Navier AI retweeted
generic 8 m patrol hull, 22 kn into 1.83 m head seas, free to heave and pitch
in calm water it lands within 6% of savitsky on trim and 3% on resistance. in the waves it heaves 2.09 m peak to peak and pitches from −3 to +13 deg
single mesh, and slamming isn't resolved yet. towing tank comparison is next
done with @NavierAI with our GPU solver
Navier AI retweeted
stoke's 2nd stage is one of the most interesting designs for reusability
ran some reentry and landing sims with @NavierAI. this is a powered landing flown by a closed-loop gnc controller coupled to the cfd, simulated with ventus, our gpu cfd solver
*representative geometry from public info, not affiliated with stoke*
Navier AI retweeted
agree with most of this. models will get good at design fast. but most of the time on a real program goes to building the thing, measuring it, and figuring out why it didn't work as expected
more sims won't remove that, but they can cut how many times you have to loop. @NavierAI
AI is on track to become a superhuman hardware designer, but design is only the beginning. The interface between bits and atoms is lossy, and turning designs into real hardware gets harder at every level:
1. Sourcing off-the-shelf parts that work together (a 2006 Falcon 1 engine test was delayed by one capacitor, in stock 6,000 miles away)
2. Custom parts, where a master machinist translates CAD into machine instructions
3. Assemblies, where every part can meet its drawing and still not fit
4. Outside vendors (even SpaceX has 591 employees with supply chain titles)
5. Doing all of it at a scale of 1,000,000
"Superhuman AI might be great at pure design, meaning at defining the platonically ideal part for a particular job, but struggle with 'design for manufacturing,' or finding a point design that meets the requirements while remaining easy to build/procure and inexpensive."
Full piece from a16z's Christian Keil on what it takes to turn AI-designed hardware into the real thing: a16z.news/p/superhuman-hardw…
Navier AI retweeted
the bots are grinding on this one.
immersed boundary layer solved on gpu with @NavierAI agents
Navier AI retweeted
Supersonic retropropulsion temperature field. Different angle on the same case run with @NavierAI's solver.
Heat comes from the hot nozzle exit gas + shock compression.
What fields or post-processing would you add here?
