It's difficult to speculate as to the exact failure from blurry pictures but the solder on that choke (inductor) looks terrible.
Something went wrong in manufacturing. The solder should have wicked to cover the entire pad, not just a small square, and there should be no (brown) discoloration.
Our company has drastically downsized its dependence on Atlassian in the past month, we will be completely free in a few more. With the help of modern AI tools we've been able to replace their products with internal tools that are better tailored to our needs.
The flickering is solely a result of cost cutting in the power supplies of these LED lights. The problem is totally solvable with a constant current switching power supply. But the filtering circuitry adds cost.
There could be a few benefits to what is proposed in the article, here are my semi-educated guesses.
1. Less dependent on local geology. Geothermal wells are well suited for hot, non-pourous (?) geology.
2. Might be cheaper. It can take years to drill the wells for a closed loop system (e.g. Eavor), less for a fracked geothermal well (e.g. Fervo). I imagine drilling a single borehole for this is way simpler.
3. Less water loss. Fracked geothermal well wells can be pretty lossy (20%?). If water supply is an issue your options may be limited.
200W at 300g is realistic for a modern BLDC motor. The article states that Maxon was the motor supplier so they likely used the EC-4pole series, I'm guessing it's one of these.
It's 30mm diameter to fit in a down-tube, 200W, and 300g. But just because it's 200W doesn't mean you have to run it at maximum capacity, there's no reason you couldn't run it at 5-50W.
Has anyone tried a controller for cooling that is dependent on processor current consumption? Temperature measurements lag, but the current used by the processor is instantaneous and directly converted to heat (P = VI). In theory, it should be possible to reduce temperature spikes.
A Kalman filter will give you the "best guess" for some state (x) at the current timestep (k). This estimate often has some lag in it, likely because you have some incomplete information that you couldn't model. Sometimes we care about the previous states (e.g., x_k-1). But if we just save these states and refer to them, we're not getting the most out of our data.
The Kalman Smoother can be used to go back and update these past values with all the samples up to the current time. To update your previous measurements, you need to save the state of your filter at every timestep (x_k) and its associated covariance matrix (P_k). You can then apply Kalman Smoothing to reprocess previous data and update it with all current information. This will often remove the phase delay that you would otherwise observe in your estimate.
We develop navigation instrumentation for directional drilling (oil/gas, geothermal, etc.). In these applications you have to estimate the path of the drilling string based on directional measurements and the length of drill pipe you've put into the ground. To take good measurements you have to stop so that rotation and vibration don't corrupt your measurement.
We use a type of Kalman Filter to estimate direction and instantaneous dynamics of the drill string, this way the drilling operation no longer needs to stop to get a directional measurement.
No, it doesn't appear so. It seems the bristles do wear out somehow, to me it appears that they simply become less stiff over time. The change to a new brush head is noticeable.
This should be relatively easy to verify. One could take a new brush head and forward its counter to the limit, directly comparing it to a new unmodified brush.
Something went wrong in manufacturing. The solder should have wicked to cover the entire pad, not just a small square, and there should be no (brown) discoloration.