So I’m currently working on this research problem: Microjets in cross flow for disturbance-based flow control. Jets in crossflow have some promise to be a viable flow control technique in aerodynamic applications, but it’s still in its early-mid research stages, where the technology has good theoretical support (i.e. it should work) and some experimental successes (it does work given several lab constraints, in very simple problems). Part of my thesis work will be to further the experimental support side of things.
But when working with complex curved shapes (like any realistic aerodynamic surface) it is not clear where we should place a jet in the surface. Where is separation going to happen? Where to place the jets to prevent it from happening / make it happen earlier? Maybe we want to excite boundary layer waves, like the Tollmien-Schlichting waves? From the computational/theoretical standpoint, there’s some heavy-duty stability analysis that could potentially give possible “sensitive” locations for the jets. I’m not fully a computational person myself, but my current opinion given what I’ve seen so far is that we have too many assumptions we need to trust are approximate enough (i.e., Navier-Stokes are linear equations, jets produce content in the unstable eigenmodes of the flow, we did resolve the relevant flow structures with the simulation results, cows are spherical, etc.). Again, this is not my specialty, so that’s probably why I find it hard to believe in the effectiveness of that approach.
But from the experimental (wind tunnel) standpoint, we need to drill a physical hole in the aerodynamic surface and route a pipe from inside to blow the jet. That requires some work, but more importantly it takes precious testing time when you’re testing your jet configurations. If all you were able to come up with were ineffective or mildly effective actuator patterns, that’s what you’re stuck with. And you’ll never know how close you got because you can only afford a few data points in the experiment. Furthermore, the background fluid dynamics knowledge required to come up with effective patterns requires decades of study and experience – which I don’t have. So I suggested: Why don’t we manufacture a reconfigurable actuator array and let a computer run thousands of pattern configurations? We could potentially abstract the jet placement problem from the fluid mechanics realm into a (rather complex, I admit) optimization problem. More jet configurations will be explored, increasing the confidence on the solutions found. And with the beauty of advanced flow diagnostics, we can even learn new physics from these solutions.
But then you might ask: Why don’t you just do CFD on these jets? Well, turns out that in order to perform any simulation work with jets in cross flow we need an obscene amount of resolution, which increases the computational time to an extent it is just easier to do the experiment. When it involves multiple jet configurations, you really need to be able to discard multiple runs, which requires them to be cheap. It’s a similar thing with AI. AI is only possible now because now each iteration is cheap to run, even though the math and the theoretical foundations come from several decades ago.
So this is the road I’m going through now, basically making microjet actuator studies cheap to run so we can discard most of them and try random stuff until we hit the jackpot. But, even though the prospect of having to build a reconfigurable array of jets with hundreds of jets may sound like a rather daunting task, there’s some fun along the path. And this is the point of this post!
I’m building a manifold with 100 solenoids that can be individually controlled by a reconfigurable signal generator I designed and built (pictures below!). The signal generator board is based on a PIC32MZ (Design here) and has effectively 108 channels. I was able to update all channels simultaneously at 24900 Samples/s (well, there’s a 200ns delay between physical uC ports, but that’s virtually instantaneous from the mechanical standpoint). I designed it such that the board appears as a USB serial COM port in your computer, which then can receive messages through either a serial terminal or a serial interface on Matlab or C++, for example. This gives me a lot of control over the jets.
While putting all of this together and seeing the results of the system I built, I figured: Hey, I can turn this into a musical instrument! Of course, a rather crude one, because my bandwidth is very low (like <200Hz). But I decided anyways to code up a MIDI driver for this jet array and then change the notes to fit the bandwidth by shifting a few octaves on the song. The result is rather crude but it was so much fun to play with! MIDI files, for the uninitiated, are like a digital version of a sheet music. It contains a table of notes and the timing when they should be played and how long for. My job was to simply convert the digital instructions into the protocol I came up with for my serial communication and stream the instructions to the USB serial port.
So here’s a few songs I was able to play to a level where I believe people can actually reconize them: See if you can! (Answers in the description of the video). If you want to have more info on how I did it, perhaps you might consider following my research on ResearchGate and maybe a few years from now an academic paper on this topic will come from that! =)
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