Explore Venus With A Strandbeest Rover

There’s a little problem with sending drones to Venus: it’s too hostile for electronics; the temperature averages 867 °F and the pressure at sea level is 90 atmospheres. The world duration record is 2 hours and 7 minutes, courtesy of Russia’s Venera 13 probe. To tackle the problem, JPL has created a concept for AREE, a mechanical robot designed to survive in that environment.

AREE consists of a Strandbeest configuration of multiple legs with a monster fan propelling it, and one can imagine it creeping over the Venusian landscape. While its propulsion system might be handled by the Strandbeest mechanism, it will still have to navigate and transmit data. We’re not sure how a mechanical radio wave might work–maybe like those propeller arrow-cutters that [Dain of the Iron Hills] busts out in movie version of the Hobbit? Chemical rockets that somehow don’t spontaneously ignite? Or maybe it can just “transfer all energy to life support” and AC the heck out of the radio.

We’re space nerds here at Hackaday–check out our piece about NASA employees’ talks at the 2016 Hackaday Superconference and our extracurricular tour of JPL.

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Books You Should Read: IGNITION!

Isaac Asimov described the business of rocket fuel research as “playing footsie with liquids from Hell.” If that piques your interest even a little, even if you do nothing else today, read the first few pages of IGNITION! which is available online for free. I bet you won’t want to stop reading.

IGNITION! An Informal History of Liquid Rocket Propellants is about how modern liquid rocket fuel came to be. Written by John D. Clark and published in 1972, the title might at first glance make the book sound terribly dry — it’s not. Liquid rocket fuel made modern rocketry possible. But most of us have no involvement with it at all besides an awareness that it exists, and that makes it easy to take for granted.

Most of us lack any understanding of the fact that its development was the result of a whole lot of hard scientific work, and that work required brilliance (and bravery) and had many frustrating dead ends. It was also an amazingly dangerous business to be in. Isaac Asimov put it this way in the introduction:

“[A]nyone working with rocket fuels is outstandingly mad. I don’t mean garden-variety crazy or a merely raving lunatic. I mean a record-shattering exponent of far-out insanity.

There are, after all, some chemicals that explode shatteringly, some that flame ravenously, some that corrode hellishly, some that poison sneakily, and some that stink stenchily. As far as I know, though, only liquid rocket fuels have all these delightful properties combined into one delectable whole.”

At the time that the book was written and published, most of the work on liquid rocket fuels had been done in the 40’s, 50’s, and first half of the 60’s. There was plenty written about rocketry, but very little about the propellants themselves, and nothing at all written about why these specific substances and not something else were being used. John Clark — having run a laboratory doing propellant research for seventeen years — had a unique perspective of the whole business and took the time to write IGNITION! An Informal History of Liquid Rocket Propellants.

Liquid rocket propellant was in two parts: a fuel and an oxidizer. The combination is hypergolic; that is, the two spontaneously ignite and burn upon contact with each other. As an example of the kinds of details that mattered (i.e. all of them), the combustion process had to be rapid and complete. If the two liquids flow into the combustion chamber and ignite immediately, that’s good. If they form a small puddle and then ignite, that’s bad. There are myriad other considerations as well; the fuel must burn at a manageable temperature (so as not to destroy the motor), the energy density of the fuel must be high enough to be a practical fuel in the first place, and so on.

The actual process of discovering exactly what materials to use and how precisely to make them work in a rocket motor was the very essence of the phrase “the devil is in the details.” For every potential solution, there was a mountain of dead-end possibilities that tantalizingly, infuriatingly, almost worked.

The first reliable, workable propellant combination was Aniline and Red Fuming Nitric Acid (RFNA). “It had the one – but magnificent – virtue that it worked,” writes Clark. “Otherwise it was an abomination.” Aniline was difficult to procure, ferociously poisonous and rapidly absorbed through skin, and froze at an inconvenient -6.2 Celsius which limited it to warm weather only. RFNA was fantastically corrosive, and this alone went on to cause no end of problems. It couldn’t be left sitting in a rocket tank waiting to be used for too long, because after a while you wouldn’t have a tank left. It needed to be periodically vented while in storage. Pouring it gave off dense clouds of remarkably toxic gas. This propellant would go on to cause incredibly costly and dangerous problems, but it worked. Still, no one wanted to put up with any of it one moment longer than they absolutely had to. As a result, that combination was not much more than a first step in the whole process; there was plenty of work left to do.

By the mid-sixties, liquid rocket propellant was a solved problem and the propellant community had pretty much worked themselves out of a job. Happily, a result of that work was this book; it captures history and detail that otherwise would simply have disappeared.

Clark has a gift for writing, and the book is easy to read and full of amusing (and eye-widening) anecdotes. Clark doesn’t skimp on the scientific background, but always in an accessible way. It’s interesting, it’s relevant, it’s relatable, and there is plenty to learn about how hard scientific and engineering development actually gets done. Download the PDF onto your favorite device. You’ll find it well worth the handful of evenings it takes to read through it.

Enigma neural network

Decoding Enigma Using A Neural Network

[Sam Greydanus] created a neural network that can encode and decode messages just as Enigma did. For those who don’t know, the Enigma machine was most famously used by the Germans during World War II to encrypt and decrypt messages. Give the neural network some encrypted text, called the ciphertext, along with the three-letter key that was used to encrypt the text, and the network predicts what the original text, or plaintext, was with around 96-97% accuracy.

The type of neural network he used was a Long Short Term Memory (LSTM ) network, a type of Recurrent Neural Network (RNN) that we talked about in our article covering many of the different types of neural networks developed over the years. RNNs are Turing-complete, meaning they can approximate any function. [Sam] noticed the irony in this, namely that Alan Turing both came up with the concept of Turing-completeness as well as played a big part in breaking the Enigma used in World War II.

How did [Sam] do it?

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Rushing The Design And Construction Of LED Centerpieces

‘Dragon Flame’ RGB LED table centerpieces, by [Alex Lao]
Sometimes the most important thing is getting something done.

[Alex Lao] was recently in such a situation. His sister was getting married and he designed, built, and delivered twenty RGB LED table centerpieces in a rush. There were no prototypes made, and when the parts arrived all twenty were built all at once over a single weekend. These table centerpieces are illuminated by RGB LEDs and battery-powered, but have an option to be powered by a wall adapter.

[Alex] helpfully shared some tips on reducing the production risks and helping ensure results in such a limited time frame. His advice boils down to this: reduce the unknowns. For Alex this meant re-using code and components from a previous project — even if they were not optimal — so that known-good schematic and footprint libraries could be used for the design.

From one perspective, the PIC32 microcontroller inside each lamp is overkill for an LED centerpiece. From another perspective, it was in fact the perfect part to use because it was the fastest way for [Alex] to get the devices working with no surprises.

For an added perspective on needing to get production right the first time on a much larger scale, be sure to check out getting an installation made up of 25,000 PCBs right the first time.

We Should Stop Here, It’s Bat Country!

[Roland Meertens] has a bat detector, or rather, he has a device that can record ultrasound – the type of sound that bats use to echolocate. What he wants is a bat detector. When he discovered bats living behind his house, he set to work creating a program that would use his recorder to detect when bats were around.

[Roland]’s workflow consists of breaking up a recording from his backyard into one second clips, loading them in to a Python program and running some machine learning code to determine whether the clip is a recording of a bat or not and using this to determine the number of bats flying around. He uses several Python libraries to do this including Tensorflow and LibROSA.

The Python code breaks each one second clip into twenty-two parts. For each part, he determines the max, min, mean, standard deviation, and max-min of the sample – if multiple parts of the signal have certain features (such as a high standard deviation), then the software has detected a bat call. Armed with this, [Roland] turned his head to the machine learning so that he could offload the work of detecting the bats. Again, he turned to Python and the Keras library.

With a 95% success rate, [Roland] now has a bat detector! One that works pretty well, too. For more on detecting bats and machine learning, check out the bat detector in this list of ultrasonic projects and check out this IDE for working with Tensorflow and machine learning.

You Know You Can Do That With A 555

Hardly a week goes by that we don’t post a project where at least one commenter will lament that the hacker could have just used a 555. [Peter Monta] clearly gets that point of view. For a 555 design contest, he created both digital logic gates and an op amp, all using 555 chips. We can’t quite imagine the post apocalyptic world where the only surviving electronic components are 555 chips, but if that day were to come, [Peter] is your guy.

Using the internal structure of the 555, [Peter] formed a basic logic gate, an inverter, latches, and more. He also composed things like counters and seven-segment decoders. He had a very simple 4-bit CPU design in Verilog that he was going to attempt until he realized it would map into almost 400 chips (half of that if you’d use a dual 555, but still). If you built this successfully, we would probably post it, by the way.  You can see a video of the digital logic counter, below.

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Handheld Gimbal With Off-The-Shelf Parts

For anything involving video capture while moving, most videographers, cinematographers, and camera operators turn to a gimbal. In theory it is a simple machine, needing only three sets of bearings to allow the camera to maintain a constant position despite a shifting, moving platform. In practice it’s much more complicated, and gimbals can easily run into the thousands of dollars. While it’s possible to build one to reduce the extravagant cost, few use 100% off-the-shelf parts like [Matt]’s handheld gimbal.

[Matt]’s build was far more involved than bolting some brackets and bearings together, though. Most gimbals for filming are powered, so motors and electronics are required. Not only that, but the entire rig needs to be as balanced as possible to reduce stress on those motors. [Matt] used fishing weights to get everything calibrated, as well as an interesting PID setup.

Be sure to check out the video below to see the gimbal in action. After a lot of trial-and-error, it’s hard to tell the difference between this and a consumer-grade gimbal, and all without the use of a CNC machine or a 3D printer. Of course, if you have access to those kinds of tools, there’s no limit to the types of gimbals you can build.

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