gov/citations/19740004402
[2] https://github.com/Noiredd/PEGAS
Edit: I now see it's on your roadmap, so I guess it is your thing.
For this model I kinda just made up my own system to play around with it, while it wasn't very effective it was pretty fun. That being said thanks a lot for the recommendations, I wasn't very aware of the options I had for it.
I've got another kos-PEG implementation here as well, along with a really extensive bibliography:
https://github.com/lamont-granquist/KSP-KOS-PEG/blob/main/li...
This is awesome, great work! There’s a huge audience for something like this based on the popularity of Kerbal Space Program.
Biggest low-hanging fruit UI improvement would be mobile responsiveness. It was a bit challenging testing on my phone.
Awesome, thanks alot, thats a great idea to work on
I think it's a good start, but the user experience and the color theme make the simulation cumbersome to absorb. Maybe you can do a theming exercise with an AI. The zoom feature is highly sensitive. It's not intuitive at first on what the user should do or take away from the simulation. The events feature is great, and essential.
Looking forward to seeing the next iteration. Nice work.
Impresive work OP!
Regarding other comments about AI use, I tend to agree but can maybe add some context.
I finished my ME degree this fall and found dynamics to be the most enlightening course for me by far. The ability to build a reasonable approximation of real-world systems from a set of reference frames and ODEs is incredible, and I find that it has affected the way I see the world in a meaningful way.
That said, a well-defined set of reference frames in particular are the "unsexy" backbone of modeling & sim work, and I acknowledge that the barrier for entry is somewhat high (cumbersome notation, some linear algebra/ODE prerequisites). It seems like you may already have run into a reference frame issue based on some of the other comments. I still think that this is worth learning unassisted by AI! If you're a textbook guy, Kasdin and Paley's Introduction to Engineering Dynamics is how I learned. If not, I'm sure many other online sources are useful.
And once again, great work here. I have no doubt that this will be useful for your college search, especially with a well-understood dynamics foundation. Best of luck!
This is really neat and pretty fun to play around with!
How accurate are the simulations? I'm able to get orbit by turning 45 degrees as soon as I launch and then doing some minor burns at the height of the initial trajectory.
I don't feel like this strategy would work in real life.
Subtracting gravity losses, the largest effect of your 45 degree thrust vector would be sideways acceleration. Which is generally what you want, to get up to speed as quickly as possible, instead of wasting energy ascending vertically.
We just don't do it on Earth because we need to get out of the atmosphere first for efficiency and structural reasons. But on the moon or another vacuum body, "diagonal kick followed by minor circularization burns at apogee" is pretty close to the optimal strategy. Even on Earth, it's similar to the trajectories proposed by SpinLaunch and other "space cannon" concepts.
Its fairly accurate as far as I can tell. Your right about the fact that It wouldnt work in real life due to the dynamic pressure your exerting on your rocket, using your method you get to about 150 kPa, Falcon 9 usually maxes out around 35 kPa so it would explode. Also it is a fairly fuel inneficent way of getting into orbit, but yes it is possible
I think it would, given that there is no air resistance.
This is really neat.
I've been toying with the idea of building something similiar but with a bunch of different space stuff, like a calculator for different rotating space station geometries, mars/lunar cycler orbits, or solar shade sizes/distances.
It's been many years since I've done this kind of stuff in school and it's great to be able to refresh yourself on this stuff. The kind of UI you're using makes it really friendly and approachable, like a game.
Why is it launching with a horizontal velocity? It initially moves to the right even when pointing up perfectly.
Thats due to the rotation of the earth, for the purposes of the simulation the earth is rotating at 408 m/s (earths rotation at cape canaveral) so thats why It appears its going sideways
When the rocket launches it already moves with earth's rotation at that latitude (and is stationary with respect to the ground). That's in fact the reason why many rocket launch sites are near the equator: Free velocity.
But the rotational velocity of the rocket is going to be very similar to Earth's at the launch site. Eventually the rotational velocity between ground and the rocket will begin to noticeably differ, but the launch trajectory starting at 408 m/s would look terrifying if it were that way in real life.
I found the problem, It appears that I had the earth staying stationary but gave its rotational velocity to the rocket, What i think needs to happen is I need to also be rotating the earth so the inertial frame stays the same. Thanks for bringing this up
Good point, I implemented that very early and hadn't really thought about it, let me look into the true solution, thanks
I was able to fiddle with the settings so it launches in the air a bit and then hits the city. All by just going vertically. Pretty funny.
Don't rockets also start with the same horizontal velocity though, since nothing canceled it out when it got off the launch pad?
It would be like jumping, and finding yourself ~250-400 meters away from where you lept by the time you landed.
That said, neat project, and way fun learning experience. Good job.
How much AI was used vs manual lines?
Around 90% AI for syntax, I did alot of debugging manually. For implementing new features I would design them and do the reasearch then have a AI write lines for me and verify the work
It's a quick path to having something to play with but I'd encourage folks specifically wanting to learn something new or create something for a portfolio to try to have it be more the other way around: write 90% by hand, implement the features yourself, and have AI help you with the research and debugging (but not try to automatically write the code to fix the bugs). AI is really great at all of the things listed, but I guarantee if you were in an interview and someone asked you about this project your responses about how and why everything works would be 2x-3x stronger.
If this was your 3rd time implementing an orbit simulator and you just wanted to get the bulk done quick so you could move on to work on new stuff then that's when having AI write the bulk & doing checking really works best as you're already an expert in what needs to be done rather than trying to learn about what needs to be done from someone/something else doing it for you.
I say this as an orbital dynamics simulator in C++ having been one of my first big projects I put in a portfolio myself :). And I also like to use AI often, I'm not an AI hater in general, just I like to use in certain specific ways sometimes.
Feedback on the UI side: It'd be cool if the stars used more random positions rather than sine/cosine looking patterns. There are lots of different approaches one could try (from simple random positions to very complex types of clustered randomness and brightness variation to realistic star maps). My suggestion would be to ask AI what types of approaches could be taken and try implementing whichever approach sounds the most fun!
I completely agree with what your saying, with one caveat. If I understand how the underlying physics and dynamics are working but don't know how to write the code then theres no world where I could make something like this. The way that I see it is that AI is a tool that really depends on the person.
If I were to be someone who just tells AI "implement drag" and lets it do it then sure, im not learning, but if I do my research outside and just use AI to translate what I give it into the language of computers I feel like im not only building something cool but im understanding whats happening because AI is just translating.
So basically while yes I do believe that AI can be harmful if you approach it inproperly, it helps novice programers implement cool things by just using english.
>If I understand how the underlying physics and dynamics are working but don't know how to write the code then theres no world where I could make something like this.
You could learn coding same way as you learned the physics and dynamics. Programming and physics aren't mutually exclusive. Actually every physicist is (was?) required to know (multiple) programming (languages).
>If I were to be someone who just tells AI "implement drag"
That'll mean at least you understand drag. Could do even worse, regards to learning at least rather result, prompting something like "make a cool-looking physically-realistic 2d rocket launch simulator", which we're at point that will most certainly return a functional app.
>but im understanding whats happening
Do you though? You depend on AI correctly translating your natural language input to code. Though arguably this is something LLMs excel at, since math (logic) also plays role you've to be able to at least read and review the resulting code for correctness. (Assuming you actually care about the physical accuracy that is.)
Curious what age bracket you’re in? I understand where you’re coming from, but I don’t think it will be the paradigm for the future. My own experience is that you actually learn a lot by having AI write piecemeal code and then do overall review.
Author 17, me 31. Having AI write your physics code by telling it what to do is a relatively poor way to learn either physics or code, but I agree there are ways to use AI to learn things - just not necessarily this particular way here.
The main thing is the author did normal research and then gave it to AI to make it happen and verified it seemed to match what they asked for. In that, AI helps build the demo but it's not really adding anything to the understanding of the physics than reading about it in the first place did (akin to reading a post of someone else doing something, reviewing their code, and putting it in your project rather than trying to work with the new ideas yourself). Similarly, having AI write 90% of the code is not really helping you get familiar with the code any more than telling it which physics formulas to implement helps you get more familiar with the formulas.
OTOH, asking AI to help validate your research and understanding before going in to write the code helps you learn things like "the rocket shouldn't be flying right at takeoff just because the planet is spinning". Having AI help debug code issues you can't figure out in the code you wrote yourself (but not having it fix them for you directly) helps you really test 100% of the understanding you're trying to demonstrate instead of testing if you can say another's implementation seems reasonable.
If the only goal is to get to generate output instead of learn, then the calculus of course changes. E.g. yesterday I wanted to run an optical flow method for heart rate posted here in my browser. I wasn't really seeking to learn about it, I just didn't have python set up on my machine with the good webcam and wanted to play with the author's demo live. So I copied the article into AI and told it to make it into a single page HTML app. I didn't learn much of anything more than the article already covered in doing so, however I did achieve my goal of having some output to mess with instead of learning.
Perhaps worth adding I just completed my first college degree last year (figured it'd be fun to get one), so perhaps a bit more recent experience with the learning eras they may be about to enter than a typical 30-40 year old range.
He is 17
This breaks my heart, software modeling orbital mechanics is something I do for fun from time to time.
Looks super awesome. Strong work! It showed the karman line at 98-99km. Maybe a tiny tweak needed.
I'm sure that city just *loves* being downrange of the launch site :)
JK, nicely done! lots of fun to watch.
Fortunately for it, that city is mounted on some antigrav sled and moves around to always stay ahead of the launch vehicle, safe from my totally not intentional attempts at crashing into it.
Completely unrelated bug: pitch control can go down only to -5 degrees.
Took the anti grav sleds off, might be possible to crash into now idk
Did you do all the math yourself?
yup, derived most of the equations myself then double checked them before implementing them. That being said all of the positining is done through time steps and not so much equations
Nice work! Had a "blast" playing it ;)
Much better than KSP2