Determining Diamond’s Properties Under Extreme Pressures

September 29, 2026 by Maya Posch 2 Comments

Although graphene gives diamond a solid run for its money when it comes to being the most useful assembly of carbon atoms, both have the distinct property of material scientists still trying to figure out all their properties and potential applications. This includes something like the melting curve of diamond and potential phases beyond this diamond lattice phase that occur when exposed to extreme pressures and temperatures. Such as those experienced on a planetary scale and during inertial confinement fusion (ICF).

In this research (paywalled) by researchers at the Lawrence Livermore National Laboratory (LLNL), it was investigated how close theoretical simulations were to physical reality by blasting diamond samples with a laser. This ablated the surface and sent a shockwave through the material that caused it to melt. Using X-ray diffraction data this entire process was followed, elucidating the exact melting temperature under such conditions.

This revealed that previous estimates based on earlier experiments had been off by many hundreds of degrees, giving a far better idea of how diamond responds to such extreme pressures and temperatures. Where such information is very relevant is in fields like planetary science where diamonds can occur naturally and being able to predict their presence can be essential.

The other application, and the primary reason why LLNL does this kind of research is for the sake of ICF at the national ignition facility (NIF), which is the best way to investigate the behavior of e.g. hydrogen isotopes under extreme conditions like those of nuclear weapons.

Unfortunately this research will have no impact on practical power generation using nuclear fusion, as the only viable path there involves forms of magnetic confinement fusion (MCF), but it’s still pretty rad to improve our understanding this carbon form.

Designing A Fully 3D-Printed Mechanical Calculator

September 29, 2026 by Maya Posch 10 Comments

Even if almost tragically impractical in a world where digital calculators are cheap as chips, mechanical calculators and their big mechanical computer brethren remain an absolute marvel of engineering. Using nothing but elements like simple gears their motion is used to calculate everything from a simple multiplication to the proper targeting instructions for an Iowa-class battleship’s guns.

This fascination, along with the mind-bendingly high prices for commercial digital calculators led [3D all Workshop] to spend 2 months on designing his own mechanical calculator. Fully FDM 3D-printed, of course.

In the video the design process and troubleshooting step are covered along with the workings of the mechanisms for both addition and multiplication. While this may seem simple, basically converting numbers of rotations into a final indicator position, aspects like carrying a digit and adding a multiplication feature to the mechanism require some proper engineering.

Of course, using FDM printing for tolerance-sensitive things like gears meant that a lot of time was spent redesigning aspects of the mechanism, going through about a hundred design iterations until it worked, with the help from a bit of lubrication.

Naturally this isn’t the first 3D-printed mechanical calculator, not to mention ones made from wood, but always it’s pretty cool to see one made from first fundamentals.

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Testing Coulomb’s Law And Similar Fundamentals Yourself Remains Tricky

September 29, 2026 by Maya Posch 12 Comments

How easy is it to build a simple demonstration at home that proves fundamental law of physics, such as Coulomb’s inverse square law, or Maxwell’s equations? This is one of those questions that always nagged at [Casual Physics Enjoyer] as they sought to find ways to develop a more intuitive understanding of these fundamentals with simple DIY-at-home experiments. Unfortunately this turned out to be rather tricky for Coulomb’s law.

Us H. sapiens – courtesy of a few bright individuals – have figured out many of the fundamentals that underlie this physical world over the past few thousands of years. Even if we still got some pretty massive fundamentals left today for equally bright-minded folk to bash their collective heads against, basics like Coulomb’s law or Maxwell’s equations ought to be a snap now for the average person to replicate since they were after all performed in an era before quantum mechanics, computers or even electrification.

To the dismay of the average physics student, experiments that demonstrate these fundamental laws of physics are still pretty hard to replicate without some solid time and monetary investment. This is demonstrated quite succinctly in the linked article, where the question of how to demonstrate the force between two charged particles, as in Coulomb’s law, and that it is an inverse square relationship is attempted.

Whereas the Wikipedia entry claims it to be a ‘simple experiment’ with just two identical spheres, in a DIY setting this is somewhat tricky, including the part where you have to charge up the spheres and measure the force. Using some aluminium foil and wires you can get the two ends to separate as you’d expect, but quantifying the force is a whole other matter.

Pitting A CFD-Optimized Toroidal Propeller Against A Conventional One

September 28, 2026 by Maya Posch 23 Comments

Although we often think that we got certain aspects of aerodynamics pretty much licked at this point, details like the optimal shape of a propeller remains hotly debated, both in- and outside of academia. This also includes wilder designs like toroidal propellers that even after more than a hundred years are still mostly just being evaluated. Recently [Neuronautics] took a shot at figuring out whether toroidal propellers even make sense.

In order to do this, first an efficiency baseline was established using a conventional and highly optimized propeller. After scanning it in to get its exact geometry and running it through a computational fluid dynamics (CFD) simulation, the software spat out a number of about 73%.

This left figuring out an optimized shape for the toroidal propeller to pit against it. While you can absolutely brute-force the seventeen shape parameters being considered here and test them in CFD, this would take insanely long. The hack here is to use multiple reference frame (MRF) to drastically speed up the selection process, though even then it still took two months. An example of using MRF with regular propellers is discussed in a 2019 paper by [Randi Franzke] et al. in Energies.

Although MRF saves a lot of simulation time, you still end up with a lot of data that has to be analyzed for interesting patterns. For this [Neuronautics] trained a artificial neural network to automate filtering the many options for the most optimal ones, until converging onto a single design.

This G1401 design was the lucky winner, though with only a simulated 62.9% efficiency. Subsequently the one aspect that had been left unchanged was also iterated through, in the form of many different airfoil shapes until the final design appeared.

This design was then 3D printed in resin, which showed the first hurdle with the selection process, in that the printed versions were too thin and flexible to be usable as propellers. Cue many hours of manual tweaking of the design to make it actually printable.

Although the final design didn’t exceed 62% efficiency in a final test, the comment section to the video rightfully points out that the comparison was between a commercially made propeller and a DIY resin-printed one, which adds a whole other batch of variables. That said, it’s unlikely that there’d have been an obvious improvement either way, otherwise we’d already have seen toroidal propellers pop up everywhere on drones and aircraft.

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Jet Megatextures Demo For ESP32-S3

September 28, 2026 by Maya Posch 5 Comments

Mipmapping is a good way to add a lot more detail to a 3D scene without overburdening the rendering hardware with detail that won’t be seen by the user. This level-of-detail rendering technique was demonstrated on the N64 console hardware a few years ago by [James Lambert] with [Michael Biggins], also known as [PhonicUK], now demonstrating it on the ESP32-S3 using his own Jet rendering engine.

Although level-of-detail rendering really speeds things up, it does also require far larger texture sizes, with [James]’s N64 demo taking up 40 MB of a 64 MB cartridge. To fit it on an ESP32-S3 with 16 MB of PSRAM and no SD card expansion or such the textures were further compressed to use 8-bit indexing, resulting in a mere 5.01 MB of textures.

There’s a demonstration video over on the associated Reddit thread, which shows the camera moving through the scene. Even if not as exciting as the Wipeout port by [Michael] that we previously covered, it does make clear that even without a proper 3D GPU the ESP32-S3 is already a pretty capable gaming machine that can go toe-to-toe with some 1990s consoles.

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Using The SNES Super FX Chip To Run Super Mario 64

September 28, 2026 by Maya Posch 9 Comments

Although the Nintendo 64 was the first to bring real 3D graphics to the table in 1996, the Super Nintendo had an ace up its sleeve in the form of the Super FX chip. One major advantage of using cartridge-based games is that you have the option to add wild features such as a 3D graphics chip to your SNES, something that got used to make games like Star Fox, and as [Tobi] demonstrates in a recent video, can also totally run Super Mario 64 if you squint a lot.

While there’s a rumor that Nintendo was looking to release a ‘Super Mario FX’ game for the SNES, there’s no evidence for such a project. Fortunately these days we got hobbyists prepared to give it a shake to see what a determined group of SNES game developers could have accomplished back then.

The pleasant surprise here is that although a new engine was needed, the SM64 assets could be used with this ‘SMFX’ game and it runs fairly well. There is still room for performance improvement, and the 2 MB memory limit is a problem that may require some culling of parts of levels.

Frames are painted back to front since there’s no advanced Z-culling or similar features, but it shows just how capable the Super FX chip is. [Tobi] has said that he’ll look at releasing the project in some form once he’s happy with how it works and runs, which is definitely something that we’ll look forward to.

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Going On A Tangent With The Intel 8087’s Hybrid CORDIC Algorithm

September 27, 2026 by Maya Posch 4 Comments

Continuing their reverse-engineering of Intel’s 8087 FPU, [Ken Shirriff] and friends took a look at one of the trigonometric functions, specifically FPTAN. The most exciting part with such reverse-engineering is probably figuring out which algorithm was used in the implementation, while trying to determine the reasoning behind the final hardware design.

If you’re running a simple MCU or MPU like the 6502 or Z80 without hardware functions you’d likely use an algorithm such as CORDIC or similar, as this requires only basic hardware features like addition, subtraction, bitshift, and look-up tables. One can also use polynomial approximation if there’s hardware support for a potential speed-up, or as is the case in the 8087, create a hybrid approach that targets speed and accuracy.

In the article the exact implementation to get to 64 bits of accuracy is detailed, starting with the 16 bits calculated using CORDIC before switching to the Padé approximant technique involving the ratio of two polynomials. Since after calculating the brunt of the final value with CORDIC the remainder is a fairly small value, this polynomial approximation is not just very accurate but also fast.

This approach allows the FPTAN and similar trigonometric functions in this FPU to hit a very high level of accuracy and not require the look-up table sizes and additional time required to work through the remaining bits with CORDIC. For those who want to see the full algorithm Intel’s engineers used, [Ken] has the full microcode listing with comments in the article as well.

As for the exact speed-up from this approach, [Ken] calculates for one value that FPTAN would spend 33% on CORDIC pseudo-division, 47% on CORDIC pseudo-multiplication and a mere 15% on the polynomial approximation along with about 5% overhead.

With the Pentium series of CPUs Intel moved completely away from CORDIC, as it’s clear that as accurate as it may be, it’s hard to scale to a significant number of bits without incurring significant time penalties. With the introduction of SIMD instructions the x87 ISA has further seen its functionality reduced, but this analysis shows once again why the 8087 made such an impact when it was released.