Pl

Planetary CFD on a Phone

Hacker News

Planetary CFD on a Phone

After a trek in Nepal this year, I had a month to relax, and an idea from my trek to work on, but no computer in reach. I had been experimenting with phone IDEs though and decided to see what could be accomplished. The end result of a month at a few hours a day using AIDE is DreamPearl (source on Github), a planetary formation simulator that does all of the plate mechanics using computational fluid dynamics (CFD). Currently working on volumetric air/water cycle. Quick (6s) video to demonstrate. https://youtu.be/5Nv9Klqlc2M Github link https://github.com/Araesmojo/DreamPearl Things I like: Plate mechanics as CFD "dirt" particles - plates are defined as surface regions based on target size and then dirt flows across them like a conveyor. Models pressure, drag, and frictional acc. in non-z-locking sphere built from a split D20. 3D as 2D - CFD calculations are performed on a 3D surface with no artficial wrapping or edges, but particles are tricked into believing they are 2D by performing all operations in a tile localized frame of reference. Shaders by parts - while the move to vertex/frag shaders opened a lot of flexibilty, they also add much complexity. My approach decouples shaders into subsegments that can be superimposed to create a full shader using lines like shaderProgram = BuildShaderFromParts( "based3d", "tex", "light" ) where each call out references only the relevant portions in a file such as "light.gg" or "tex.gg". They can be mixed and matched so that swapping "point" for "base3d" in the above example would create a floating 2d sprite instead. Barnes-Hutt tree decomposed space and lookup in a non z-locking spherical space w multiple options for level of detail (LoD) Useful for: using opengl on a phone, implementing a CFD code, strategy/adventure games that need a random world, examples of procedural creation that diverge from the standard perlin / voronoi noise.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, computer, models · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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