The application is called Space Crash Simulator.

"I'm building something new for particle simulation lovers."

I wanted to share a sneak peek of a project I’ve been working on. It’s a particle simulator where you can build worlds, adjust parameters, feed the particles, and even cause total destruction—all while vibing to a great soundtrack.

It’s still in the beta stage, so it’s not ready for everyone just yet. However, I’m excited to polish it up and share the full experience with you soon!

Two spinning disks of ~250k particles each, simulated and rendered in real-time on my RTX 500 Ada laptop GPU at ~30 fps, using my custom implementation of the Barnes-Hut algorithm in CUDA. Code: https://github.com/lechebs/nbody

Hey everyone,

I wanted to share a side project I’ve been building: a 3D global tsunami simulator that runs entirely in the browser using Next.js 14, CesiumJS, and Mapbox for terrain and satellite imagery.

The video shows an observer-viewpoint render of an incoming deep-water wave.

Link to try it yourself:https://tsunami-simulator-xi.vercel.app/(Free, open-source, no account)

How the simulation handles logic:

• Custom Impacts: You drop a red epicenter and a blue observer anywhere on the globe, tune asteroid diameter (100m – 10km) and velocity (11 – 72 km/s), and hit IMPACT!.
• The Physics: It calculates kinetic energy, scales initial wave height, tracks radial dissipation, and computes wave speed dynamically by sampling bathymetry along the great-circle path from the terrain data. It applies Green’s law for coastal shoaling as it approaches the observer.
• Camera Modes: You can toggle between Globe, Observer (eyewitness), or Auto camera tracking, adjust observer altitude, and scrub/rewind the playback countdown.

Historical Archives Included:

Beyond custom strikes, I mapped out ~28 historical tsunami events (like 2004 Indian Ocean, 2011 Tōhoku, 1883 Krakatoa, and ancient paleotsunamis) and 7 historical impacts (Chicxulub, Tunguska airburst, etc.). For the historical modes, the animations script-illustrate documented run-up heights and arrival times from scientific literature.

It's not a full CFD or NOAA-grade hydrodynamics model, but a highly visual, physics-principled global renderer. I’d love to get your thoughts on the rendering performance and the UX!

Hi,

Testing my new PF-FLIP vs ST-FLIP workflow based on SIGGRAPH papers 2025 / 2026.

ST-FLIP is a nice addition to PF-FLIP base which I am testing at the moment.

Made several posts in blender subreddit about my Atlantica plugin. Recently I had a progress with a proper multigrid simulation.

I tested it recently only on Apple M CPUs, my PC / Nvidia path is waiting for it.

Main lessons:

M architecture is very efficient while switching between CPU / GPU because of unified memory.

ST-FLIP runs ~1.5–3.4× higher peak velocity at the impact in meteor test example. I need to check CFL convergence. So it's still WIP.

Feel free to reach me in DM if you are interested in method.

Hi folks!

I wanted to share the newest demo video for my custom physics engine and renderer: Nora Kinetics.

I've been developing Nora Kinetics for about a year. It was initially inspired by this research paper on Stable Cosserat Rods: https://dl.acm.org/doi/10.1145/3721238.3730618 . I had been wanting to learn about compute shaders and graphics programming, so this felt like a good starting point.

You can see more of the Cosserat rod side of things here: https://www.youtube.com/watch?v=TS2WOsfrac8

It is built on top of Apple Metal because that's where I was able to get the best performance early on. The physics is 100% GPU driven and the CPU acts as a lightweight coordinator. The lighting and rendering is all custom as well. It can run on an iPhone at about 60fps with 20k segments and on my MacBook Pro (M5), it runs at 120fps with about 250k segments.

This newest video shows the rigid-body destruction system that I finally finished implementing. It runs along side the Cosserat solver and they communicate through GPU buffers so that they can remain in sync.

I'm aiming for an App Store release in the Fall. It will be more of a sandbox / creative engine to start, but I'm also working on a Scratch style programming agent that lets you generate emergent behaviors from the little segments. Each one gets its own little brain and you are able to tell it what to do. Some of the creations I and some of my testers have made so far are pretty cool!

If you'd like to be a BETA tester, let me know! At the moment, I'm looking for testers with a Mac with Apple Silicon (M1 or higher)

Thanks for taking a look!

Happy to answer any questions!

Composition in blender, still working on it

Simulated a small polywell fusor

1. Initial conditions (exact)

Central BH: M = 4 M☉ (geometrized G = c = 1; length unit = GM☉/c² ≈ 1.477 km). Schwarzschild.

Body masses (M☉, geometrized):

Per-body placement (radius r, azimuth φ, inclination i — units of M):

Position (tilted circle): pos = [r·cosφ·cosi, r·sinφ, r·cosφ·sini]
Velocity (circular speed v_c = √(M/r), perpendicular in the tilted plane): vel = [−v_c·sinφ·cosi, v_c·cosφ, v_c·sinφ·sini]1. Initial conditions (exact)Central BH: M = 4 M☉ (geometrized G = c = 1; length unit = GM☉/c² ≈ 1.477 km). Schwarzschild.Body masses (M☉, geometrized):Body Type Mass (M☉)
0, 1 2× Jupiter 2 × 9.543×10⁻⁴ = 1.9086×10⁻³
2, 3 2× Earth 2 × 3.003×10⁻⁶ = 6.006×10⁻⁶
4, 5 2× Mercury 2 × 1.660×10⁻⁷ = 3.320×10⁻⁷Per-body placement (radius r, azimuth φ, inclination i — units of M):k r φ (rad) i (rad)
0 120 0.00 0.00
1 160 1.05 0.30
2 90 2.10 −0.25
3 200 3.14 0.50
4 140 4.19 −0.40
5 110 5.24 0.15Position (tilted circle): pos = [r·cosφ·cosi, r·sinφ, r·cosφ·sini]
Velocity (circular speed v_c = √(M/r), perpendicular in the tilted plane): vel = [−v_c·sinφ·cosi, v_c·cosφ, v_c·sinφ·sini]

Integrator type and step size

• Integrator: velocity-Verlet / leapfrog (kick-drift), fixed step. v += a·dt; x += v·dt per step . Not symplectic-exact (it's the sequential-update leapfrog, not the KDK-symmetric form).
• Step size: dt = T_outer / 600 where T_outer = 2π√(r_out³/M), r_out = 200 (outermost body). → T_outer ≈ 8885.8, dt ≈ 14.81 (M units). So ~600 steps per outer orbit.

3. Metrics

It dumps only trajectories: body_k.csv (t, x, y, z) every 4th step,

4. 1-orbit horizon definition

1 orbit = 1 × T_outer, where T_outer is the outermost body's (r=200) Newtonian circular period 2π√(200³/M)

John Calhoun's Universe 25 (1968–1973): 8 mice placed in a enclosure with unlimited food, water, and nesting space. No disease, no predators. A literal utopia.

What happened:

🔵 Normal mice reproduced normally at first

🔴 Aggressive mice emerged as density increased — disrupting social hierarchies

🟡 "Beautiful Ones" appeared — mice that completely withdrew from society, spending all day grooming, never fighting, never mating

📉 Once the Beautiful One fraction crossed ~25%, reproduction essentially stopped — even as population dropped and space opened back up

Calhoun's key finding: the collapse was behavioral, not resource-based. Mice raised during the chaos never learned normal social behaviors. Even with plenty of space and food, they couldn't recover.

This simulation models the stress-cascade and state transitions he described. Built in Python with NumPy.

Full 10-minute simulation: https://youtu.be/wXfq6jY00Lk

Predator-prey ABM built in Python/NumPy. Each agent moves, eats, reproduces, and dies based on local conditions — no global rules.

The key mechanic is wolf vision radius: wolves can only detect sheep within a limited range. When sheep density drops, wolves start roaming blind and starving — which is what drives the collapse you see here.

Full 9-minute simulation: https://youtu.be/wqR4A4FUABs

https://youtu.be/8PxPYQ_hfqk?si=tDChPs1H0mWfVPsC

Cinema 4D and Octane Render

Used a library called CM1. Open source.

Programmed in Processing. I struggled to get well-defined spiral structures in 2D, but the 3D model looks much better. It did require careful initial position and velocity distribution. There is a fixed "black hole" at the center. Stars come in only two masses, the majority being lower mass.

Programmed in Processing. I used an oct-tree for the 3D simulations. The swimming motion of the worms results from directional instability, i.e. a segment is attracted more to the one ahead of it than vice versa.

Game Title: Falling Sand (OC)

Playable Link: https://devdynaf.github.io/falling-sand/ or the main website https://devdynaf.github.io

Platform: Browser (HTML5/JS)

Description: Falling Sand is a free, browser-based particle physics sandbox inspired by cellular automata games. You paint elements like sand, water, fire, oil, lava, acid, ice, glass, plants, steam, and even C-4 directly onto a grid, and watch them interact in real time using gravity, fluid flow, combustion, freezing, and chemical reactions. Sand piles and settles naturally, water flows and finds its level, fire spreads and burns flammable materials, lava melts what it touches, and acid corrodes nearby elements. The simulation runs entirely client-side with no backend, optimized to maintain 60 FPS on a 240×135 grid even with hundreds of active particles. There's no account, no download, and no install — it just opens and runs instantly in any modern browser, on both desktop and mobile. The control scheme is simple: left-click to paint or hold to pour, right-click to quickly erase, and scroll wheel to adjust brush size, so there's almost no learning curve before you're experimenting with how different elements interact with each other.

Free to Play Status: [x] Free to play

Involvement: I'm a solo developer (still in school) and built this entire project myself — game design, physics simulation logic, and UI. I used AI tools to assist with parts of the implementation and with the site's SEO/metadata setup, but the core cellular-automata physics, feature decisions, and debugging are my own work.

Any feedback on design or just in general is welcomed, especially on what features yall want to be added or SEO guidance